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A lowcost, compliant, underactuated prosthetic hand with custom flex sensors for finger bending estimation Access to quality prosthetics to aid in daily activities is a privilege of only 5 % of those in need of such equipment. It is noted that the high cost of these, associated with the lack of skilled labor, are two of the factors that aggravate the situation. Thus, the need for costeffective prosthetic technologies, targeting the population of developing countries, is observed. Inspired by this problem, this paper presents the process of conceptual study, design, and prototyping of a lowcost prosthetic hand that is compliant and underactuated. The hand has a wrist of two degrees of freedom, and five independently actuated fingers. One of the main contributions of this work is the design of a lowcost optoelectronic sensor for the fingeru0027s curvature estimation.	Design of a Highly Compliant Underactuated Prosthetic Hand This paper presents a highly compliant prosthetic hand based on a novel synergy mechanism. The prosthetic hand consists of a synergy mechanism, four fingers and one thumb. The synergy mechanism can transmit the power from two motors to four fingers. It can effectively reduce the number of motors. At the same time, it makes the prosthetic hand have excellent dexterity. The underactuated finger has three joints with two DOF. It has two main motion modes: coupled motion in free space and selfadaptive motion when contacting with the object, which can mimic the human finger as much as possible. The thumb uses one motor to drive the two flexionextension joints and uses a manual switch to drive the abductionadduction joint, which can reduce the number of motors and the cost efficiently. The performance evaluation is given to demonstrate the comprehensive performance in terms of the versatility, compliance, sensing, size, weight and cost of the proposed design.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
A lowcost, compliant, underactuated prosthetic hand with custom flex sensors for finger bending estimation Access to quality prosthetics to aid in daily activities is a privilege of only 5 % of those in need of such equipment. It is noted that the high cost of these, associated with the lack of skilled labor, are two of the factors that aggravate the situation. Thus, the need for costeffective prosthetic technologies, targeting the population of developing countries, is observed. Inspired by this problem, this paper presents the process of conceptual study, design, and prototyping of a lowcost prosthetic hand that is compliant and underactuated. The hand has a wrist of two degrees of freedom, and five independently actuated fingers. One of the main contributions of this work is the design of a lowcost optoelectronic sensor for the fingeru0027s curvature estimation.	Design of a Highly Compliant Underactuated Prosthetic Hand This paper presents a highly compliant prosthetic hand based on a novel synergy mechanism. The prosthetic hand consists of a synergy mechanism, four fingers and one thumb. The synergy mechanism can transmit the power from two motors to four fingers. It can effectively reduce the number of motors. At the same time, it makes the prosthetic hand have excellent dexterity. The underactuated finger has three joints with two DOF. It has two main motion modes: coupled motion in free space and selfadaptive motion when contacting with the object, which can mimic the human finger as much as possible. The thumb uses one motor to drive the two flexionextension joints and uses a manual switch to drive the abductionadduction joint, which can reduce the number of motors and the cost efficiently. The performance evaluation is given to demonstrate the comprehensive performance in terms of the versatility, compliance, sensing, size, weight and cost of the proposed design.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
A lowcost, compliant, underactuated prosthetic hand with custom flex sensors for finger bending estimation Access to quality prosthetics to aid in daily activities is a privilege of only 5 % of those in need of such equipment. It is noted that the high cost of these, associated with the lack of skilled labor, are two of the factors that aggravate the situation. Thus, the need for costeffective prosthetic technologies, targeting the population of developing countries, is observed. Inspired by this problem, this paper presents the process of conceptual study, design, and prototyping of a lowcost prosthetic hand that is compliant and underactuated. The hand has a wrist of two degrees of freedom, and five independently actuated fingers. One of the main contributions of this work is the design of a lowcost optoelectronic sensor for the fingeru0027s curvature estimation.	Design of a Highly Compliant Underactuated Prosthetic Hand This paper presents a highly compliant prosthetic hand based on a novel synergy mechanism. The prosthetic hand consists of a synergy mechanism, four fingers and one thumb. The synergy mechanism can transmit the power from two motors to four fingers. It can effectively reduce the number of motors. At the same time, it makes the prosthetic hand have excellent dexterity. The underactuated finger has three joints with two DOF. It has two main motion modes: coupled motion in free space and selfadaptive motion when contacting with the object, which can mimic the human finger as much as possible. The thumb uses one motor to drive the two flexionextension joints and uses a manual switch to drive the abductionadduction joint, which can reduce the number of motors and the cost efficiently. The performance evaluation is given to demonstrate the comprehensive performance in terms of the versatility, compliance, sensing, size, weight and cost of the proposed design.	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their
A lowcost, compliant, underactuated prosthetic hand with custom flex sensors for finger bending estimation Access to quality prosthetics to aid in daily activities is a privilege of only 5 % of those in need of such equipment. It is noted that the high cost of these, associated with the lack of skilled labor, are two of the factors that aggravate the situation. Thus, the need for costeffective prosthetic technologies, targeting the population of developing countries, is observed. Inspired by this problem, this paper presents the process of conceptual study, design, and prototyping of a lowcost prosthetic hand that is compliant and underactuated. The hand has a wrist of two degrees of freedom, and five independently actuated fingers. One of the main contributions of this work is the design of a lowcost optoelectronic sensor for the fingeru0027s curvature estimation.	Design of a Highly Compliant Underactuated Prosthetic Hand This paper presents a highly compliant prosthetic hand based on a novel synergy mechanism. The prosthetic hand consists of a synergy mechanism, four fingers and one thumb. The synergy mechanism can transmit the power from two motors to four fingers. It can effectively reduce the number of motors. At the same time, it makes the prosthetic hand have excellent dexterity. The underactuated finger has three joints with two DOF. It has two main motion modes: coupled motion in free space and selfadaptive motion when contacting with the object, which can mimic the human finger as much as possible. The thumb uses one motor to drive the two flexionextension joints and uses a manual switch to drive the abductionadduction joint, which can reduce the number of motors and the cost efficiently. The performance evaluation is given to demonstrate the comprehensive performance in terms of the versatility, compliance, sensing, size, weight and cost of the proposed design.	FIRE 2019 AILA Track: Artificial Intelligence for Legal Assistance The FIRE 2019 AILA track focused on creating a framework for evaluating different methods of retrieving relevant priorprecedent cases and statutes given a factual scenario. There were two tasks for this track: (i) Identifying relevant prior cases for a given situation (Precedent Retrieval), and (ii) Identifying most relevant statutes for a given situation (Statute Retrieval). Given a situation that can lead to filing a case, the precedent retrieval task aims at finding case documents where similar legal situations were addressed. The statute retrieval task aims at finding relevant statutes that are applicable to the situation. The factual scenarios, statutes and prior case documents used in the tasks were from the Indian Supreme Court judiciary.
A lowcost, compliant, underactuated prosthetic hand with custom flex sensors for finger bending estimation Access to quality prosthetics to aid in daily activities is a privilege of only 5 % of those in need of such equipment. It is noted that the high cost of these, associated with the lack of skilled labor, are two of the factors that aggravate the situation. Thus, the need for costeffective prosthetic technologies, targeting the population of developing countries, is observed. Inspired by this problem, this paper presents the process of conceptual study, design, and prototyping of a lowcost prosthetic hand that is compliant and underactuated. The hand has a wrist of two degrees of freedom, and five independently actuated fingers. One of the main contributions of this work is the design of a lowcost optoelectronic sensor for the fingeru0027s curvature estimation.	Design of a Highly Compliant Underactuated Prosthetic Hand This paper presents a highly compliant prosthetic hand based on a novel synergy mechanism. The prosthetic hand consists of a synergy mechanism, four fingers and one thumb. The synergy mechanism can transmit the power from two motors to four fingers. It can effectively reduce the number of motors. At the same time, it makes the prosthetic hand have excellent dexterity. The underactuated finger has three joints with two DOF. It has two main motion modes: coupled motion in free space and selfadaptive motion when contacting with the object, which can mimic the human finger as much as possible. The thumb uses one motor to drive the two flexionextension joints and uses a manual switch to drive the abductionadduction joint, which can reduce the number of motors and the cost efficiently. The performance evaluation is given to demonstrate the comprehensive performance in terms of the versatility, compliance, sensing, size, weight and cost of the proposed design.	Its time to rethink DDoS protection When you think of distributed denial of service (DDoS) attacks, chances are you conjure up an image of an overwhelming flood of traffic that incapacitates a network. This kind of cyber attack is all about overt, brute force used to take a target down. Some hackers are a little smarter, using DDoS as a distraction while they simultaneously attempt a more targeted strike, as was the case with a Carphone Warehouse hack in 2015. 1 But in general, DDoS isnu0027t subtle. Retailers are having to rethink how they approach distributed denial of service (DDoS) protection following the rise of a stealthier incarnation of the threat. There has been a significant increase in smallscale DDoS attacks and a corresponding reduction in conventional largescale events. The hackerxe2x80x99s aim is to remain below the conventional xe2x80x98detect and alertxe2x80x99 threshold that could trigger a DDoS mitigation strategy. Roy Reynolds of Vodat International explains the nature of the threat and the steps organisations can take to protect themselves.
Implementing Inquiry Based Collaborative Learning in Solid State Physics Course Twentytwo students have opted Solid State Physics as an elective in their second semester of their B. Tech course at GITAM Hyderabad. These students were introduced to learning by collaborative methods for new concepts and solve tutorial problem. The ABAB method 1 of instructions was used to evaluate the effect of collaborative learning (CL), with A type being traditional methods and B type active learning methods. The gain has been calculated on the post test for every student after implementing this methodology was obtained to be insignificant. This research articles also tries to analyze, what could be possible causes for such insignificant gain, when CL methods is well studied.	Improving Student Readiness for InquiryBased Learning: An Engineering Case Study During the past decade, the authors have sought to advance student research in a predominantly teaching institution. The two primary challenges were: academic how to introduce and promote inquirybased learning (IBL) given the constraints, and business how to obtain and sustain funding in the area of industrysponsored research. The authors developed a successful multidisciplinary modeling course that integrates four teaching and learning strategies and where key learning outcomes strengthen student readiness to engage in research. The course culminates with research performed as part of an IBL strategy that is relevant and supported by mentoring. The benefits include development of intellectual and practical skills that underlie a central activity of engineering design. The course structure, evidence of student work, and evolution over time to meet challenges are presented and discussed. Most importantly, the potential of this strategy to be implemented across other topical areas is addressed. Student participation in research improves learning of engineering and scientific concepts, increases interaction with faculty and industry sponsors, and provides opportunities for work in emerging technology areas. Benefits accrue both to students who pursue a research career and to those who enter applied fields by strengthening their ability to propose innovative solutions.	Supporting BlockchainBased Cryptocurrency Mobile Payment With Smart Devices The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchainbased cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the two main obstacles for mobile payment using cryptocurrency. We propose two different schemes for cryptocurrency mobile payment, one involves a centralized bank and the other one does not require any centralized party. We also provide a solution for the bank to meet KYC (know your customer)AML (antimoney laundering) compliance requirements when it is involved in cryptocurrency mobile payment processing.
Implementing Inquiry Based Collaborative Learning in Solid State Physics Course Twentytwo students have opted Solid State Physics as an elective in their second semester of their B. Tech course at GITAM Hyderabad. These students were introduced to learning by collaborative methods for new concepts and solve tutorial problem. The ABAB method 1 of instructions was used to evaluate the effect of collaborative learning (CL), with A type being traditional methods and B type active learning methods. The gain has been calculated on the post test for every student after implementing this methodology was obtained to be insignificant. This research articles also tries to analyze, what could be possible causes for such insignificant gain, when CL methods is well studied.	Improving Student Readiness for InquiryBased Learning: An Engineering Case Study During the past decade, the authors have sought to advance student research in a predominantly teaching institution. The two primary challenges were: academic how to introduce and promote inquirybased learning (IBL) given the constraints, and business how to obtain and sustain funding in the area of industrysponsored research. The authors developed a successful multidisciplinary modeling course that integrates four teaching and learning strategies and where key learning outcomes strengthen student readiness to engage in research. The course culminates with research performed as part of an IBL strategy that is relevant and supported by mentoring. The benefits include development of intellectual and practical skills that underlie a central activity of engineering design. The course structure, evidence of student work, and evolution over time to meet challenges are presented and discussed. Most importantly, the potential of this strategy to be implemented across other topical areas is addressed. Student participation in research improves learning of engineering and scientific concepts, increases interaction with faculty and industry sponsors, and provides opportunities for work in emerging technology areas. Benefits accrue both to students who pursue a research career and to those who enter applied fields by strengthening their ability to propose innovative solutions.	Structure and expression of the gene coding for the alphasubunit of DNAdependent RNA polymerase from the chloroplast genome of Zea mays. :0,rpoA gene coding for the alphasubunit of DNAdependent RNA polymerase located on the DNA of Zea mays chloroplasts has been characterized with respect to its position on the chloroplast genome and its nucleotide sequence. The amino acid sequence derived for a 39 Kd polypeptide shows strong homology with sequences derived from the :0,rpoA genes of other chloroplast species and with the amino acid sequence of the alphasubunit from E. coli RNA polymerase. Transcripts of the :0,rpoA gene were identified by Northern hybridization and characterized by S1 mapping using total RNA isolated from maize chloroplasts. Antibodies raised against a synthetic Cterminal heptapeptide show cross reactivity with a 39 Kd polypeptide contained in the stroma fraction of maize chloroplasts. It is concluded that the :0,rpoA gene is a functional gene and that therefore, at least the alphasubunit of plastidic RNA polymerase, is expressed in chloroplasts.
Implementing Inquiry Based Collaborative Learning in Solid State Physics Course Twentytwo students have opted Solid State Physics as an elective in their second semester of their B. Tech course at GITAM Hyderabad. These students were introduced to learning by collaborative methods for new concepts and solve tutorial problem. The ABAB method 1 of instructions was used to evaluate the effect of collaborative learning (CL), with A type being traditional methods and B type active learning methods. The gain has been calculated on the post test for every student after implementing this methodology was obtained to be insignificant. This research articles also tries to analyze, what could be possible causes for such insignificant gain, when CL methods is well studied.	Improving Student Readiness for InquiryBased Learning: An Engineering Case Study During the past decade, the authors have sought to advance student research in a predominantly teaching institution. The two primary challenges were: academic how to introduce and promote inquirybased learning (IBL) given the constraints, and business how to obtain and sustain funding in the area of industrysponsored research. The authors developed a successful multidisciplinary modeling course that integrates four teaching and learning strategies and where key learning outcomes strengthen student readiness to engage in research. The course culminates with research performed as part of an IBL strategy that is relevant and supported by mentoring. The benefits include development of intellectual and practical skills that underlie a central activity of engineering design. The course structure, evidence of student work, and evolution over time to meet challenges are presented and discussed. Most importantly, the potential of this strategy to be implemented across other topical areas is addressed. Student participation in research improves learning of engineering and scientific concepts, increases interaction with faculty and industry sponsors, and provides opportunities for work in emerging technology areas. Benefits accrue both to students who pursue a research career and to those who enter applied fields by strengthening their ability to propose innovative solutions.	Unmanned agricultural product sales system The invention relates to the field of agricultural product sales, provides an unmanned agricultural product sales system, and aims to solve the problem of agricultural product waste caused by the factthat most farmers can only prepare goods according to guessing and experiences when selling agricultural products at present. The unmanned agricultural product sales system comprises an acquisition module for acquiring selection information of customers; a storage module which prestores a vegetable preparation scheme; a matching module which is used for matching a corresponding side dish schemefrom the storage module according to the selection information of the client; a pushing module which is used for pushing the matched side dish scheme back to the client; an acquisition module which isalso used for acquiring confirmation information of a client; an order module which is used for generating order information according to the confirmation information of the client, wherein the pushing module is used for pushing the order information to the client and the seller, and the acquisition module is also used for acquiring the delivery information of the seller; and a logistics trackingmodule which is used for tracking the delivery information to obtain logistics information, wherein the pushing module is used for pushing the logistics information to the client. The scheme is usedfor sales of unmanned agricultural product shops.
Implementing Inquiry Based Collaborative Learning in Solid State Physics Course Twentytwo students have opted Solid State Physics as an elective in their second semester of their B. Tech course at GITAM Hyderabad. These students were introduced to learning by collaborative methods for new concepts and solve tutorial problem. The ABAB method 1 of instructions was used to evaluate the effect of collaborative learning (CL), with A type being traditional methods and B type active learning methods. The gain has been calculated on the post test for every student after implementing this methodology was obtained to be insignificant. This research articles also tries to analyze, what could be possible causes for such insignificant gain, when CL methods is well studied.	Improving Student Readiness for InquiryBased Learning: An Engineering Case Study During the past decade, the authors have sought to advance student research in a predominantly teaching institution. The two primary challenges were: academic how to introduce and promote inquirybased learning (IBL) given the constraints, and business how to obtain and sustain funding in the area of industrysponsored research. The authors developed a successful multidisciplinary modeling course that integrates four teaching and learning strategies and where key learning outcomes strengthen student readiness to engage in research. The course culminates with research performed as part of an IBL strategy that is relevant and supported by mentoring. The benefits include development of intellectual and practical skills that underlie a central activity of engineering design. The course structure, evidence of student work, and evolution over time to meet challenges are presented and discussed. Most importantly, the potential of this strategy to be implemented across other topical areas is addressed. Student participation in research improves learning of engineering and scientific concepts, increases interaction with faculty and industry sponsors, and provides opportunities for work in emerging technology areas. Benefits accrue both to students who pursue a research career and to those who enter applied fields by strengthening their ability to propose innovative solutions.	Its time to rethink DDoS protection When you think of distributed denial of service (DDoS) attacks, chances are you conjure up an image of an overwhelming flood of traffic that incapacitates a network. This kind of cyber attack is all about overt, brute force used to take a target down. Some hackers are a little smarter, using DDoS as a distraction while they simultaneously attempt a more targeted strike, as was the case with a Carphone Warehouse hack in 2015. 1 But in general, DDoS isnu0027t subtle. Retailers are having to rethink how they approach distributed denial of service (DDoS) protection following the rise of a stealthier incarnation of the threat. There has been a significant increase in smallscale DDoS attacks and a corresponding reduction in conventional largescale events. The hackerxe2x80x99s aim is to remain below the conventional xe2x80x98detect and alertxe2x80x99 threshold that could trigger a DDoS mitigation strategy. Roy Reynolds of Vodat International explains the nature of the threat and the steps organisations can take to protect themselves.
Implementing Inquiry Based Collaborative Learning in Solid State Physics Course Twentytwo students have opted Solid State Physics as an elective in their second semester of their B. Tech course at GITAM Hyderabad. These students were introduced to learning by collaborative methods for new concepts and solve tutorial problem. The ABAB method 1 of instructions was used to evaluate the effect of collaborative learning (CL), with A type being traditional methods and B type active learning methods. The gain has been calculated on the post test for every student after implementing this methodology was obtained to be insignificant. This research articles also tries to analyze, what could be possible causes for such insignificant gain, when CL methods is well studied.	Improving Student Readiness for InquiryBased Learning: An Engineering Case Study During the past decade, the authors have sought to advance student research in a predominantly teaching institution. The two primary challenges were: academic how to introduce and promote inquirybased learning (IBL) given the constraints, and business how to obtain and sustain funding in the area of industrysponsored research. The authors developed a successful multidisciplinary modeling course that integrates four teaching and learning strategies and where key learning outcomes strengthen student readiness to engage in research. The course culminates with research performed as part of an IBL strategy that is relevant and supported by mentoring. The benefits include development of intellectual and practical skills that underlie a central activity of engineering design. The course structure, evidence of student work, and evolution over time to meet challenges are presented and discussed. Most importantly, the potential of this strategy to be implemented across other topical areas is addressed. Student participation in research improves learning of engineering and scientific concepts, increases interaction with faculty and industry sponsors, and provides opportunities for work in emerging technology areas. Benefits accrue both to students who pursue a research career and to those who enter applied fields by strengthening their ability to propose innovative solutions.	MToS: MultiTenant Network Over Software Defined Networking MToS, a multitenant network service is designed and implemented under SoftwareDefined Network (SDN) environment. One of the solutions to establish multitenant network in nonSDN environment is MultiProtocol Label Switching Virtual Private Network that involves numerous and complicated protocols to be configured prior to the establishment of multitenant network. With SDN, it opens new opportunities to create multitenant networks that are less complicated, more automated, and lower implementation cost via SDN commodity devices. MToS categorizes OpenFlow switches into three hierarchies, where matching fields and actions in flow entries are different depending on the switch hierarchy. Therefore, traffic forwarding can be scalable. MToS provides tenant isolation through dedicate flow table associated with each tenant, and tenant MAC address as the identifier which is used in flow table redirection and packet header modification. Traffic forwarding between Edge switches is achieved through Edge MAC address as the identifier which is used in packet header modification, and Edge switch serves as ARP Proxy for tenant end hosts. Comparing to MPLS VPN, MToS only requires essential information about tenants to construct a multitenant network. By taking advantage of SDN centralized global network information, MToS adds automations of IP address management and shortest path routes calculation. MToS is developed based upon OpenFlow version 1.3, and implemented in Python and runs on top of Ryu SDN framework.
Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to runtofailure data sets of CMAPSS testbed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.	Robust Functional Regression for Outlier Detection In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to runtofailure data sets of CMAPSS testbed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.	Robust Functional Regression for Outlier Detection In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to runtofailure data sets of CMAPSS testbed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.	Robust Functional Regression for Outlier Detection In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.	What Makes a Social Robot Good at Interacting with Humans This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: xe2x80x9cDo social robots need to look like living creatures that already exist in the world for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have animated faces for humans to interact well with them?xe2x80x9d; xe2x80x9cDo social robots need to have the ability to speak a coherent human language for humans to interact well with them?xe2x80x9d and xe2x80x9cDo social robots need to have the capability to make physical gestures for humans to interact well with them?xe2x80x9d. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethicalmoral concerns have also been discussed.
Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to runtofailure data sets of CMAPSS testbed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.	Robust Functional Regression for Outlier Detection In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.	Extremal Problems for tPartite and tColorable Hypergraphs Fix integers t ge r ge 2 and an runiform hypergraph F. We prove that the maximum number of edges in a tpartite runiform hypergraph on n vertices that contains no copy of F is c_t, Fn choose r o(nr), where c_t, F can be determined by a finite computation. We explicitly define a sequence F_1, F_2, ldots of runiform hypergraphs, and prove that the maximum number of edges in a tchromatic runiform hypergraph on n vertices containing no copy of F_i is alpha_t,r,in choose r o(nr), where alpha_t,r,i can be determined by a finite computation for each ige 1. In several cases, alpha_t,r,i is irrational. The main tool used in the proofs is the Lagrangian of a hypergraph.
Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to runtofailure data sets of CMAPSS testbed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.	Robust Functional Regression for Outlier Detection In this paper we propose an outlier detection algorithm for temperature sensor data from jet engine tests. Effective identification of outliers would enable engine problems to be examined and resolved efficiently. Outlier detection in this data is challenging because a human controller determines the speed of the engine during each manoeuvre. This introduces variability which can mask abnormal behaviour in the engine response. We therefore suggest modelling the dependency between speed and temperature in the process of identifying abnormalities. The engine temperature has a delayed response with respect to the engine speed, which we will model using robust functional regression. We then apply functional depth with respect to the residuals to rank the samples and identify the outliers. The effectiveness of the outlier detection algorithm is shown in a simulation study. The algorithm is also applied to real engine data, and identifies samples that warrant further investigation.	Humanoid coworkers: How is it like to work with a robot? Humanrobot interaction in corporate workplaces is a research area which remains unexplored. In this paper, we present the results and analysis of a social experiment we conducted by introducing a humanoid robot (Nadine) into a collaborative social workplace. The humanoidu0027s primary task was to function as a receptionist and provide general assistance to the customers. Moreover, the employees who interacted with Nadine were given over a month to get used to her capabilities, after which, the feedback was collected from the staff on the grounds of influence on productivity, affect experienced during interaction and their views on social robots assisting with regular tasks. Our results show that the usage of social robots for assisting with normal daytoday tasks is taken quite positively by the coworkers and that in the near future, more capable humanoid social robots can be used in workplaces for assisting with menial tasks. Finally, we posit that surveys such as ours could result in constructive opinions based on technological awareness, rather than opinions from mediadriven fears about the threats of technology.
An FPGA based verification platform for pipeline ADC digital calibration technology In fine line technology, pipeline ADC performance improvement is difficult and power consumption becomes more considerable. In this context, digital calibration algorithm is applied to improve system performance and reduce power consumption. In order to verify pipeline ADC digital calibration technology before chip verification, to evaluate power consumption and hardware overhead, an FPGA based verification platform is established. In this platform, ADC digital model is established to simulate the actual ADC, digital calibration algorithm is described by digital calibration module, and the ideal ADC is used to provide input signal for the system. Applying this platform, simulation is carried out in a deterministic digital calibration technique. The simulation results show that, by applying of the platform, the correctness and reliability of the design of pipeline ADC digital calibration system can be effectively improved, and the system developing cycle can be shortened.	An Automatic SlopeCalibrated Ramp Generator for SingleSlope ADCs This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12bit singleslope ADC (SSADC), employing the proposed ramp generator, has been implemented in a 0.11xc2xb5m CMOS process. The simulation results show that the offset of the rampu0027s stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.	Erkundung und Erforschung. Alexander von Humboldts Amerikareise Zusammenfassung Ahnlich wie Adalbert Stifters Erzahler im Roman xe2x80x9eNachsommerxe2x80x9c verband A. v. Humboldt auf seiner Amerikareise Erkundung und Erforschung, Reiselust und Erkenntnisstreben. Humboldt hat sein doppeltes Ziel klar benannt: Bekanntmachung der besuchten Lander, Sammeln von Tatsachen zur Erweiterung der physikalischen Geographie. Der Aufsatz ist in funf Abschnitte gegliedert: Anliegen, Route, Methoden, Ergebnisse, Auswertung. Abstract In a similar way as Adalbert Stifteru0027s narrator in the novel xe2x80x9cLate summerxe2x80x9d A. v. Humboldt combined exploration with research, fondness for travelling with striving for findings during his travel through South America. Humboldt clearly indicated his double aim: to report on the visited countries, to collect facts in order to improve physical geography. The treatise consists of five sections: object, route, methods, results, evaluation.
An FPGA based verification platform for pipeline ADC digital calibration technology In fine line technology, pipeline ADC performance improvement is difficult and power consumption becomes more considerable. In this context, digital calibration algorithm is applied to improve system performance and reduce power consumption. In order to verify pipeline ADC digital calibration technology before chip verification, to evaluate power consumption and hardware overhead, an FPGA based verification platform is established. In this platform, ADC digital model is established to simulate the actual ADC, digital calibration algorithm is described by digital calibration module, and the ideal ADC is used to provide input signal for the system. Applying this platform, simulation is carried out in a deterministic digital calibration technique. The simulation results show that, by applying of the platform, the correctness and reliability of the design of pipeline ADC digital calibration system can be effectively improved, and the system developing cycle can be shortened.	An Automatic SlopeCalibrated Ramp Generator for SingleSlope ADCs This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12bit singleslope ADC (SSADC), employing the proposed ramp generator, has been implemented in a 0.11xc2xb5m CMOS process. The simulation results show that the offset of the rampu0027s stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
An FPGA based verification platform for pipeline ADC digital calibration technology In fine line technology, pipeline ADC performance improvement is difficult and power consumption becomes more considerable. In this context, digital calibration algorithm is applied to improve system performance and reduce power consumption. In order to verify pipeline ADC digital calibration technology before chip verification, to evaluate power consumption and hardware overhead, an FPGA based verification platform is established. In this platform, ADC digital model is established to simulate the actual ADC, digital calibration algorithm is described by digital calibration module, and the ideal ADC is used to provide input signal for the system. Applying this platform, simulation is carried out in a deterministic digital calibration technique. The simulation results show that, by applying of the platform, the correctness and reliability of the design of pipeline ADC digital calibration system can be effectively improved, and the system developing cycle can be shortened.	An Automatic SlopeCalibrated Ramp Generator for SingleSlope ADCs This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12bit singleslope ADC (SSADC), employing the proposed ramp generator, has been implemented in a 0.11xc2xb5m CMOS process. The simulation results show that the offset of the rampu0027s stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
An FPGA based verification platform for pipeline ADC digital calibration technology In fine line technology, pipeline ADC performance improvement is difficult and power consumption becomes more considerable. In this context, digital calibration algorithm is applied to improve system performance and reduce power consumption. In order to verify pipeline ADC digital calibration technology before chip verification, to evaluate power consumption and hardware overhead, an FPGA based verification platform is established. In this platform, ADC digital model is established to simulate the actual ADC, digital calibration algorithm is described by digital calibration module, and the ideal ADC is used to provide input signal for the system. Applying this platform, simulation is carried out in a deterministic digital calibration technique. The simulation results show that, by applying of the platform, the correctness and reliability of the design of pipeline ADC digital calibration system can be effectively improved, and the system developing cycle can be shortened.	An Automatic SlopeCalibrated Ramp Generator for SingleSlope ADCs This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12bit singleslope ADC (SSADC), employing the proposed ramp generator, has been implemented in a 0.11xc2xb5m CMOS process. The simulation results show that the offset of the rampu0027s stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
An FPGA based verification platform for pipeline ADC digital calibration technology In fine line technology, pipeline ADC performance improvement is difficult and power consumption becomes more considerable. In this context, digital calibration algorithm is applied to improve system performance and reduce power consumption. In order to verify pipeline ADC digital calibration technology before chip verification, to evaluate power consumption and hardware overhead, an FPGA based verification platform is established. In this platform, ADC digital model is established to simulate the actual ADC, digital calibration algorithm is described by digital calibration module, and the ideal ADC is used to provide input signal for the system. Applying this platform, simulation is carried out in a deterministic digital calibration technique. The simulation results show that, by applying of the platform, the correctness and reliability of the design of pipeline ADC digital calibration system can be effectively improved, and the system developing cycle can be shortened.	An Automatic SlopeCalibrated Ramp Generator for SingleSlope ADCs This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12bit singleslope ADC (SSADC), employing the proposed ramp generator, has been implemented in a 0.11xc2xb5m CMOS process. The simulation results show that the offset of the rampu0027s stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.	General Data Protection Regulation in Health Clinics The focus on personal data has merited the EU concerns and attention, resulting in the legislative change regarding privacy and the protection of personal data. The General Data Protection Regulation (GDPR) aims to reform existing measures on the protection of personal data of European Union citizens, with a strong impact on the rights and freedoms of individuals in establishing rules for the processing of personal data. The GDPR considers a special category of personal data, the health data, being these considered as sensitive data and subject to special conditions regarding treatment and access by third parties. This work presents the evolution of the applicability of the Regulation (EU) 2016679 six months after its application in Portuguese health clinics. The results of the present study are discussed in the light of future literature and work are identified.
An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.	Extensive Examination of XOR Arbiter PUFs as Security Primitives for ResourceConstrained IoT Devices Communication security is essential for the proper functioning of the Internet of Things. Traditional approaches that rely on cryptographic keys are vulnerable to sidechannel attacks. Physical Unclonable Functions (PUFs), leveraging unavoidable and irreproducible variations of integrated circuits to produce responses unique for individual PUF devices, are emerging as promising candidates as security primitives to provide keyless solutions. Before a PUF can be adopted for real applications, the PUF must be thoroughly examined to understand its various properties for its application feasibility. In this paper, we study XOR PUFs for broad ranges of values for circuit architecture parameters. XOR PUFs have been extensively studied, and have been shown to be unable to withstand machine learning attacks for 64bit XOR PUFs with less than ten component PUFs. Attack methods employed in existing studies need a large number of challengeresponse pairs (CRPs), which are obtainable only if the PUF has an open access interface. When PUFembedded devices equipped with mutual authentication or response obfuscating techniques, it is difficult for attackers to accumulate large numbers of CRPs. With only a small number of accumulated CRPs available to attackers, small size PUFs, like XOR PUFs with a small number of component PUFs and stages, may become resistant to machine learning attacks. Since smaller sizes mean less resourcedemanding, it is worthwhile to examine such PUFs which have usually been considered unsafe against attacks. Such are thoughts that have been motivating us in this paper to explore the PUF performances for a wide range of values of the PUF architecture parameters.	Erkundung und Erforschung. Alexander von Humboldts Amerikareise Zusammenfassung Ahnlich wie Adalbert Stifters Erzahler im Roman xe2x80x9eNachsommerxe2x80x9c verband A. v. Humboldt auf seiner Amerikareise Erkundung und Erforschung, Reiselust und Erkenntnisstreben. Humboldt hat sein doppeltes Ziel klar benannt: Bekanntmachung der besuchten Lander, Sammeln von Tatsachen zur Erweiterung der physikalischen Geographie. Der Aufsatz ist in funf Abschnitte gegliedert: Anliegen, Route, Methoden, Ergebnisse, Auswertung. Abstract In a similar way as Adalbert Stifteru0027s narrator in the novel xe2x80x9cLate summerxe2x80x9d A. v. Humboldt combined exploration with research, fondness for travelling with striving for findings during his travel through South America. Humboldt clearly indicated his double aim: to report on the visited countries, to collect facts in order to improve physical geography. The treatise consists of five sections: object, route, methods, results, evaluation.
An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.	Extensive Examination of XOR Arbiter PUFs as Security Primitives for ResourceConstrained IoT Devices Communication security is essential for the proper functioning of the Internet of Things. Traditional approaches that rely on cryptographic keys are vulnerable to sidechannel attacks. Physical Unclonable Functions (PUFs), leveraging unavoidable and irreproducible variations of integrated circuits to produce responses unique for individual PUF devices, are emerging as promising candidates as security primitives to provide keyless solutions. Before a PUF can be adopted for real applications, the PUF must be thoroughly examined to understand its various properties for its application feasibility. In this paper, we study XOR PUFs for broad ranges of values for circuit architecture parameters. XOR PUFs have been extensively studied, and have been shown to be unable to withstand machine learning attacks for 64bit XOR PUFs with less than ten component PUFs. Attack methods employed in existing studies need a large number of challengeresponse pairs (CRPs), which are obtainable only if the PUF has an open access interface. When PUFembedded devices equipped with mutual authentication or response obfuscating techniques, it is difficult for attackers to accumulate large numbers of CRPs. With only a small number of accumulated CRPs available to attackers, small size PUFs, like XOR PUFs with a small number of component PUFs and stages, may become resistant to machine learning attacks. Since smaller sizes mean less resourcedemanding, it is worthwhile to examine such PUFs which have usually been considered unsafe against attacks. Such are thoughts that have been motivating us in this paper to explore the PUF performances for a wide range of values of the PUF architecture parameters.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.	Extensive Examination of XOR Arbiter PUFs as Security Primitives for ResourceConstrained IoT Devices Communication security is essential for the proper functioning of the Internet of Things. Traditional approaches that rely on cryptographic keys are vulnerable to sidechannel attacks. Physical Unclonable Functions (PUFs), leveraging unavoidable and irreproducible variations of integrated circuits to produce responses unique for individual PUF devices, are emerging as promising candidates as security primitives to provide keyless solutions. Before a PUF can be adopted for real applications, the PUF must be thoroughly examined to understand its various properties for its application feasibility. In this paper, we study XOR PUFs for broad ranges of values for circuit architecture parameters. XOR PUFs have been extensively studied, and have been shown to be unable to withstand machine learning attacks for 64bit XOR PUFs with less than ten component PUFs. Attack methods employed in existing studies need a large number of challengeresponse pairs (CRPs), which are obtainable only if the PUF has an open access interface. When PUFembedded devices equipped with mutual authentication or response obfuscating techniques, it is difficult for attackers to accumulate large numbers of CRPs. With only a small number of accumulated CRPs available to attackers, small size PUFs, like XOR PUFs with a small number of component PUFs and stages, may become resistant to machine learning attacks. Since smaller sizes mean less resourcedemanding, it is worthwhile to examine such PUFs which have usually been considered unsafe against attacks. Such are thoughts that have been motivating us in this paper to explore the PUF performances for a wide range of values of the PUF architecture parameters.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.	Extensive Examination of XOR Arbiter PUFs as Security Primitives for ResourceConstrained IoT Devices Communication security is essential for the proper functioning of the Internet of Things. Traditional approaches that rely on cryptographic keys are vulnerable to sidechannel attacks. Physical Unclonable Functions (PUFs), leveraging unavoidable and irreproducible variations of integrated circuits to produce responses unique for individual PUF devices, are emerging as promising candidates as security primitives to provide keyless solutions. Before a PUF can be adopted for real applications, the PUF must be thoroughly examined to understand its various properties for its application feasibility. In this paper, we study XOR PUFs for broad ranges of values for circuit architecture parameters. XOR PUFs have been extensively studied, and have been shown to be unable to withstand machine learning attacks for 64bit XOR PUFs with less than ten component PUFs. Attack methods employed in existing studies need a large number of challengeresponse pairs (CRPs), which are obtainable only if the PUF has an open access interface. When PUFembedded devices equipped with mutual authentication or response obfuscating techniques, it is difficult for attackers to accumulate large numbers of CRPs. With only a small number of accumulated CRPs available to attackers, small size PUFs, like XOR PUFs with a small number of component PUFs and stages, may become resistant to machine learning attacks. Since smaller sizes mean less resourcedemanding, it is worthwhile to examine such PUFs which have usually been considered unsafe against attacks. Such are thoughts that have been motivating us in this paper to explore the PUF performances for a wide range of values of the PUF architecture parameters.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.	Extensive Examination of XOR Arbiter PUFs as Security Primitives for ResourceConstrained IoT Devices Communication security is essential for the proper functioning of the Internet of Things. Traditional approaches that rely on cryptographic keys are vulnerable to sidechannel attacks. Physical Unclonable Functions (PUFs), leveraging unavoidable and irreproducible variations of integrated circuits to produce responses unique for individual PUF devices, are emerging as promising candidates as security primitives to provide keyless solutions. Before a PUF can be adopted for real applications, the PUF must be thoroughly examined to understand its various properties for its application feasibility. In this paper, we study XOR PUFs for broad ranges of values for circuit architecture parameters. XOR PUFs have been extensively studied, and have been shown to be unable to withstand machine learning attacks for 64bit XOR PUFs with less than ten component PUFs. Attack methods employed in existing studies need a large number of challengeresponse pairs (CRPs), which are obtainable only if the PUF has an open access interface. When PUFembedded devices equipped with mutual authentication or response obfuscating techniques, it is difficult for attackers to accumulate large numbers of CRPs. With only a small number of accumulated CRPs available to attackers, small size PUFs, like XOR PUFs with a small number of component PUFs and stages, may become resistant to machine learning attacks. Since smaller sizes mean less resourcedemanding, it is worthwhile to examine such PUFs which have usually been considered unsafe against attacks. Such are thoughts that have been motivating us in this paper to explore the PUF performances for a wide range of values of the PUF architecture parameters.	Symmetric Simplicial Pseudoline Arrangements A simplicial arrangement of pseudolines is a collection of topological lines in the projective plane where each region that is formed is triangular. This paper refines and develops David Eppsteinu0027s notion of a kaleidoscope construction for symmetric pseudoline arrangements to construct and analyze several infinite families of simplicial pseudoline arrangements with high degrees of geometric symmetry. In particular, all simplicial pseudoline arrangements with the symmetries of a regular kgon and three symmetry classes of pseudolines, consisting of the mirrors of the kgon and two other symmetry classes, plus sometimes the line at infinity, are classified, and other interesting families (with more symmetry classes of pseudolines) are discussed.
Smartphone Architecture for EdgeCentric IoT Analytics. The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloudcore for analysis and data storage. This research, therefore, focuses on formulating an edgecentric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in realtime. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.	IoT and Microservice Architecture for Multimobility in a Smart City In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for generalpurpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
Smartphone Architecture for EdgeCentric IoT Analytics. The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloudcore for analysis and data storage. This research, therefore, focuses on formulating an edgecentric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in realtime. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.	IoT and Microservice Architecture for Multimobility in a Smart City In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for generalpurpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.	Symmetry Group Classification and Conservation Laws of the Nonlinear Fractional Diffusion Equation with the Riesz Potential Symmetry properties of a nonlinear twodimensional spacefractional diffusion equation with the Riesz potential of the order xcexb1 xe2x88x88 ( 0 , 1 ) are studied. Lie point symmetry group classification of this equation is performed with respect to diffusivity function. To construct conservation laws for the considered equation, the concept of nonlinear selfadjointness is adopted to a certain class of spacefractional differential equations with the Riesz potential. It is proved that the equation in question is nonlinearly selfadjoint. An extension of Ibragimovxe2x80x99s constructive algorithm for finding conservation laws is proposed, and the corresponding Noether operators for fractional differential equations with the Riesz potential are presented in an explicit form. To illustrate the proposed approach, conservation laws for the considered nonlinear spacefractional diffusion equation are constructed by using its Lie point symmetries.
Smartphone Architecture for EdgeCentric IoT Analytics. The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloudcore for analysis and data storage. This research, therefore, focuses on formulating an edgecentric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in realtime. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.	IoT and Microservice Architecture for Multimobility in a Smart City In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for generalpurpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.	Classifying unavoidable Tverberg partitions Let T(d,r) (r1)(d1)1 be the parameter in Tverbergu0027s theorem, and call a partition mathcal I of 1,2,ldots,T(d,r) into r parts a Tverberg type . We say that mathcal I o ccurs xc2xa0in an ordered point sequence P if P contains a subsequence Pu0027 of T(d,r) points such that the partition of Pu0027 that is orderisomorphic to mathcal I is a Tverberg partition. We say that mathcal I is unavoidable xc2xa0if it occurs in every sufficiently long point sequence. In this paper we study the problem of determining which Tverberg types are unavoidable. We conjecture a complete characterization of the unavoidable Tverberg types, and we prove some cases of our conjecture for dle 4. Along the way, we study the avoidability of many other geometric predicates. Our techniques also yield a large family of T(d,r)point sets for which the number of Tverberg partitions is exactly (r1)!d. This lends further support for Sierksmau0027s conjecture on the number of Tverberg partitions.
Smartphone Architecture for EdgeCentric IoT Analytics. The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloudcore for analysis and data storage. This research, therefore, focuses on formulating an edgecentric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in realtime. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.	IoT and Microservice Architecture for Multimobility in a Smart City In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for generalpurpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Smartphone Architecture for EdgeCentric IoT Analytics. The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloudcore for analysis and data storage. This research, therefore, focuses on formulating an edgecentric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in realtime. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.	IoT and Microservice Architecture for Multimobility in a Smart City In this paper, we will illustrate how microservice architectures can be adopted to build Internet of Things services for multimobility in a smart city. Traditional centralized architectures are built as monolithic solutions, which lack the flexibility required to deal with heterogeneous devices efficiently. Microservice architectures implement small features bounded within a running process; independent microservices can be deployed separately in a distributed system. We have proposed a draft of architecture for generalpurpose Internet of Things applications. Thanks to the choice of the microservice paradigm, the architecture is capable of interfacing with a wide range of heterogeneous IoT devices while implementing scalability by design. On this basis, a web application has been developed bearing in mind a set of real case scenarios mobility services for citizens multimobility in a smart city.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Breaking Band: A Breakdown of Highperformance Communication The critical path of internode communication on largescale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using a highlevel communication routine such as an MPI_Send, the payload of the message traverses multiple software stacks, the IO subsystem on both the host and target nodes, and network components such as the switch. In this paper, we analyze where, why, and how much time is spent on the critical path of communication by modeling the overall injection overhead and endtoend latency of a system. We focus our analysis on the performance of small messages since finegrained communication is becoming increasingly important with the growing trend of an increasing number of cores per node. The analytical models present an accurate and detailed breakdown of time spent in internode communication. We validate the models on Arm ThunderX2based servers connected with Mellanox InfiniBand. This is the first work of this kind on Arm. Alongside our breakdown, we describe the methodology to measure the time spent in each component so that readers with access to precise CPU timers and a PCIe analyzer can measure breakdowns on systems of their interest. Such a breakdown is crucial for software developers, system architects, and researchers to guide their optimization efforts. As researchers ourselves, we use the breakdown to simulate the impacts and discuss the likelihoods of a set of optimizations that target the bottlenecks in todayu0027s highperformance communication.	Evaluating the Impact of Energy Efficient Networks on HPC Workloads Interconnection networks grow larger as supercomputers include more nodes and require higher bandwidth for performance. This scaling significantly increases the fraction of power consumed by the network, by increasing the number of network components (links and switches). Typically, network links consume power continuously once they are turned on. However, recent proposals for energy efficient interconnects have introduced lowpower operation modes for periods when network links are idle. Lowpower operation can increase messaging time when switching a link from lowpower to active operation. We extend the TraceRCODES network simulator for power modeling to evaluate the impact of energy efficient networking on power and performance. Our evaluation presents the first study on both singlejob and multijob execution to realistically simulate power consumption and performance under congestion for a largescale HPC network. Results on several workloads consisting of HPC proxy applications show that singlejob and multijob execution favor different modes of low power operation to have significant power savings at the cost of minimal performance degradation.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
Breaking Band: A Breakdown of Highperformance Communication The critical path of internode communication on largescale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using a highlevel communication routine such as an MPI_Send, the payload of the message traverses multiple software stacks, the IO subsystem on both the host and target nodes, and network components such as the switch. In this paper, we analyze where, why, and how much time is spent on the critical path of communication by modeling the overall injection overhead and endtoend latency of a system. We focus our analysis on the performance of small messages since finegrained communication is becoming increasingly important with the growing trend of an increasing number of cores per node. The analytical models present an accurate and detailed breakdown of time spent in internode communication. We validate the models on Arm ThunderX2based servers connected with Mellanox InfiniBand. This is the first work of this kind on Arm. Alongside our breakdown, we describe the methodology to measure the time spent in each component so that readers with access to precise CPU timers and a PCIe analyzer can measure breakdowns on systems of their interest. Such a breakdown is crucial for software developers, system architects, and researchers to guide their optimization efforts. As researchers ourselves, we use the breakdown to simulate the impacts and discuss the likelihoods of a set of optimizations that target the bottlenecks in todayu0027s highperformance communication.	Evaluating the Impact of Energy Efficient Networks on HPC Workloads Interconnection networks grow larger as supercomputers include more nodes and require higher bandwidth for performance. This scaling significantly increases the fraction of power consumed by the network, by increasing the number of network components (links and switches). Typically, network links consume power continuously once they are turned on. However, recent proposals for energy efficient interconnects have introduced lowpower operation modes for periods when network links are idle. Lowpower operation can increase messaging time when switching a link from lowpower to active operation. We extend the TraceRCODES network simulator for power modeling to evaluate the impact of energy efficient networking on power and performance. Our evaluation presents the first study on both singlejob and multijob execution to realistically simulate power consumption and performance under congestion for a largescale HPC network. Results on several workloads consisting of HPC proxy applications show that singlejob and multijob execution favor different modes of low power operation to have significant power savings at the cost of minimal performance degradation.	Symmetry Group Classification and Conservation Laws of the Nonlinear Fractional Diffusion Equation with the Riesz Potential Symmetry properties of a nonlinear twodimensional spacefractional diffusion equation with the Riesz potential of the order xcexb1 xe2x88x88 ( 0 , 1 ) are studied. Lie point symmetry group classification of this equation is performed with respect to diffusivity function. To construct conservation laws for the considered equation, the concept of nonlinear selfadjointness is adopted to a certain class of spacefractional differential equations with the Riesz potential. It is proved that the equation in question is nonlinearly selfadjoint. An extension of Ibragimovxe2x80x99s constructive algorithm for finding conservation laws is proposed, and the corresponding Noether operators for fractional differential equations with the Riesz potential are presented in an explicit form. To illustrate the proposed approach, conservation laws for the considered nonlinear spacefractional diffusion equation are constructed by using its Lie point symmetries.
Breaking Band: A Breakdown of Highperformance Communication The critical path of internode communication on largescale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using a highlevel communication routine such as an MPI_Send, the payload of the message traverses multiple software stacks, the IO subsystem on both the host and target nodes, and network components such as the switch. In this paper, we analyze where, why, and how much time is spent on the critical path of communication by modeling the overall injection overhead and endtoend latency of a system. We focus our analysis on the performance of small messages since finegrained communication is becoming increasingly important with the growing trend of an increasing number of cores per node. The analytical models present an accurate and detailed breakdown of time spent in internode communication. We validate the models on Arm ThunderX2based servers connected with Mellanox InfiniBand. This is the first work of this kind on Arm. Alongside our breakdown, we describe the methodology to measure the time spent in each component so that readers with access to precise CPU timers and a PCIe analyzer can measure breakdowns on systems of their interest. Such a breakdown is crucial for software developers, system architects, and researchers to guide their optimization efforts. As researchers ourselves, we use the breakdown to simulate the impacts and discuss the likelihoods of a set of optimizations that target the bottlenecks in todayu0027s highperformance communication.	Evaluating the Impact of Energy Efficient Networks on HPC Workloads Interconnection networks grow larger as supercomputers include more nodes and require higher bandwidth for performance. This scaling significantly increases the fraction of power consumed by the network, by increasing the number of network components (links and switches). Typically, network links consume power continuously once they are turned on. However, recent proposals for energy efficient interconnects have introduced lowpower operation modes for periods when network links are idle. Lowpower operation can increase messaging time when switching a link from lowpower to active operation. We extend the TraceRCODES network simulator for power modeling to evaluate the impact of energy efficient networking on power and performance. Our evaluation presents the first study on both singlejob and multijob execution to realistically simulate power consumption and performance under congestion for a largescale HPC network. Results on several workloads consisting of HPC proxy applications show that singlejob and multijob execution favor different modes of low power operation to have significant power savings at the cost of minimal performance degradation.	General Data Protection Regulation in Health Clinics The focus on personal data has merited the EU concerns and attention, resulting in the legislative change regarding privacy and the protection of personal data. The General Data Protection Regulation (GDPR) aims to reform existing measures on the protection of personal data of European Union citizens, with a strong impact on the rights and freedoms of individuals in establishing rules for the processing of personal data. The GDPR considers a special category of personal data, the health data, being these considered as sensitive data and subject to special conditions regarding treatment and access by third parties. This work presents the evolution of the applicability of the Regulation (EU) 2016679 six months after its application in Portuguese health clinics. The results of the present study are discussed in the light of future literature and work are identified.
Breaking Band: A Breakdown of Highperformance Communication The critical path of internode communication on largescale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using a highlevel communication routine such as an MPI_Send, the payload of the message traverses multiple software stacks, the IO subsystem on both the host and target nodes, and network components such as the switch. In this paper, we analyze where, why, and how much time is spent on the critical path of communication by modeling the overall injection overhead and endtoend latency of a system. We focus our analysis on the performance of small messages since finegrained communication is becoming increasingly important with the growing trend of an increasing number of cores per node. The analytical models present an accurate and detailed breakdown of time spent in internode communication. We validate the models on Arm ThunderX2based servers connected with Mellanox InfiniBand. This is the first work of this kind on Arm. Alongside our breakdown, we describe the methodology to measure the time spent in each component so that readers with access to precise CPU timers and a PCIe analyzer can measure breakdowns on systems of their interest. Such a breakdown is crucial for software developers, system architects, and researchers to guide their optimization efforts. As researchers ourselves, we use the breakdown to simulate the impacts and discuss the likelihoods of a set of optimizations that target the bottlenecks in todayu0027s highperformance communication.	Evaluating the Impact of Energy Efficient Networks on HPC Workloads Interconnection networks grow larger as supercomputers include more nodes and require higher bandwidth for performance. This scaling significantly increases the fraction of power consumed by the network, by increasing the number of network components (links and switches). Typically, network links consume power continuously once they are turned on. However, recent proposals for energy efficient interconnects have introduced lowpower operation modes for periods when network links are idle. Lowpower operation can increase messaging time when switching a link from lowpower to active operation. We extend the TraceRCODES network simulator for power modeling to evaluate the impact of energy efficient networking on power and performance. Our evaluation presents the first study on both singlejob and multijob execution to realistically simulate power consumption and performance under congestion for a largescale HPC network. Results on several workloads consisting of HPC proxy applications show that singlejob and multijob execution favor different modes of low power operation to have significant power savings at the cost of minimal performance degradation.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Assistive method that controls joint stiffness and antagonized angle based on human joint stiffness characteristics and its application to an exoskeleton In this paper, we propose an assistance method that controls joint stiffness and the antagonized angle using variable elastic elements. The proposed system changes the stiffness and angle so that they correspond to the phase of movement and performs movement assistance in cooperation with the wearer. To achieve structural variability in the configuration of stiffness and the antagonized angle, we propose a joint structure in which the artificial muscle and tension spring are antagonistically arranged. While performing a movement, motion analysis was conducted to investigate the change in joint stiffness and antagonized angle. We confirmed that the proposed joint and human joint have the same tendency while in motion.	Complex Stiffness Model of Physical HumanRobot Interaction: Implications for Control of Performance Augmentation Exoskeletons Human joint dynamic stiffness plays an important role in the stability of performance augmentation exoskeletons. In this paper, we consider a new frequency domain model of the human joint dynamics which features a complex value stiffness. This complex stiffness consists of a real stiffness and a hysteretic damping. We use it to explain the dynamic behaviors of the human connected to the exoskeleton, in particular the observed nonzero low frequency phase shift and the near constant damping ratio of the resonance as stiffness and inertia vary. We validate this concept with an elbowjoint exoskeleton testbed (attached to a subject) by experimentally varying joint stiffness behavior, exoskeleton inertia, and the strength augmentation gain. We compare three different models of elbowjoint dynamic stiffness: a model with real stiffness, viscous damping and inertia; a model with complex stiffness and inertia; and a model combining the previous two models. Our results show that the hysteretic damping term improves modeling accuracy (via a statistical Ftest). Moreover, this term contributes more to model accuracy than the viscous damping term. In addition, we experimentally observe a linear relationship between the hysteretic damping and the real part of the stiffness which allows us to simplify the complex stiffness model down to a 1parameter system. Ultimately, we design a fractional order controller to demonstrate how human hysteretic damping behavior can be exploited to improve strength amplification performance while maintaining stability.	Virtually perfect democracy In the 2009 Security Protocols Workshop, the Pretty Good Democracy scheme was presented. This scheme has the appeal of allowing voters to cast votes remotely, e.g. via the Internet, and confirm correct receipt in a single session. The scheme provides a degree of endto end verifiability: receipt of the correct acknowledgement code provides assurance that the vote will be accurately included in the final tally. The scheme does not require any trust in a voter client device. It does however have a number of vulnerabilities: privacy and accuracy depend on vote codes being kept secret. It also suffers the usual coercion style threats common to most remote voting schemes.
Assistive method that controls joint stiffness and antagonized angle based on human joint stiffness characteristics and its application to an exoskeleton In this paper, we propose an assistance method that controls joint stiffness and the antagonized angle using variable elastic elements. The proposed system changes the stiffness and angle so that they correspond to the phase of movement and performs movement assistance in cooperation with the wearer. To achieve structural variability in the configuration of stiffness and the antagonized angle, we propose a joint structure in which the artificial muscle and tension spring are antagonistically arranged. While performing a movement, motion analysis was conducted to investigate the change in joint stiffness and antagonized angle. We confirmed that the proposed joint and human joint have the same tendency while in motion.	Complex Stiffness Model of Physical HumanRobot Interaction: Implications for Control of Performance Augmentation Exoskeletons Human joint dynamic stiffness plays an important role in the stability of performance augmentation exoskeletons. In this paper, we consider a new frequency domain model of the human joint dynamics which features a complex value stiffness. This complex stiffness consists of a real stiffness and a hysteretic damping. We use it to explain the dynamic behaviors of the human connected to the exoskeleton, in particular the observed nonzero low frequency phase shift and the near constant damping ratio of the resonance as stiffness and inertia vary. We validate this concept with an elbowjoint exoskeleton testbed (attached to a subject) by experimentally varying joint stiffness behavior, exoskeleton inertia, and the strength augmentation gain. We compare three different models of elbowjoint dynamic stiffness: a model with real stiffness, viscous damping and inertia; a model with complex stiffness and inertia; and a model combining the previous two models. Our results show that the hysteretic damping term improves modeling accuracy (via a statistical Ftest). Moreover, this term contributes more to model accuracy than the viscous damping term. In addition, we experimentally observe a linear relationship between the hysteretic damping and the real part of the stiffness which allows us to simplify the complex stiffness model down to a 1parameter system. Ultimately, we design a fractional order controller to demonstrate how human hysteretic damping behavior can be exploited to improve strength amplification performance while maintaining stability.	A Critical Look at the 2019 College Admissions Scandal Discusses the 2019 College admissions scandal. Let me begin with a disclaimer: I am making no legal excuses for the participants in the current scandal. I am only offering contextual background that places it in the broader academic, cultural, and political perspective required for understanding. It is only the most recent installment of a wellworn narrative: the controlling elite make their own rules and live by them, if they can get away with it. Unfortunately, some of the participants, who are either serving or facing jail time, didnxe2x80x99t know to not go into a gunfight with a sharp stick. Money alone is not enough to avoid prosecution for fraud: you need political clout. The best protection a defendant can have is a prosecutor who fears political reprisal. Compare how the Koch brothers escaped prosecution for stealing millions of oil dollars from Native American tribes1,2 with the fate of actresses Lori Loughlin and Felicity Huffman, who, at the time of this writing, face jail time for paying bribes to get their children into good universities.3,4 In the former case, the federal prosecutor who dared to empanel a grand jury to get at the truth was fired for cause, which put a quick end to the prosecution. In the latter case, the prosecutors pushed for jail terms and public admonishment with the zeal of Oliver Cromwell. There you have it: stealing oil from Native Americans versus trying to bribe your kids into a great university. Where is the greater crime? Admittedly, these actresses and their
Assistive method that controls joint stiffness and antagonized angle based on human joint stiffness characteristics and its application to an exoskeleton In this paper, we propose an assistance method that controls joint stiffness and the antagonized angle using variable elastic elements. The proposed system changes the stiffness and angle so that they correspond to the phase of movement and performs movement assistance in cooperation with the wearer. To achieve structural variability in the configuration of stiffness and the antagonized angle, we propose a joint structure in which the artificial muscle and tension spring are antagonistically arranged. While performing a movement, motion analysis was conducted to investigate the change in joint stiffness and antagonized angle. We confirmed that the proposed joint and human joint have the same tendency while in motion.	Complex Stiffness Model of Physical HumanRobot Interaction: Implications for Control of Performance Augmentation Exoskeletons Human joint dynamic stiffness plays an important role in the stability of performance augmentation exoskeletons. In this paper, we consider a new frequency domain model of the human joint dynamics which features a complex value stiffness. This complex stiffness consists of a real stiffness and a hysteretic damping. We use it to explain the dynamic behaviors of the human connected to the exoskeleton, in particular the observed nonzero low frequency phase shift and the near constant damping ratio of the resonance as stiffness and inertia vary. We validate this concept with an elbowjoint exoskeleton testbed (attached to a subject) by experimentally varying joint stiffness behavior, exoskeleton inertia, and the strength augmentation gain. We compare three different models of elbowjoint dynamic stiffness: a model with real stiffness, viscous damping and inertia; a model with complex stiffness and inertia; and a model combining the previous two models. Our results show that the hysteretic damping term improves modeling accuracy (via a statistical Ftest). Moreover, this term contributes more to model accuracy than the viscous damping term. In addition, we experimentally observe a linear relationship between the hysteretic damping and the real part of the stiffness which allows us to simplify the complex stiffness model down to a 1parameter system. Ultimately, we design a fractional order controller to demonstrate how human hysteretic damping behavior can be exploited to improve strength amplification performance while maintaining stability.	On the Probabilistic Degrees of Symmetric Boolean functions. The probabilistic degree of a Boolean function f:0,1nrightarrow 0,1 is defined to be the smallest d such that there is a random polynomial mathbfP of degree at most d that agrees with f at each point with high probability. Introduced by Razborov (1987), upper and lower bounds on probabilistic degrees of Boolean functions specifically symmetric Boolean functions have been used to prove explicit lower bounds, design pseudorandom generators, and devise algorithms for combinatorial problems. :76,this paper, we characterize the probabilistic degrees of all symmetric Boolean functions up to polylogarithmic factors over all fields of fixed characteristic (positive or zero).
Assistive method that controls joint stiffness and antagonized angle based on human joint stiffness characteristics and its application to an exoskeleton In this paper, we propose an assistance method that controls joint stiffness and the antagonized angle using variable elastic elements. The proposed system changes the stiffness and angle so that they correspond to the phase of movement and performs movement assistance in cooperation with the wearer. To achieve structural variability in the configuration of stiffness and the antagonized angle, we propose a joint structure in which the artificial muscle and tension spring are antagonistically arranged. While performing a movement, motion analysis was conducted to investigate the change in joint stiffness and antagonized angle. We confirmed that the proposed joint and human joint have the same tendency while in motion.	Complex Stiffness Model of Physical HumanRobot Interaction: Implications for Control of Performance Augmentation Exoskeletons Human joint dynamic stiffness plays an important role in the stability of performance augmentation exoskeletons. In this paper, we consider a new frequency domain model of the human joint dynamics which features a complex value stiffness. This complex stiffness consists of a real stiffness and a hysteretic damping. We use it to explain the dynamic behaviors of the human connected to the exoskeleton, in particular the observed nonzero low frequency phase shift and the near constant damping ratio of the resonance as stiffness and inertia vary. We validate this concept with an elbowjoint exoskeleton testbed (attached to a subject) by experimentally varying joint stiffness behavior, exoskeleton inertia, and the strength augmentation gain. We compare three different models of elbowjoint dynamic stiffness: a model with real stiffness, viscous damping and inertia; a model with complex stiffness and inertia; and a model combining the previous two models. Our results show that the hysteretic damping term improves modeling accuracy (via a statistical Ftest). Moreover, this term contributes more to model accuracy than the viscous damping term. In addition, we experimentally observe a linear relationship between the hysteretic damping and the real part of the stiffness which allows us to simplify the complex stiffness model down to a 1parameter system. Ultimately, we design a fractional order controller to demonstrate how human hysteretic damping behavior can be exploited to improve strength amplification performance while maintaining stability.	Optimal Multiplexed Erasure Codes for Streaming Messages with Different Decoding Delays This paper considers multiplexing two sequences of messages with two different decoding delays over a packet erasure channel. In each time slot, the source constructs a packet based on the current and previous messages and transmits the packet, which may be erased when the packet travels from the source to the destination. The destination must perfectly recover every source message in the first sequence subject to a decoding delay T_mathrmv and every source message in the second sequence subject to a shorter decoding delay T_mathrmule T_mathrmv. We assume that the channel loss model introduces a burst erasure of a fixed length B on the discrete timeline. Under this channel loss assumption, the capacity region for the case where T_mathrmvle T_mathrmuB was previously solved. In this paper, we fully characterize the capacity region for the remaining case T_mathrmvu003e T_mathrmuB. The key step in the achievability proof is achieving the nontrivial corner point of the capacity region through using a multiplexed streaming code constructed by superimposing two singlestream codes. The converse is proved by obtaining a genieaided bound when the channel is subject to a periodic erasure pattern where each period consists of a lengthB burst erasure followed by a lengthT_mathrmu noiseless duration.
Assistive method that controls joint stiffness and antagonized angle based on human joint stiffness characteristics and its application to an exoskeleton In this paper, we propose an assistance method that controls joint stiffness and the antagonized angle using variable elastic elements. The proposed system changes the stiffness and angle so that they correspond to the phase of movement and performs movement assistance in cooperation with the wearer. To achieve structural variability in the configuration of stiffness and the antagonized angle, we propose a joint structure in which the artificial muscle and tension spring are antagonistically arranged. While performing a movement, motion analysis was conducted to investigate the change in joint stiffness and antagonized angle. We confirmed that the proposed joint and human joint have the same tendency while in motion.	Complex Stiffness Model of Physical HumanRobot Interaction: Implications for Control of Performance Augmentation Exoskeletons Human joint dynamic stiffness plays an important role in the stability of performance augmentation exoskeletons. In this paper, we consider a new frequency domain model of the human joint dynamics which features a complex value stiffness. This complex stiffness consists of a real stiffness and a hysteretic damping. We use it to explain the dynamic behaviors of the human connected to the exoskeleton, in particular the observed nonzero low frequency phase shift and the near constant damping ratio of the resonance as stiffness and inertia vary. We validate this concept with an elbowjoint exoskeleton testbed (attached to a subject) by experimentally varying joint stiffness behavior, exoskeleton inertia, and the strength augmentation gain. We compare three different models of elbowjoint dynamic stiffness: a model with real stiffness, viscous damping and inertia; a model with complex stiffness and inertia; and a model combining the previous two models. Our results show that the hysteretic damping term improves modeling accuracy (via a statistical Ftest). Moreover, this term contributes more to model accuracy than the viscous damping term. In addition, we experimentally observe a linear relationship between the hysteretic damping and the real part of the stiffness which allows us to simplify the complex stiffness model down to a 1parameter system. Ultimately, we design a fractional order controller to demonstrate how human hysteretic damping behavior can be exploited to improve strength amplification performance while maintaining stability.	On Colorings Avoiding a Rainbow Cycle and a Fixed Monochromatic Subgraph Let H and G be two graphs on fixed number of vertices. An edge coloring of a complete graph is called (H,G)good if there is no monochromatic copy of G and no rainbow (totally multicolored) copy of H in this coloring. As shown by Jamison and West, an (H,G)good coloring of an arbitrarily large complete graph exists unless either G is a star or H is a forest. The largest number of colors in an (H,G)good coloring of K_n is denoted maxR(n, G,H). For graphs H which can not be vertexpartitioned into at most two induced forests, maxR(n, G,H) has been determined asymptotically. Determining maxR(n; G, H) is challenging for other graphs H, in particular for bipartite graphs or even for cycles. This manuscript treats the case when H is a cycle. The value of maxR(n, G, C_k) is determined for all graphs G whose edges do not induce a star.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance childrenu0027s language writing experience and motivate them for selflearning.	Collaborative Approaches to ProblemSolving on Lines and Angles Using Augmented Reality The concepts of 2D geometry are often taught in classrooms without the analysis of the learnersu0027 understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the studentsu0027 approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At xcexb10.05 (t2.21, p0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M4.07) as compared to the dyads (65.23; M3.94).	Its time to rethink DDoS protection When you think of distributed denial of service (DDoS) attacks, chances are you conjure up an image of an overwhelming flood of traffic that incapacitates a network. This kind of cyber attack is all about overt, brute force used to take a target down. Some hackers are a little smarter, using DDoS as a distraction while they simultaneously attempt a more targeted strike, as was the case with a Carphone Warehouse hack in 2015. 1 But in general, DDoS isnu0027t subtle. Retailers are having to rethink how they approach distributed denial of service (DDoS) protection following the rise of a stealthier incarnation of the threat. There has been a significant increase in smallscale DDoS attacks and a corresponding reduction in conventional largescale events. The hackerxe2x80x99s aim is to remain below the conventional xe2x80x98detect and alertxe2x80x99 threshold that could trigger a DDoS mitigation strategy. Roy Reynolds of Vodat International explains the nature of the threat and the steps organisations can take to protect themselves.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance childrenu0027s language writing experience and motivate them for selflearning.	Collaborative Approaches to ProblemSolving on Lines and Angles Using Augmented Reality The concepts of 2D geometry are often taught in classrooms without the analysis of the learnersu0027 understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the studentsu0027 approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At xcexb10.05 (t2.21, p0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M4.07) as compared to the dyads (65.23; M3.94).	MToS: MultiTenant Network Over Software Defined Networking MToS, a multitenant network service is designed and implemented under SoftwareDefined Network (SDN) environment. One of the solutions to establish multitenant network in nonSDN environment is MultiProtocol Label Switching Virtual Private Network that involves numerous and complicated protocols to be configured prior to the establishment of multitenant network. With SDN, it opens new opportunities to create multitenant networks that are less complicated, more automated, and lower implementation cost via SDN commodity devices. MToS categorizes OpenFlow switches into three hierarchies, where matching fields and actions in flow entries are different depending on the switch hierarchy. Therefore, traffic forwarding can be scalable. MToS provides tenant isolation through dedicate flow table associated with each tenant, and tenant MAC address as the identifier which is used in flow table redirection and packet header modification. Traffic forwarding between Edge switches is achieved through Edge MAC address as the identifier which is used in packet header modification, and Edge switch serves as ARP Proxy for tenant end hosts. Comparing to MPLS VPN, MToS only requires essential information about tenants to construct a multitenant network. By taking advantage of SDN centralized global network information, MToS adds automations of IP address management and shortest path routes calculation. MToS is developed based upon OpenFlow version 1.3, and implemented in Python and runs on top of Ryu SDN framework.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance childrenu0027s language writing experience and motivate them for selflearning.	Collaborative Approaches to ProblemSolving on Lines and Angles Using Augmented Reality The concepts of 2D geometry are often taught in classrooms without the analysis of the learnersu0027 understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the studentsu0027 approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At xcexb10.05 (t2.21, p0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M4.07) as compared to the dyads (65.23; M3.94).	On the Round Complexity of Randomized Byzantine Agreement We prove lower bounds on the round complexity of randomized Byzantine agreement (BA) protocols, bounding the halting probability of such protocols after one and two rounds. In particular, we prove that: :31,BA protocols resilient against n3 resp., n4 corruptions terminate (under attack) at the end of the first round with probability at most o(1) resp., 12 o(1). :31,BA protocols resilient against :58,n4 corruptions terminate at the end of the second round with probability at most 1Theta(1). :78,For a large class of protocols (including all :31,BA protocols used in practice) and under a plausible combinatorial conjecture, :31,BA protocols resilient against n3 resp., n4 corruptions terminate at the end of the second round with probability at most o(1) resp., 12 o(1). :123,141,above bounds hold even when the parties use a trusted setup phase, e.g., a publickey infrastructure (PKI). third bound essentially matches the recent protocol of Micali (ITCSu002717) that tolerates up to n3 corruptions and terminates at the end of the third round with constant probability.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance childrenu0027s language writing experience and motivate them for selflearning.	Collaborative Approaches to ProblemSolving on Lines and Angles Using Augmented Reality The concepts of 2D geometry are often taught in classrooms without the analysis of the learnersu0027 understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the studentsu0027 approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At xcexb10.05 (t2.21, p0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M4.07) as compared to the dyads (65.23; M3.94).	Trust Degree Calculation Method Based on Trust Blockchain Node Due to the diversity and mobility of blockchain network nodes and the decentralized nature of blockchain networks, traditional trust value evaluation indicators cannot be directly used. In order to obtain trusted nodes, a trustworthiness calculation method based on trust blockchain nodes is proposed. Different from the traditional P2P network trust value calculation, the trust blockchain not only acquires the working state of the node, but also collects the special behavior information of the node, and calculates the joining time by synthesizing the trust value generated by the node transaction and the trust value generated by the node behavior. After the attenuation factor is comprehensively evaluated, the trusted nodes are selected to effectively ensure the security of the blockchain network environment, while reducing the average transaction delay and increasing the block rate.
Design and Development of an Augmented Reality Tracing Application for Kindergarten Students Children at the age of 3 to 5 years, start to learn language writing by tracing over alphabets. They start by joining the dots marked on books to create an alphabet. However, this learning process is not very effective in engaging children for active and creative learning. In this paper, we present an Augmented Reality Tracing (ART) application that creates an interactive learning environment for kids. Although the basic idea of tracing remains the same, ART allows children to recognize the mistracing or deviation and encourages them to trace better in the next trial. Therefore, the application would enhance childrenu0027s language writing experience and motivate them for selflearning.	Collaborative Approaches to ProblemSolving on Lines and Angles Using Augmented Reality The concepts of 2D geometry are often taught in classrooms without the analysis of the learnersu0027 understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the studentsu0027 approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At xcexb10.05 (t2.21, p0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M4.07) as compared to the dyads (65.23; M3.94).	A Spatialxe2x80x93Temporal SubspaceBased Compressive Channel Estimation Technique in Unknown Interference MIMO Channels Spatialxe2x80x93temporal (ST) subspacebased channel estimation techniques formulated with ell 2 minimum mean square error (MMSE) criterion alleviate the multiaccess interference (MAI) problem when the interested signals exhibit lowrank property. However, the conventional ell 2 ST subspacebased methods suffer from mean squared error (MSE) deterioration in unknown interference channels, due to the difficulty to separate the interested signals from the channel covariance matrices (CCMs) contaminated with unknown interference. As a solution to the problem, we propose a new ell 1 regularized ST channel estimation algorithm by applying the expectationmaximization (EM) algorithm to iteratively examine the signal subspace and the corresponding sparsesupports. The new algorithm updates the CCM independently of the slotdependent ell 1 regularization, which enables it to correctly perform the sparseindependent component analysis (ICA) with a reasonable complexity order. Simulation results shown in this paper verify that the proposed technique significantly improves MSE performance in unknown interference MIMO channels, and hence, solves the BER floor problems from which the conventional receivers suffer.
A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.	Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.	The longterm effect of media violence exposure on aggression of youngsters Abstract The effect of media violence on aggression has always been a trending issue, and a better understanding of the psychological mechanism of the impact of media violence on youth aggression is an extremely important research topic for preventing the negative impacts of media violence and juvenile delinquency. From the perspective of anger, this study explored the longterm effect of different degrees of media violence exposure on the aggression of youngsters, as well as the role of aggressive emotions. The studies found that individuals with a high degree of media violence exposure (HMVE) exhibited higher levels of proactive aggression in both irritation situations and higher levels of reactive aggression in lowirritation situations than did participants with a low degree of media violence exposure (LMVE). After being provoked, the anger of all participants was significantly increased, and the anger and proactive aggression levels of the HMVE group were significantly higher than those of the LMVE group. Additionally, rumination and anger played a mediating role in the relationship between media violence exposure and aggression. Overall, this study enriches the theoretical understanding of the longterm effect of media violence exposure on individual aggression. Second, this study deepens our understanding of the relatively new and relevant phenomenon of the mechanism between media violence exposure and individual aggression.
A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.	Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.	Extremal Problems for tPartite and tColorable Hypergraphs Fix integers t ge r ge 2 and an runiform hypergraph F. We prove that the maximum number of edges in a tpartite runiform hypergraph on n vertices that contains no copy of F is c_t, Fn choose r o(nr), where c_t, F can be determined by a finite computation. We explicitly define a sequence F_1, F_2, ldots of runiform hypergraphs, and prove that the maximum number of edges in a tchromatic runiform hypergraph on n vertices containing no copy of F_i is alpha_t,r,in choose r o(nr), where alpha_t,r,i can be determined by a finite computation for each ige 1. In several cases, alpha_t,r,i is irrational. The main tool used in the proofs is the Lagrangian of a hypergraph.
A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.	Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.	Symmetric Simplicial Pseudoline Arrangements A simplicial arrangement of pseudolines is a collection of topological lines in the projective plane where each region that is formed is triangular. This paper refines and develops David Eppsteinu0027s notion of a kaleidoscope construction for symmetric pseudoline arrangements to construct and analyze several infinite families of simplicial pseudoline arrangements with high degrees of geometric symmetry. In particular, all simplicial pseudoline arrangements with the symmetries of a regular kgon and three symmetry classes of pseudolines, consisting of the mirrors of the kgon and two other symmetry classes, plus sometimes the line at infinity, are classified, and other interesting families (with more symmetry classes of pseudolines) are discussed.
A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.	Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic LowCost Biomedical Sensor. Cardiovascular diseases are the leading cause of death around the world. As a result, lowcost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a lowcost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical lowcost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a wellaccepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.	Simultaneous Multiple Features Tracking of Beats: A Representation Learning Approach to Reduce False Alarm Rate in ICUs The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rulebased and machine learningbased techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm. That calls for novel methods that can accurately detect the cardiac events by only accessing one signal (e.g., ECG) with a low level of computation and sensors requirement. We propose a novel and robust representation learning framework for ECG analysis that only rely on a single lead ECG signal and yet achieves considerably better performance compared to the stateoftheart works in this domain, without relying on an expert knowledge. We evaluate the performance of this method using the xe2x80x9c2015 Physionet computing in cardiology challengexe2x80x9d dataset. To the best of our knowledge, the best previously reported performance is based on both expert knowledge and machine learning where all available signals of ECG, ABP and PPG are utilized. Our proposed method reaches the performance of 97.3%, 95.5%, and 90.8% in terms of sensitivity, specificity, and the challengeu0027s score, respectively for the detection of five arrhythmias when only one single ECG lead signals is used without any expert knowledge11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. 22The copyright notice: 97817281186731931.00 xc2xa92019 IEEE.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.	Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.	Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.	Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.	Symmetric Simplicial Pseudoline Arrangements A simplicial arrangement of pseudolines is a collection of topological lines in the projective plane where each region that is formed is triangular. This paper refines and develops David Eppsteinu0027s notion of a kaleidoscope construction for symmetric pseudoline arrangements to construct and analyze several infinite families of simplicial pseudoline arrangements with high degrees of geometric symmetry. In particular, all simplicial pseudoline arrangements with the symmetries of a regular kgon and three symmetry classes of pseudolines, consisting of the mirrors of the kgon and two other symmetry classes, plus sometimes the line at infinity, are classified, and other interesting families (with more symmetry classes of pseudolines) are discussed.
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.	Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.	Robust cluster consensus of general fractionalorder nonlinear multi agent systems via adaptive sliding mode controller Abstract In this paper robust cluster consensus is investigated for general fractionalorder multi agent systems with nonlinear dynamics with dynamic uncertainty and external disturbances via adaptive sliding mode controller. First, robust cluster consensus for general fractionalorder nonlinear multi agent systems is investigated with dynamic uncertainty and external disturbances in which multi agent systems are weakly heterogeneous because they have identical nominal dynamics with different normbounded parameter uncertainties. Then, robust cluster consensus for the fractionalorder nonlinear multi agent systems with general form dynamics is investigated by using adaptive sliding mode controller. Robust cluster consensus for general fractionalorder nonlinear multi agent systems is achieved asymptotically without disturbance. It is shown that the errors between agents can converge to a small region in the presence of disturbances based on the linear matrix inequality (LMI) and MittagLeffler stability theory. Finally, simulation examples are presented for general form multi agent systems, i.e. a singlelink flexible joint manipulator which demonstrates the efficiency of the proposed adaptive controller.
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.	Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.	Symmetry Group Classification and Conservation Laws of the Nonlinear Fractional Diffusion Equation with the Riesz Potential Symmetry properties of a nonlinear twodimensional spacefractional diffusion equation with the Riesz potential of the order xcexb1 xe2x88x88 ( 0 , 1 ) are studied. Lie point symmetry group classification of this equation is performed with respect to diffusivity function. To construct conservation laws for the considered equation, the concept of nonlinear selfadjointness is adopted to a certain class of spacefractional differential equations with the Riesz potential. It is proved that the equation in question is nonlinearly selfadjoint. An extension of Ibragimovxe2x80x99s constructive algorithm for finding conservation laws is proposed, and the corresponding Noether operators for fractional differential equations with the Riesz potential are presented in an explicit form. To illustrate the proposed approach, conservation laws for the considered nonlinear spacefractional diffusion equation are constructed by using its Lie point symmetries.
Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.	Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.	Proposal of a Disposable Camera App Providing Sensory Reality of Analog Operation on Smartphone Disposable Camera, officially called Film with lens, which was widely used in the 1980s and 1990s, is now attracting public attention again, creating a boom. It was common that people owned no cameras in the 1980s and 1990s before the devices like digital cameras and smartphones became widespread. It led to the spread of Disposable Camera on a global scale, which were available at a low price for people to purchase from stores such as convenience stores when they were on business or personal trip. Though most people own smartphones with advanced digital camera function today, analog film camera or film with lens with considerably low picture quality and fewer functions made a comeback and are gaining popularity. The reason lies in the reality felt in the use of the camera as a tool. It refers to a series of tasks with the simple Analog Camera recording objects on the film and printing out the pictures on papers. The process gives younger generations, who are not familiar with analog cameras, a feeling of novelty and interest. Disposable Camera gives the realistic feel of using analog device which film cameras provide, and users can easily enjoy that feel. This is considered to be generating a great demand for disposable cameras, but they are bulky, larger and have less mobility compared to smartphones. The current generation who are used to smartphones might feel uncomfortable using these cameras. Thus, this study proposes an application that represents Disposable Camera on smartphone.
Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.	Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.	Rickshaw Buddy RICKSHAW BUDDY is a lowcost automated assistance system for threewheeler auto rickshaws to reduce the high rate of accidents in the streets of developing countries like Bangladesh. It is a given fact that the lack of over speed alert, back camera, detection of rear obstacle and delay of maintenance are causes behind fatal accidents. These systems are absent not only in auto rickshaws but also most public transports. For this system, surveys have been done in different phases among the passengers, drivers and even the conductors for a useful and successful result. Since the system is very cheap, the lowincome drivers and owners of vehicles will be able to afford it easily making road safety the first and foremost priority.
Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.	Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.	Quantum Gravity. Gravitons should have momentum just as photons do; and since graviton momentum would cause compression rather than elongation of spacetime outside of matter; it does not appear that gravitons are compatible with Swartzchildu0027s spacetime curvature. Also, since energy is proportional to mass, and mass is proportional to gravity; the energy of matter is proportional to gravity. The energy of matter could thus contract space within matter; and because of the interconnectedness of space, cause the elongation of space outside of matter. And this would be compatible with Swartzchild spacetime curvature. Since gravity could be initiated within matter by the energy of mass, transmitted to space outside of matter by the interconnectedness of space; and also transmitted through space by the same interconnectedness of space; and since spatial and relativistic gravities can apparently be produced without the aid of gravitons; massive gravity could also be produced without gravitons as well. Gravity divided by an infinite number of segments would result in zero expression of gravity, because it could not curve spacetime. So spatial segments must have a minimum size, which is the Planck length; thus resulting in quantized space. And since gravity is always expressed over some distance in space, quantum space would therefore always quantize gravity. So the nonmediation of gravity by gravitons does not result in unquantized gravity, because quantum space can quantize gravity; thus making gravitons unproven and unnecessary, and explaining why gravitons have never been found.
Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.	Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.	Development and Flight Experiments of a Bluffbodied X4Blimp The body of X4blimp using four propellers manufactured in conventional research was a structure which has arranged four envelopes in which the buoyancy was equally divided centering on the gondola to which the propeller was attached. However, with this structure, the variation in the buoyancy arose among four envelopes, and there was a problem to which the body posture becomes unstable. In this research, it returns to the starting point which arranges one envelope at the center of the body, and the body of a fundamental structure of the nonstreamline is developed, in which the number of envelopes is suppressed to the minimum, and the variation in the buoyancy is avoided by attaching the special frame which can carry four propellers in the circumference of the envelope. The validity of the manufactured body is demonstrated through some flight experiments.
Computational predictions of hostpathogen interactions using domain and sequence signature Pathogens have specialized proteins to invade and infect the host by interacting with the host proteins. Despite several experimental studies, knowledge about hostpathogen interactions (HPIs) is limited. However, this knowledge is essential to understand disease mechanism and identify potential targets for disease prevention and intervention. In this study, we propose a pipeline for identifying hostpathogen proteinprotein interactions. The pipeline consists of a biological knowledgebased filter, a domainbased statistical filter followed by a sequencesignature based machine learning method. Interspecies proteinprotein interaction data between eukaryotic pathogens and human was used to build the domainbased statistical model. Known hostpathogen interactions of all eukaryotic pathogens from HPIDB and noninteracting protein data from Negatome were used as positive and negative training sets, respectively to train the machine learning model. We applied our pipeline to predict HPIs between human and malarial parasite, P. falciparum. Several biologically relevant features like tissue specificity, protein annotations and functions were used to construct a primary list of possible HPIs. Next, the statistical and machine learning based models were used as filters on the initial list to predict novel proteinprotein interactions between human and P. falciparum during intraerythrocytic stages. We have predicted several HPIs that are involved in host erythrocyte cytoskeleton remodeling, signaling and immune response. The proposed method can be used to find novel HPIs between human cells and any eukaryotic pathogens.	Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.	An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.	Death Ground Death Ground is a competitive musical installationgame for two players. The work is designed to provide the framework for the playersparticipants in which to perform gamemediated musical gestures against eachother. The main mechanic involves destroying the other playeru0027s avatar by outmaneuvering and using audio weapons and improvised musical actions against it. These weapons are spawned in an enclosed area during the performance and can be used by whoever is collects them first. There is a multitude of such powerups, all of which have different properties, such as speed boost, additional damage, ground traps and so on. All of these weapons affect the sound and sonic textures that each of the avatars produce. Additionally, the players can use elements of the environment such as platforms, obstructions and elevation in order to gain competitive advantage, or position themselves strategically to access first the spawned powerups.
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.	An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.	Beyond Reality In virtual reality (VR), a new language of sound design is emerging. As directors grapple to find solutions to some of the inherent problems of telling a story in VRxe2x80x94for instance, the audienceu0027s ability to control the field of viewxe2x80x94sound designers are playing a new role in subconsciously guiding the audienceu0027s attention and consequently, are framing the narrative. However, developing a new language of sound design requires time for creative experimentation, and in direct opposition to this, a typical VR workflow often features compressed project timelines, software difficulties, and budgetary constraints. Turning to VR sound research offers little guidance to sound designers, where decades of research has focused on high fidelity and realistic sound representation in the name of presence and uninterrupted immersion McRoberts, 2018, largely ignoring the potential contribution of cinematic sound design practices that use creative sound to guide an audienceu0027s emotion. Angela McArthur, Rebecca Stewart, and Mark Sandler go as far as to argue that unrealistic and creative sound design may be crucial for an audienceu0027s emotional engagement in virtual reality McArthur et al., 2017. To make a contribution towards the new language of sound for VR, and with reference to the literature, this practiceled research explores cinematic sound practices and principles within 360film through the production of a 5minute 360film entitled Afraid of the Dark. The research is supported by a contextual survey including unpublished interviews with the sound designers of three 360films that had the budget and time to experiment with cinematic sound practices namely, xe2x80x9cUnder the Canopyxe2x80x9d with sound design by Joel Douek, xe2x80x9cMy Africaxe2x80x9d with sound design by Roland Heap, and Emmy awardwinning xe2x80x9cCollisionsxe2x80x9d with sound design by Oscarnominated Tom Myers from Skywalker Sound. Additional insights are included from an unpublished interview with an experienced team of 360film sound designers from xe2x80x9cCutting Edgexe2x80x9d in Brisbane Australia xe2x80x93 Mike Lange, Michael Thomas and Heath Plumb. The findings detail the benefits of thinking about sound from the beginning of preproduction, the practical considerations of onset sound recording, and differing approaches to realistic representation and creative design for documentary in the sound studio. Additionally, the research contributes a lowbudget workflow for creating spatial sound for 360film as well as a template for an ambisonic location sound report.
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.	An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.	On Hypergroups with a xcexb2Class of Finite Height In every hypergroup, the equivalence classes modulo the fundamental relation xcexb2 are the union of hyperproducts of element pairs. Making use of this property, we introduce the notion of height of a xcexb2 class and we analyze properties of hypergroups where the height of a xcexb2 class coincides with its cardinality. As a consequence, we obtain a new characterization of 1hypergroups. Moreover, we define a hierarchy of classes of hypergroups where at least one xcexb2 class has height 1 or cardinality 1, and we enumerate the elements in each class when the size of the hypergroups is n xe2x89xa4 4 , apart from isomorphisms.
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.	An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.	Boundary state feedback exponential stabilization for a onedimensional wave equation with velocity recirculation Abstract In this paper, we consider boundary state feedback stabilization of a onedimensional wave equation with indomain feedbackrecirculation of an intermediate point velocity. We firstly construct an auxiliary control system which has a nonlocal term of the displacement at the same intermediate point. Then by choosing a wellknown exponentially stable wave equation as its target system, we find one backstepping transformation from which a state feedback law for this auxiliary system is proposed. Finally, taking the resulting closedloop of the auxiliary system as a new target system, we obtain another backstepping transformation from which a boundary state feedback controller for the original system is designed. By the equivalence of three systems, the closedloop of original system is proved to be wellposed and exponentially stable. Some numerical simulations are presented to validate the theoretical results.
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.	An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.	Analysis of Charging Continuous Energy System and Stable Current Collection for Pantograph and Catenary of Pure Electric LHD Aiming at the problem of limited power battery capacity of pure electric LoadHaulDump (LHD), a method of charging and supplying sufficient power through pantographcatenary current collection system is proposed, which avoids the problem of poor flexibility and mobility of towed cable electric LHD. In this paper, we introduce the research and application status of pantograph and catenary, describe the latest methods and techniques for studying the dynamics of pantographcatenary system, elaborate and analyze various methods and technologies, and outline the important indicators for analyzing and evaluating the stability of current collection between pantographcatenary system. Simultaneously, various control strategies for pantographcatenary system are introduced. Finally, the application of the pantographcatenary system in highspeed railway and urban electric bus is discussed to illustrate the advantages of pantographcatenary system charging and energy supply, and it is applied to pure electric LHD charging and energy supply to ensure power adequacy.

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