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10.3390/ai5030079
|
Facial Recognition Using Hidden Markov Model and Convolutional Neural Network
|
Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost.
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26732688
|
AI
|
10.3389/frai.2024.1410790
|
Transparency and precision in the age of AI: evaluation of explainability-enhanced recommendation systems
|
In today’s information age, recommender systems have become an essential tool to filter and personalize the massive data flow to users. However, these systems’ increasing complexity and opaque nature have raised concerns about transparency and user trust. Lack of explainability in recommendations can lead to ill-informed decisions and decreased confidence in these advanced systems. Our study addresses this problem by integrating explainability techniques into recommendation systems to improve both the precision of the recommendations and their transparency. We implemented and evaluated recommendation models on the MovieLens and Amazon datasets, applying explainability methods like LIME and SHAP to disentangle the model decisions. The results indicated significant improvements in the precision of the recommendations, with a notable increase in the user’s ability to understand and trust the suggestions provided by the system. For example, we saw a 3% increase in recommendation precision when incorporating these explainability techniques, demonstrating their added value in performance and improving the user experience.
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26248212
|
AI
|
10.3389/frai.2024.1419638
|
Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification
|
Introduction: Deep learning (DL) has significantly advanced medical image classification. However, it often relies on transfer learning (TL) from models pretrained on large, generic non-medical image datasets like ImageNet. Conversely, medical images possess unique visual characteristics that such general models may not adequately capture.Methods: This study examines the effectiveness of modality-specific pretext learning strengthened by image denoising and deblurring in enhancing the classification of pediatric chest X-ray (CXR) images into those exhibiting no findings, i.e., normal lungs, or with cardiopulmonary disease manifestations. Specifically, we use a VGG-16-Sharp-U-Net architecture and leverage its encoder in conjunction with a classification head to distinguish normal from abnormal pediatric CXR findings. We benchmark this performance against the traditional TL approach, viz., the VGG-16 model pretrained only on ImageNet. Measures used for performance evaluation are balanced accuracy, sensitivity, specificity, F-score, Matthew’s Correlation Coefficient (MCC), Kappa statistic, and Youden’s index.Results: Our findings reveal that models developed from CXR modality-specific pretext encoders substantially outperform the ImageNet-only pretrained model, viz., Baseline, and achieve significantly higher sensitivity (p < 0.05) with marked improvements in balanced accuracy, F-score, MCC, Kappa statistic, and Youden’s index. A novel attention-based fuzzy ensemble of the pretext-learned models further improves performance across these metrics (Balanced accuracy: 0.6376; Sensitivity: 0.4991; F-score: 0.5102; MCC: 0.2783; Kappa: 0.2782, and Youden’s index:0.2751), compared to Baseline (Balanced accuracy: 0.5654; Sensitivity: 0.1983; F-score: 0.2977; MCC: 0.1998; Kappa: 0.1599, and Youden’s index:0.1327).Discussion: The superior results of CXR modality-specific pretext learning and their ensemble underscore its potential as a viable alternative to conventional ImageNet pretraining for medical image classification. Results from this study promote further exploration of medical modality-specific TL techniques in the development of DL models for various medical imaging applications.
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26248212
|
AI
|
10.3389/feduc.2024.1398477
|
Piloting puberty content books and a teacher training guide in Sierra Leone: a qualitative assessment
|
Introduction: Ensuring young people receive adequate information and guidance about puberty is essential for healthy adolescent transitions. Although many countries are moving toward including comprehensive sexuality education in national curricula, content on puberty during early adolescence, including peer pressure and stigma related to physical and emotional changes, are rarely included. Limited evidence exists about the inclusion of puberty education in schools, and the role of teachers in delivering such content in low-and middle-income countries, including Sierra Leone.Methods: We conducted a qualitative assessment using multiple methodologies (in-depth interviews with teachers; focus group discussions with girls and boys; key informant interviews with teacher training lecturers and government) to explore the feasibility and acceptability of a puberty education package (a teacher training guide and boys’ and girls’ puberty books) for primary school teachers to introduce puberty content in Sierra Leone.Results: Three key themes were identified, including the importance of teacher comfort in discussing puberty, the value of the teacher’s guide for delivering puberty content, and system and resource constraints that impact the implementation of puberty education. Additional insights included how integrating puberty education into existing curriculum courses may be more effective than stand-alone puberty classes; education systems can enable in-service and pre-service teacher training, along with culturally appropriate puberty resources, to increase effective puberty education delivery in schools; and governments serve a key role in providing puberty education teacher training, ensuring sustainable funding to retain trained teachers, and offering guidance on national curriculum requirements on puberty education.Discussion: There is a strong need to integrate puberty education into formal educational systems, with well trained teachers serving a valuable role in its delivery. Research is needed on how best to scale sustainable teacher training interventions to support the delivery of puberty education to adolescents in low- and middle-income contexts.
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2504284X
|
EDUCATION
|
10.1007/s00432-024-05930-z
|
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma
|
Purpose To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients. Methods 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test. Results The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724–0.871) in the training set and 0.785 (95% CI: 0.716–0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553–0.706; pathomics: 0.732, 95% CI: 0.662–0.802; clinical model: 0.655, 95% CI: 0.581–0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001). Conclusion The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.
|
14321335
|
ONCOLOGY
|
10.1007/s00432-024-05923-y
|
Paclitaxel hyperthermia suppresses gastric cancer migration through MiR-183-5p/PPP2CA/AKT/GSK3β/β-catenin axis
|
Background Gastric cancer (GC), a prevalent malignant tumor which is a leading cause of death from malignancy around the world. Peritoneal metastasis accounts for the major cause of mortality in patients with GC. Despite hyperthermia intraperitoneal chemotherapy (HIPEC) improves the therapeutic effect of GC, it’s equivocal about the mechanism under HIPEC. Methods MiR-183-5p expression was sifted from miRNA chip and detected in both GC patients and cell lines by qRT-PCR. Gene interference and rescue experiments were performed to identified biological function in vitro and vivo. Next, we affirmed PPP2CA as targeted of miR-183-5p by dual luciferase reporter assay. Finally, the potential relationship between HIPEC and miR-183-5p was explored. Results MiR-183-5p is up-regulated in GC and associated with advanced stage and poor prognosis. MiR-183-5p accelerate GC migration in vitro which is influenced by miR-183-5p/PPP2CA/AKT/GSK3β/β-catenin Axis. HIPEC exerts migration inhibition via attenuating miR-183-5p expression. Conclusion MiR-183-5p can be used as a potential HIPEC biomarker in patients with CC.
|
14321335
|
ONCOLOGY
|
10.3390/ai5030081
|
Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT for Classifying Focused Ultrasound-Related Articles
|
Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for various medical conditions. However, the exponential growth in the published literature on FUS therapies has made the literature review process increasingly time-consuming, inefficient, and error-prone. Machine learning approaches offer a promising solution to address these challenges. Therefore, the purpose of our study is to (1) explore and compare machine learning techniques for the text classification of scientific abstracts, and (2) integrate these machine learning techniques into the conventional literature review process. A classified dataset of 3588 scientific abstracts related and unrelated to FUS therapies sourced from the PubMed database was used to train various traditional machine learning and deep learning models. The fine-tuned Bio-ClinicalBERT (Bidirectional Encoder Representations from Transformers) model, which we named FusBERT, had comparatively optimal performance metrics with an accuracy of 0.91, a precision of 0.85, a recall of 0.99, and an F1 of 0.91. FusBERT was then successfully integrated into the literature review process. Ultimately, the integration of this model into the literature review pipeline will reduce the number of irrelevant manuscripts that the clinical team must screen, facilitating efficient access to emerging findings in the field.
|
26732688
|
AI
|
10.1186/s40359-024-01956-7
|
Prediction of accident-proneness among a sample of Iranian workers: usefulness of an adjusted version of the Health Belief Model with spiritual health
|
Workforce health is one of the primary and challenging issues, especially in industrialized countries. The purpose of the present study was to evaluate the ability to predict accident-proneness among Saveh Industry workers in Iran, based on an extended Health Belief Model, that included the construct of spiritual health. This descriptive-analytical study was conducted in 2022 on 384 workers in Saveh, Iran. The study aimed to explore relationships between accident proneness behavior, spiritual health, and health beliefs. The accident-proneness questionnaire consisted of two parts: the first part included demographic questions, and the second part comprised 9 sections covering personality traits, workplace harmful factors, miscellaneous factors, musculoskeletal disorders, safety culture, safety attitudes, job stress, organization interest, and degree of risk-taking. The Health Belief Model included 31 questions, while spiritual health was measured with the 20-question Paloutzian and Ellison questionnaire. The collected data were analyzed using SPSS version 26 software. In terms of accident proneness, 229 (59.6%), exhibited high levels, 148 (38.5%) had medium levels, and 7 (1.8%) showed low levels of accident-proneness. Hierarchical multiple regression analysis showed that in the first model, variables of perceived self-efficacy, vulnerability, and severity independently predicted workers accident proneness, explaining a total of 43% of variance in accident proneness behavior. In the second step, perceived self-efficacy (β = 34%), perceived sensitivity (β = 27%), spiritual health (β = 16%), and perceived severity (β = 12%) were included, respectively, which explained a total of 46% of the variance of accident-prone behavior of workers. Given the high rate of accident proneness observed in this study, there is a critical need for policymakers and health planners to design policies aimed at mitigating the risks associated with occupational accidents. Furthermore, the findings highlight the potential of integrating spiritual health into the Health Belief Model, as a conceptual framework for planning effective intervention programs to enhance workplace safety.
|
20507283
|
PSYCHOLOGY
|
10.3389/fpsyg.2024.1440013
|
Slovenian validation of the Capacity to Love Inventory: associations with clinical measures and mindfulness
|
Aim: The main purpose of the present study was to validate the Slovenian version of the 41- item Capacity to Love Inventory (CTL-I). Based on psychoanalytic theory, limitations to capacity to love are expected to be associated with personality dysfunction and disintegration as well as fundamental mental capacities such as self-reflection and self-awareness.Method: To examine these assumptions, a sample of 552 Slovenian non-clinical individuals were recruited through academic networks. The construct validity of the CTL-I was assessed using a confirmatory factor analysis and convergent validity of the CTL-I and its subscales was established against IPO-16, PID-5 BF, MAAS.Results: Our findings show that the Slovenian version of the CTL-I replicated the six-factor structure, exhibiting good model fit as well as satisfactory internal consistency of all subscales. In line with expectations, capacity to love was found to be inversely associated with dysfunctional personality traits and structural personality disturbances. Accordingly, higher dispositional mindfulness was coherently associated with all domains of CTL-I.Conclusion: The results add to the growing evidence for the cross-cultural validity and sound psychometric properties of CTL-I, presented here in the Slovenian version. Our findings also point to the significance of dispositional mindfulness both in relation to capacity to love as well as mental health.
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16641078
|
PSYCHOLOGY
|
10.1186/s40594-024-00498-z
|
The transfer effect of computational thinking (CT)-STEM: a systematic literature review and meta-analysis
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Background: Integrating computational thinking (CT) into STEM education has recently drawn significant attention, strengthened by the premise that CT and STEM are mutually reinforcing. Previous CT-STEM studies have examined theoretical interpretations, instructional strategies, and assessment targets. However, few have endeavored to delineate the transfer effects of CT-STEM on the development of cognitive and noncognitive benefits. Given this research gap, we conducted a systematic literature review and meta-analysis to provide deeper insights. Results: We analyzed results from 37 studies involving 7,832 students with 96 effect sizes. Our key findings include: (i) identification of 36 benefits; (ii) a moderate overall transfer effect, with moderate effects also observed for both near and far transfers; (iii) a stronger effect on cognitive benefits compared to noncognitive benefits, regardless of the transfer type; (iv) significant moderation by educational level, sample size, instructional strategies, and intervention duration on overall and near-transfer effects, with only educational level and sample size being significant moderators for far-transfer effects. Conclusions: This study analyzes the cognitive and noncognitive benefits arising from CT-STEM’s transfer effects, providing new insights to foster more effective STEM classroom teaching.
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21967822
|
EDUCATION
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10.1007/s44196-024-00641-2
|
Creating Personalized Higher Education Teaching System Using Fuzzy Association Rule Mining
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Universities and colleges aim to provide students with a solid academic foundation. Quality instruction is one strategy for achieving the highest possible standard in the higher education system. Personalized teaching caters to each student by adapting the learning pace and method to their specific requirements. However, the present state of customized education in higher education resources prevents proper resources from being extracted due to a lack of multi-dimensional association analysis between students, circumstances, and materials. A hybrid personalized teaching system utilizing fuzzy association rules mining is the goal of this research to improve learning in higher education. Effective multi-dimensional association analysis among students, settings, and instructional materials is facilitated by the fuzzy association rules mining-based hybrid personalized teaching system (FARM-HPT). The proposed study conforms to AI standards, is based on fuzzy logic theories, and guarantees precise university-level resource discovery. The study builds on earlier work in data mining by presenting a new, learner-specific recommendation model for personalized teaching that uses FARM to ensure accurate resource recognition and efficient mining of instructional assets at the higher education level. This new approach generates fewer set comparisons and does them faster than the current standard. Focusing on experimental validation, the study shows that the FARM-HPT system can generate individualized lessons while overcoming the constraints of traditional information mining methods. These findings align with AI standards, which shows how important it is to validate new AI approaches using robust empirical evidence. The system ensures effective accuracy on various datasets: LFW (89.76%), JAOLAD (94.43%), OECD (95.43%) and OULAD (97.45%).
|
18756883
|
AI
|
10.3389/feduc.2024.1466128
|
Multi-version interactive assessment through the integration of GeoGebra with Moodle
|
AI systems are now capable of providing accurate solutions to questions presented in text format, causing a major problem in assessment integrity. To address this issue, interactive material can be integrated with the questions, preventing current AI systems from processing the requirements. This study proposes a novel approach that combines two important tools: GeoGebra and Moodle. GeoGebra is a widely used tool in schools and universities for creating dynamic and interactive material in the STEM field. On the other hand, Moodle is a popular learning management system with integrated tools capable of generating multiple versions of the same question to enhance academic integrity. We combine these two tools to automatically create unique interactive questions for each student in a computer-based assessment. Detailed implementation steps that do not require prior coding experience or the installation of additional plugins are presented, making the technique accessible to a wider range of instructors. The proposed approach was tested on a group of students and showed enhanced performance in animation-based questions compared to traditional question formats. Moreover, a survey exploring the students’ opinions on the proposed approach reported strong student endorsement of animated questions.
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2504284X
|
EDUCATION
|
10.3389/feduc.2024.1447731
|
Relationship between cultural diversity awareness and achievement motivation of medical students at the undergraduate level in Pakistan
|
This study investigates the relationship between cultural diversity awareness and achievement-oriented goals among undergraduate medical students at the university level. Utilizing the Achievement Motivation Model by McInerney et al. (2003) and the General Fulfillment Aims Orientation Scale (GAGOS), it examines mastery, performance, and social goals. Additionally, it incorporates Ennejar's (2021) cultural diversity awareness model to assess students' attitudes toward cultural diversity. Data were collected from 80 final-year MBBS students through a survey and analyzed using SPSS for descriptive and inferential statistics. Results show that students have a high level of cultural diversity awareness and recognize biases, supporting diverse voices and cultural differences. A significant positive correlation (r = 0.948, p < 0.05) between cultural diversity awareness and achievement motivation was found, although no significant differences were observed based on gender or age. These findings suggest that enhancing personal development, altruism, and social recognition may boost motivation and that diversity and inclusion programs are crucial for fostering environments that promote achievement motivation.
|
2504284X
|
EDUCATION
|
10.3389/frai.2024.1401126
|
OLTW-TEC: online learning with sliding windows for text classifier ensembles
|
In the digital age, rapid dissemination of information has elevated the challenge of distinguishing between authentic news and disinformation. This challenge is particularly acute in regions experiencing geopolitical tensions, where information plays a pivotal role in shaping public perception and policy. The prevalence of disinformation in the Ukrainian-language information space, intensified by the hybrid war with russia, necessitates the development of sophisticated tools for its detection and mitigation. Our study introduces the “Online Learning with Sliding Windows for Text Classifier Ensembles” (OLTW-TEC) method, designed to address this urgent need. This research aims to develop and validate an advanced machine learning method capable of dynamically adapting to evolving disinformation tactics. The focus is on creating a highly accurate, flexible, and efficient system for detecting disinformation in Ukrainian-language texts. The OLTW-TEC method leverages an ensemble of classifiers combined with a sliding window technique to continuously update the model with the most recent data, enhancing its adaptability and accuracy over time. A unique dataset comprising both authentic and fake news items was used to evaluate the method’s performance. Advanced metrics, including precision, recall, and F1-score, facilitated a comprehensive analysis of its effectiveness. The OLTW-TEC method demonstrated exceptional performance, achieving a classification accuracy of 93%. The integration of the sliding window technique with a classifier ensemble significantly contributed to the system’s ability to accurately identify disinformation, making it a robust tool in the ongoing battle against fake news in the Ukrainian context. The application of the OLTW-TEC method highlights its potential as a versatile and effective solution for disinformation detection. Its adaptability to the specifics of the Ukrainian language and the dynamic nature of information warfare offers valuable insights into the development of similar tools for other languages and regions. OLTW-TEC represents a significant advancement in the detection of disinformation within the Ukrainian-language information space. Its development and successful implementation underscore the importance of innovative machine learning techniques in combating fake news, paving the way for further research and application in the field of digital information integrity.
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26248212
|
AI
|
10.3389/fpsyg.2024.1435691
|
Navigating virtual selves: validation of the German version of the presentation of online self scale
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The Presentation of Online Self Scale for Adults (POSSA), originally developed by Strimbu et al. is a well-regarded instrument for assessing online self-presentation. This study evaluated the factorial structure, reliability, and validity of the German adaptation of POSSA. A CFA analysis confirmed a satisfactory fit for the proposed three-factor model, as evidenced by a CFI of 0.919, a TLI of 0.902 and a RSMEA of 0.075. The subscales of the German POSSA demonstrated high internal consistency. Additionally, convergent validity was established through significant correlations with the Impostor-Profile 30 (IPP), affirming the interpretive accuracy of the subscale scores. Specifically, the Adaptable Self and Freedom of Self Online subscales positively correlated with IPP measures of Alienation and Other-Self-Divergence, whereas the Authentic Self subscale inversely correlated with these measures. Moreover, the German POSSA scores accounted for variance in the number of Instagram followers, surpassing the predictive power of self-esteem alone. Notably, the Adaptable Self factor was positively associated with the follower count, while the Freedom of Self Online factor displayed a negative association. Collectively, these findings underscore the DE-POSSA as a robust tool for assessing self-presentation behaviors in German-speaking populations and highlight its potential for cross-cultural research in online interpersonal interactions.
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16641078
|
PSYCHOLOGY
|
10.1186/s40359-024-01973-6
|
Development of Chinese college students’ perception teacher differential behavior scale and its reliability and validity test
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To compile a scale of Chinese college students’ perception of teachers’ differential behavior and to provide a reference for college students to establish correct life values, promote college students’ physical and mental health, and reduce teachers’ differential treatment. Open-ended questionnaires and expert interviews were used to conduct interviews and correspondence with 58 college students, ten psychologists, and six psychologists to form an initial questionnaire. Then, the scale’s exploratory factor analysis, confirmatory factor analysis, and reliability and validity test were conducted on 7053 college students from 18 universities in 6 provinces (municipalities directly under the Central Government). The Chinese college students’ perception of teachers’ differential behavior scale has two dimensions: teacher prejudice and preference. Each dimension includes three aspects: emotional feedback, behavior orientation, and opportunity privilege, and each aspect have a total of 4 items. The consistency test coefficients of each dimension and each factor of the prepared scale are all above 0.7, and the split-half reliability is above 0.6. Confirmatory factor analysis shows that the six-factor structural model fits well (χ2/df = 4.287, RMSEA = 0.066, CFI = 0.950, TLI = 0.919). Using the generalized anxiety disorder scale and the patient health questionaire-9items as empirical criteria, each factor in the scale demonstrated significant correlations with both the GAD scale and the patient health questionaire-9items. The Chinese college students’ perception of teachers’ differential behavior scale has a two-dimensional six-factor structure and has good reliability and validity. It can be used as an effective tool to measure Chinese college students’ perceived teacher differential behavior.
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20507283
|
PSYCHOLOGY
|
10.1186/s40359-024-01968-3
|
No significant difference in salivary cortisol response on the Trier Social Stress Test-Online based on coffee consumption habits
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Background: Coffee is widely consumed around the world. In Japan, it is a type of “Shikohin” (consumed for flavor, not nutrition). Several medical studies have reported the beneficial effects of coffee consumption, whereas others suggest that these beneficial effects on psychological aspects are marginal. The habit of consuming large amounts of caffeine through coffee may improve short-term resilience in stressful situations and may exhaust individuals in the long term. We hypothesized that people who habitually drink high amounts of coffee would have lower resilience scores and higher acute stress responses. Methods: Adult Japanese men completed a questionnaire that included a resilience scale and Shikohin consumption habits. Experimental participants were recruited from the survey respondents and classified into three groups based on their coffee consumption per day: No Coffee, Low Coffee, and High Coffee. All participants were asked to join the Trier Social Stress Test-Online (TSST-OL). Subjective stress and salivary cortisol concentrations was measured at eight time points during the experiment. There were 16 participants in each group for the analysis (mean age = 46.10 years, SD = 12.58). Results: Statistical analysis showed that both subjective stress and salivary cortisol concentrations significantly increased following TSST-OL exposure. However, there were no significant differences among the groups, and the hypotheses were not supported. Conclusions: This study demonstrated the effectiveness and stability of the TSST-OL. Additionally, coffee consumption habits were not significantly related to resilience scale scores or acute stress responses.
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20507283
|
PSYCHOLOGY
|
10.3389/feduc.2024.1447270
|
A systematic review of student learning outcomes in CLIL in LOTE
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Introduction: This paper aims to provide a first systematic research overview of student learning outcomes in programs teaching school subjects through languages other than English (LOTE) which are not the mother tongue of the students, according to school- or researcher-administered assessments and stakeholder perspectives, following the PRISMA statement. For brevity, we shall refer to these types of programs as CLIL in LOTE, though we have also included programs which use other labels, such as bilingual education or immersion, due to their similarities with those labeled “content and language integrated learning” (CLIL).Methods: The selected studies, published between November 1994 and December 2023, were identified through the search of SCOPUS and EBSCO. In determining which studies to include in the review, we employed the following selection criteria: (1) articles focusing on children and youth (ages 5–17 years), (2) articles focusing on CLIL programs in LOTE, (3) articles focusing on student achievement, (4) articles focusing on studies that have collected primary data, and (5) studies that used school−/researcher-administered assessments (objective) or self/ hetero-reported measures (subjective). The screening of titles, abstracts and keywords left a final sample of n = 29 scientific papers, which were then read exhaustively and assessed for methodological quality.Results: Most studies (26 of 29) addressed academic and/or linguistic outcomes, with some studies additionally addressing social/cultural outcomes, behavioral/affective outcomes, and/or (meta) cognitive outcomes. Of the learning outcomes reported, 25 (53%) were positive, five (11%) were negative, four (9%) were neutral, eight (17%) were mixed and four (9%) identified factors influencing outcomes.Discussion: Theoretically, the study contributes to establishing more general theories about the specific role of CLIL in LOTE in students’ learning. Empirically, the study outlines pathways for future research on CLIL in LOTE. In practice, the study presents challenges identified by stakeholders to suggest pathways forward in CLIL teaching/learning.Systematic review registration: Open Science Framework (OSF):
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2504284X
|
EDUCATION
|
10.1186/s40359-024-01989-y
|
Associations between motivational factors and burnout syndrome among elite skiers
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The present research investigated the association between a series of motivational factors and burnout syndrome among elite skiers at the contextual level within the Hierarchical Model of Intrinsic and Extrinsic Motivation (HMIEM). There are 352 subjects (258 males, 94 females, aged 18 to 25 years) across five skiing events from three sport universities in this study. Four psychological scales related to motivational factors and burnout syndrome were completed by subjects. Overall, the result showed that a task-involving climate had a positive relationship with basic psychological needs, eliciting a positive pathway to autonomous motivation, and thus negatively affecting burnout syndromes. On the other hand, an ego-involving climate had a negative relationship with basic psychological needs, eliciting a negative pathway to amotivation, and then positively affecting burnout syndromes. The results underscore the intricate associations between a variety of motivational factors and athletes’ burnout syndrome, supporting the need to incorporate burnout syndrome elements into the outcomes of HMIEM sequence.
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20507283
|
PSYCHOLOGY
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10.1186/s40359-024-01975-4
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Collaborative AI-enhanced digital mind-mapping as a tool for stimulating creative thinking in inclusive education for students with neurodevelopmental disorders
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Nowadays, inclusive education is becoming an increasingly important method in the education of people with various types of disabilities. This study is aimed at investigating the effectiveness of utilizing collaborative digital mind-mapping techniques in the practical work of students in inclusive educational groups, as well as examining how the use of AI-provided prompts influences the development of creative skills. The study involved 163 participants, among whom 28 had neurodevelopmental disorders. The application of the proposed methodology resulted in an improvement in the indicators of creative thinking as measured by the Torrance Figural Creativity Test, specifically in terms of Fluency, Originality, Elaboration, and overall creativity score; the observed increase was statistically significant according to the Wilcoxon signed-rank test (p = 0.05). This increase in indicators was observed both in students with neurodevelopmental disorders and in students without developmental disorders, with a notably stronger impact observed on students with neurodevelopmental disorders. Furthermore, a slightly higher effectiveness of the applied methodology was recorded when AI prompts were used for both categories of students. Students with neurodevelopmental disorders largely perceived the usefulness of the prompts they received subjectively. The present research may contribute to further study of various creativity development methodologies in inclusive education, as well as regarding the influence of AI utilization on creative skills. The obtained results can be utilized in the development of educational programs for students in higher education institutions that support inclusive forms of learning.
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20507283
|
PSYCHOLOGY
|
10.3390/educsci14091020
|
The Effects of Invented Spelling Instruction on Literacy Achievement and Writing Motivation
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Early writing performance strongly predicts long-term literacy performance. It follows that early underachievement in writing is highly correlated with early underachievement in reading. One strategy teachers and students can use to approach writing in the kindergarten classroom is invented spelling. Invented spelling is children’s spontaneous or self-directed attempts to represent words in print by matching sounds to known letters or phonics patterns. A quasi-experimental study was used to evaluate the impact of invented spelling on foundational literacy skills and writing motivation in 63 kindergarten students at a rural school in the Mid-South. The research questions focused on the impact of invented spelling instruction on a variety of literacy outcomes, including foundational skills, spelling, and motivation. The results indicate the significant main effects of invented spelling instruction on students’ invented spelling (p < 0.001), conventional spelling (p < 0.001), complex vocabulary use (p < 0.001, writing motivation (p = 0.040), and writing achievement (p < 0.001). Other outcomes as well as implications and future directions are reported. The invented spelling intervention encouraged low-stake risk taking when writing and removed barriers to writing entry. Allowing time and space for invented spellings means students can focus on communicating their ideas in print without being hindered by the expectation to conform to conventional spellings.
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22277102
|
EDUCATION
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10.3390/ai5030082
|
Probabilistic Ensemble Framework for Injury Narrative Classification
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In this research, we analyzed narratives from the National Electronic Injury Surveillance System (NEISS) dataset to predict the top two injury codes using a comparative study of ensemble machine learning (ML) models. Four ensemble models were evaluated: Random Forest (RF) combined with Logistic Regression (LR), K-Nearest Neighbor (KNN) paired with RF, LR combined with KNN, and a model integrating LR, RF, and KNN, all utilizing a probabilistic likelihood-based approach to improve decision-making across different classifiers. The combined KNN + LR ensemble achieved an accuracy of 90.47% for the top one prediction, while the KNN + RF + LR model excelled in predicting the top two injury codes with a very high accuracy of 99.50%. These results demonstrate the significant potential of ensemble models to enhance unstructured narrative classification accuracy, particularly in addressing underrepresented cases, and the potential of the proposed probabilistic ensemble framework ML models in improving decision-making in public health and safety, providing a foundation for future research in automated clinical narrative classification and predictive modeling, especially in scenarios with imbalanced data.
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26732688
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AI
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10.1007/s00432-024-05949-2
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Transarterial chemoembolization combined with sintilimab and lenvatinib for the treatment of unresectable hepatocellular carcinoma: a retrospective study
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Background The treatment of unresectable hepatocellular carcinoma (uHCC) challenging due to unfulfilled clinical requirements. Objective To evaluate the safety and efficacy of combining transarterial chemoembolization (TACE) with sintilimab and lenvatinib in the treatment of uHCC. Methods We retrospectively analyzed the data of patients with uHCC who were treated with a combination of TACE, sintilimab, and lenvatinib between May 2019 and December 2021 at the Chinese PLA General Hospital. Systemic treatment was started 1 week after TACE was performed. Sintilimab was administered intravenously at a dosage of 200 mg every three weeks, and lenvatinib was given orally at dosages of 8 mg or 12 mg daily, contingent upon the weight of the patients. The primary endpoint was the objective response rate (ORR) as per the mRECIST. Secondary endpoints were disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and treatment-related adverse events (tr-AEs). Results A total of 32 patients were enrolled in the study. Among them, 9 patients were classified as Barcelona Clinic Liver Cancer-B (BCLC-B), 23 patients were classified as BCLC-C, 14 patients diagnosed with portal vein tumors, and 12 patients were diagnosed with extra hepatic metastases. The ORR and DCR were 75% and 90.6% respectively, with 4 patients exhibiting (12.5%) complete response, 20 patients exhibiting (62.5%) partial response, 5 patients exhibiting (15.6%) stable disease, and 3 patients exhibiting (9.4%) progressive disease. With a median follow-up time of 19.6 months, the median PFS was 9.9 months, and the median OS was 33.3 months. A total of 31 patients experienced different degrees of tr-AEs, of which 2 were grade 3 tr-AEs. Conclusion The combination therapy of TACE, sintilimab, and lenvatinib demonstrates satisfactory efficacy in the treatment of uHCC with manageable tr-AEs.
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14321335
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ONCOLOGY
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10.3390/ejihpe14090170
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Implementing a Social Presence-Based Teaching Strategy in Online Lecture Learning
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Previous studies have focused on the design of video lectures to improve students’ social presence by enhancing instructor presence for learners in lecture-based online courses; however, there has been limited emphasis on the peer presence in which learning from video lectures takes place. This study’s first objective is to develop a social presence (SP)-based teaching strategy to design online learning activities aimed at improving students’ social presence by providing social clues about peer presence and encouraging peer communication. The second objective is to compare students’ social presence, social interaction, and academic performance from lecture-based online learning supported by either a conventional teaching strategy or an SP-based teaching strategy. Using a quasi-experiment, we selected 81 Chinese university students to participate in a ten-week online course. The participants were randomly assigned to either an experimental group (EG) (N = 43) or a control group (CG) (N = 38). This study revealed that the SP-based strategy enhanced EG members’ social presence in online learning and that EG members achieved better academic performance than CG members. A significant correlation was found between the EG members’ academic performance and their social presence. The researchers also identified more concentrated social network sociograms with more cohesive subgroups in the EG members’ online interactions. The results indicate the necessity of applying an SP-based teaching strategy in lecture-based online courses to promote students’ social presence, social interaction, and academic performance.
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22549625
|
PSYCHOLOGY
|
10.3390/ai5030084
|
Improving Distantly Supervised Relation Extraction with Multi-Level Noise Reduction
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Background: Distantly supervised relation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately. The former level of noise arises from the large proportion of irrelevant words within sentences, while noise at the latter level is caused by inaccurate relation labels for various sentences. Method: We propose a novel multi-level noise reduction neural network (MLNRNN) to tackle both issues by mitigating the impact of multi-level noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggregating the information of keywords. Next, we implement multi-objective multi-instance learning (MOMIL) to reduce the impact of incorrect labels in sentences by identifying the cluster of correctly labeled instances. Meanwhile, we leverage mislabeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Results: Comprehensive experimental results on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed state-of-the-art methods for distantly supervised relation extraction in almost all cases. Conclusions: The proposed MLNRNN effectively addresses both word- and sentence-level noise, providing a significant improvement in relation extraction performance under distant supervision.
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26732688
|
AI
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10.3389/frai.2024.1411838
|
Governing AI in Southeast Asia: ASEAN’s way forward
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Despite the rapid development of AI, ASEAN has not been able to devise a regional governance framework to address relevant existing and future challenges. This is concerning, considering the potential of AI to accelerate GDP among ASEAN member states in the coming years. This qualitative inquiry discusses AI governance in Southeast Asia in the past 5 years and what regulatory policies ASEAN can explore to better modulate its use among its member states. It considers the unique political landscape of the region, defined by the adoption of unique norms such as non-interference and priority over dialog, commonly termed the ASEAN Way. The following measures are concluded as potential regional governance frameworks: (1) Elevation of the topic’s importance in ASEAN’s intra and inter-regional forums to formulate collective regional agreements on AI, (2) adoption of AI governance measures in the field of education, specifically, reskilling and upskilling strategies to respond to future transformation of the working landscape, and (3) establishment of an ASEAN working group to bridge knowledge gaps among member states, caused by the disparity of AI-readiness in the region.
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26248212
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AI
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10.1186/s40594-024-00504-4
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The S in STEM: gender differences in science anxiety and its relations with science test performance-related variables
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STEM education has experienced significant growth due to its pivotal role in innovation and economic development. While cognitive factors like prior knowledge are known predictors of STEM success, non-cognitive factors, including attitudes and demographics, also play vital roles. However, there is a notable scarcity of research focusing on the "S" in STEM—science—compared to extensive studies in fields like mathematics. This study aims to address this gap by exploring gender differences in science test performance and related attitudes, providing insights into this under-researched aspect of STEM education. The effective sample comprised 1839 Estonian 12th-grade students who took a computer-assisted science test. The test consisted of tasks combining chemistry, physics, biology, and geography, and a post-test survey was also administered. Across the total sample, the results showed that test performance positively correlated with test-taking duration, effort, and test importance. Test performance was negatively correlated with perceived test difficulty. Interestingly, while general science anxiety was not associated with test performance, subject-specific anxiety, especially chemistry anxiety had a negative association with test performance. While there were no gender differences in test performance, female students scored consistently higher on all science anxiety measures, compared to male students. Furthermore, female students assessed the science test to be more difficult, and they also took more time to complete the test. The correlations in gender subsamples mirrored those observed in the total sample. The association between science test performance and test-related variables is nuanced: students might not necessarily have a “general” STEM anxiety but it may be associated with a specific subject. Moreover, the findings imply that although there are no gender differences in test performance, girls have a greater anxiety when it comes to natural sciences subjects. These findings indicate the need for investigating the origin of such anxieties, which do not seem to stem from aptitude.
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21967822
|
EDUCATION
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10.3389/feduc.2024.1389462
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Enhancing public dialogue about inclusion in school education: a citizens’ panel pilot
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Introduction: This paper reports on a small-scale Citizens’ Panel pilot project using deliberative democratic methods to produce policy ideas about inclusion in school education of young people with special educational needs and disabilities (SEN/D) in England. The project had two aims: (i) to obtain information about modifying a Citizens’ Panel process to enhance the effective participation of young people with SEN/D; and (ii) to generate more nuanced, grounded and integrated policy ideas about inclusion than can be found in recent English school education policy.Methods: The Citizens’ Panel was a two phase deliberative process. Phase 1 involved working with six young people with SEN/D and their parents/carers to shape the Citizens’ Panel agenda, and to obtain information about how they could participate and communicate their perspectives during the events. Phase 2 involved the delivery of the Citizens’ Panel itself, which comprised 28 people: the six young people from phase 1, plus four young people without SEN/D, 13 parents/carers, and five education professionals.Results: The process evaluation revealed the need for and impact of meticulous planning using a differentiated and strengths-based approach to design. While participants reported that taking part in the Citizens’ Panel was overall, a positive and worthwhile experience, the differentiated approach involved trade-offs that affected the experiences of some participants without SEN/D, though not detrimentally. The panel produced distinctive ideas about more inclusive schools, where almost all of the themes were about general school changes for everyone. Most general themes involved some specific SEN/D aspects, with only one theme being SEN/D specific. This paper illustrates how these ideas are more nuanced, grounded and integrated than those in current national policy.Discussion: This paper provides evidence of how deliberative approaches can be used within and between schools, groups of schools (e.g., multi academy trusts), local networks (including local authorities), as well as at the national level. Lessons drawn show how deliberative methods used by advocacy groups, protest movements and non-governmental organisations in support of more transformational change can be developed in ways that enable young people with SEN/D to participate and have their voices heard.
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2504284X
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EDUCATION
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10.3389/fonc.2024.1419310
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Associations of HALP score with serum prostate-specific antigen and mortality in middle-aged and elderly individuals without prostate cancer
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Background: The association between the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) score and serum prostate-specific antigen (PSA) and all-cause mortality remains underexplored. We aimed to investigate the relationship between HALP score and these outcomes among middle-aged and elderly individuals without prostate cancer (PCa).Methods: This cross-sectional study included participants aged 40 years and older from National Health and Nutrition Examination Survey (NHANES) 2001–2010. HALP score was calculated using the formula: HALP score = (Hemoglobin × Albumin × Lymphocytes)/Platelets. High PSA level was defined as a percentage free PSA (%fPSA) less than or equal to 25% and a total PSA (tPSA) level equal to or higher than 4.0 ng/mL. Mortality data were obtained through December 30, 2019 by linking to the National Death Index.Results: Among 7,334 participants, 6,826 were classified as having low PSA level, while 508 were categorized as having high PSA level. Logistic regression revealed lower odds of high PSA level with higher HALP quartiles (Ptrend<0.001). Among 508 participants with high PSA level, over a median follow-up period of 10.13 years (IQR: 5.42-13.17 years), a total of 268 all-cause deaths were recorded. Cox regression analysis showed that participants in the highest HALP quartile had the lowest risk of all-cause mortality (HR = 0.527, 95% CI: 0.368-0.754) in participants with high PSA level. Restricted cubic spline analysis indicated a non-linear and negative correlation between HALP score and all-cause mortality, with an inflection point at 43.98 (P for non-linearity = 0.009). Random survival forest analysis ranked HALP score as the most significant predictor for all-cause mortality.Conclusion: Our study highlights that the HALP score the HALP score is associated with high PSA level and all-cause mortality among middle-aged and elderly individuals without PCa. Further research is warranted to validate these findings and elucidate underlying mechanisms.
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2234943X
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ONCOLOGY
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10.3389/fpsyg.2024.1415448
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The relationship between physical activity and mental health of middle school students: the chain mediating role of negative emotions and self-efficacy
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Objective: To explore the relationship between mental health and physical activity (PA) in middle school students, and examining the roles of negative emotions and self-efficacy in the relationship.Methods: Data from 1,134 Chinese middle school students (50.2% females, 49.8% males; Mage = 15.18, SDage = 2.00) were collected using the Physical Activity Rating Scale (PARS-3), Positive and Negative Affect Scale (PANAS), General Self-Efficacy Scale (GSES), and Middle School Student Mental Health Scale (MSSMHS).Results: (1) There is a significant positive correlation between PA and mental health (r = 0.16, p < 0.01), and the direct path of PA on mental health is significant (t = 2.101, p < 0.01). (2) PA negatively predicts negative emotions (r = −0.12, p < 0.01), and is significantly positively correlated with self-efficacy (r = 0.24, p < 0.01). Negative emotions negatively predict self-efficacy (r = −0.23, p < 0.01) and mental health (r = −0.67, p < 0.01). Self-efficacy positively predicts mental health (r = 0.30, p < 0.01). (3) Negative emotions and self-efficacy play a significant mediating role between PA and mental health. The mediating effect includes three paths: PA → negative emotion → mental health (effect value: 0.130); PA → self-efficacy → mental health (effect size: 0.052); PA → negative emotions → self-efficacy → mental health (effect size: 0.006).Conclusion: PA among middle school students can indirectly affect mental health through negative emotions and self-efficacy. Middle school students should be encouraged to participate in PA to reduce their negative emotions and increase their self-efficacy, thus improving their mental health.
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16641078
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PSYCHOLOGY
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10.3389/frai.2024.1460217
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Towards enhanced creativity in fashion: integrating generative models with hybrid intelligence
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Introduction: This study explores the role and potential of large language models (LLMs) and generative intelligence in the fashion industry. These technologies are reshaping traditional methods of design, production, and retail, leading to innovation, product personalization, and enhanced customer interaction.Methods: Our research analyzes the current applications and limitations of LLMs in fashion, identifying challenges such as the need for better spatial understanding and design detail processing. We propose a hybrid intelligence approach to address these issues.Results: We find that while LLMs offer significant potential, their integration into fashion workflows requires improvements in understanding spatial parameters and creating tools for iterative design.Discussion: Future research should focus on overcoming these limitations and developing hybrid intelligence solutions to maximize the potential of LLMs in the fashion industry.
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26248212
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AI
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10.3389/frai.2024.1353873
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Image restoration in frequency space using complex-valued CNNs
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Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.
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26248212
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AI
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10.3389/frai.2024.1441205
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Anomaly detection via Gumbel Noise Score Matching
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We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e., the gradients of log likelihoods w.r.t. inputs, of continuously relaxed categorical distributions. We test our method on a suite of anomaly detection tabular datasets. GNSM achieves a consistently high performance across all experiments. We further demonstrate the flexibility of GNSM by applying it to image data where the model is tasked to detect poor segmentation predictions. Images ranked anomalous by GNSM show clear segmentation failures, with the anomaly scores strongly correlating with segmentation metrics computed on ground-truth. We outline the score matching training objective utilized by GNSM and provide an open-source implementation of our work.
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26248212
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AI
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10.3390/cancers16193271
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Genetic Analysis of Biopsy Tissues from Colorectal Tumors in Patients with Ulcerative Colitis
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Background/Objectives: Colorectal neoplasia developing from ulcerative colitis mucosa (CRNUC) can be divided into ulcerative colitis-associated neoplasia (UCAN) and non-UCAN; however, it is often difficult to distinguish UCAN from non-UCAN during a biopsy diagnosis. We investigated whether a genomic analysis could improve the diagnostic accuracy of UCAN using biopsy specimens. Methods: In step 1, 14 CRNUCs were used to examine whether the genomic landscape of biopsy and resection specimens matched. In step 2, we investigated the relationship between the genomic landscapes and the pathological diagnosis of 26 CRNUCs. The cancer genome was analyzed by deep sequencing using a custom panel of 27 genes found to be mutated in our previous CRNUC analysis. Results: In step 1, of the 27 candidate genes, 14 were mutated. The concordance rate of the pathogenic mutations in these 14 genes between the biopsy and resection specimens was 29% (4/14), while that of the pathogenic mutations in TP53 and KRAS was 79% (11/14). In step 2, the pathological diagnosis of biopsy specimens using only hematoxylin and eosin (HE) staining had a sensitivity of 33% and an accuracy of 38% for UCAN diagnosis. On the other hand, the combination of the HE pathology and p53 immunohistochemical staining had a sensitivity of 73% and an accuracy of 85% for UCAN diagnosis, while the combination of HE staining and a TP53 mutation had a sensitivity of 87% and an accuracy of 88% for UCAN diagnosis. Conclusions: An evaluation of TP53 mutations in biopsy specimens may be useful for diagnosing UCAN. However, further studies with larger sample sizes are required before this can be applied in clinical practice.
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20726694
|
ONCOLOGY
|
10.3390/cancers16193307
|
First-Line Use of Daratumumab in Patients with Multiple Myeloma Shows Delayed Neutrophil and Platelet Engraftment after Autologous Stem Cell Transplantation: Results from a Real-Life Single-Center Study
|
Background: This real-life study aimed to investigate the possible impact of D-VTd induction therapy on hematopoietic engraftment after autologous stem cell transplantation (auto-SCT). Methods: Sixty consecutive NDMM patients received four cycles of induction therapy with D-VTd. The conditioning regimen consisted of melphalan 200 mg/m2. These patients were compared with a historical control group of 80 patients who received four cycles of VTd as induction therapy. Results: The median days to reach neutrophil and platelet engraftment significantly differed between patients treated with D-VTd (11 and 13 days, respectively) and VTd (10 and 12 days). Univariate Cox analyses show that patients treated with D-VTd had a hazard ratio of neutrophil engraftment that was 42% significantly lower than those in the VTd arm (HR: 0.58, p = 0.002), and a multivariate model confirmed this result. Patients treated with D-VTd developed FN more frequently. Univariate and multivariate logistic regressions revealed an association between D-VTd and FN. Delayed engraftment did not correlate with more extended hospitalization. No patients died in the first six months after transplantation. Conclusions: Our real-life study showed that a four-drug induction therapy containing DARA does not impact transplant safety outcomes.
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20726694
|
ONCOLOGY
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10.3390/educsci14101063
|
Integration of AI Training in the Field of Higher Education in the Republic of Bulgaria: An Overview
|
The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future.
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22277102
|
EDUCATION
|
10.3390/ai5040088
|
Advancing Persistent Character Generation: Comparative Analysis of Fine-Tuning Techniques for Diffusion Models
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In the evolving field of artificial intelligence, fine-tuning diffusion models is crucial for generating contextually coherent digital characters across various media. This paper examines four advanced fine-tuning techniques: Low-Rank Adaptation (LoRA), DreamBooth, Hypernetworks, and Textual Inversion. Each technique enhances the specificity and consistency of character generation, expanding the applications of diffusion models in digital content creation. LoRA efficiently adapts models to new tasks with minimal adjustments, making it ideal for environments with limited computational resources. It excels in low VRAM contexts due to its targeted fine-tuning of low-rank matrices within cross-attention layers, enabling faster training and efficient parameter tweaking. DreamBooth generates highly detailed, subject-specific images but is computationally intensive and suited for robust hardware environments. Hypernetworks introduce auxiliary networks that dynamically adjust the model’s behavior, allowing for flexibility during inference and on-the-fly model switching. This adaptability, however, can result in slightly lower image quality. Textual Inversion embeds new concepts directly into the model’s embedding space, allowing for rapid adaptation to novel styles or concepts, but is less effective for precise character generation. This analysis shows that LoRA is the most efficient for producing high-quality outputs with minimal computational overhead. In contrast, DreamBooth excels in high-fidelity images at the cost of longer training. Hypernetworks provide adaptability with some tradeoffs in quality, while Textual Inversion serves as a lightweight option for style integration. These techniques collectively enhance the creative capabilities of diffusion models, delivering high-quality, contextually relevant outputs.
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26732688
|
AI
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10.3390/ai5040089
|
Aircraft Skin Damage Visual Testing System Using Lightweight Devices with YOLO: An Automated Real-Time Material Evaluation System
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Inspection and material evaluation are some of the critical factors to ensure the structural integrity and safety of an aircraft in the aviation industry. These inspections are carried out by trained personnel, and while effective, they are prone to human error, where even a minute error could result in a large-scale negative impact. Automated detection devices designed to improve the reliability of inspections could help the industry reduce the potential effects caused by human error. This study aims to develop a system that can automatically detect and identify defects on aircraft skin using relatively lightweight devices, including mobile phones and unmanned aerial vehicles (UAVs). The study combines an internet of things (IoT) network, allowing the results to be reviewed in real time, regardless of distance. The experimental results confirmed the effective recognition of defects with the mean average precision (mAP@0.5) at 0.853 for YOLOv9c for all classes. However, despite the effective detection, the test device (mobile phone) was prone to overheating, significantly reducing its performance. While there is still room for further enhancements, this study demonstrates the potential of introducing automated image detection technology to assist the inspection process in the aviation industry.
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26732688
|
AI
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10.1007/s00432-024-05958-1
|
Sequential therapy for extramedullary plasmacytoma of the palate: a rare case report with seven years of follow-up and literature review
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Background Extramedullary plasmacytoma (EMP) is a rare solitary malignancy that accounts for 3% of plasma cell neoplasms, and EMP with a primary occurrence in the palate is extremely uncommon. Owing to the long course of EMP and the limited available data on treatment outcomes, there are no definitive guidelines for its management, especially for high-risk patients who are more susceptible to early progression to multiple myeloma. Case presentation In this study, we review nine relevant studies and describe a 54-year-old woman who presented with an asymptomatic nonulcerative mass localized in the palate. After initial radical surgical resection of the lesion, the patient was definitively diagnosed with EMP with minimal plasmacytosis in the bone marrow, and adjuvant intensity-modulated radiation therapy with a minimum dose of 39.6 Gy was administrated in the surgical area. There was no evidence of local recurrence, nodal metastasis or progression to multiple myeloma (MM) during the seven-year follow-up period. Conclusion Given the atypical clinical features of palate EMP reported in the literature and the encouraging results of our patient, sequential therapy involving surgery and adjuvant radiotherapy for primary palatal lesions in high-risk EMP patients without nodal involvement could be an effective treatment modality.
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14321335
|
ONCOLOGY
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10.3390/ejihpe14100176
|
A Bio-Psycho-Social Approach to Understanding Optimism and Pessimism in Response to Stress
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Stress is widely known to have debilitating effects on physical health and mental wellbeing, particularly on one’s coping styles, personality traits, and outlook on life. Cumulative and chronic stress, which can serve as a triggering or aggravating factor for many pathological disorders if left unaddressed, has been linked to many life-threatening diseases. While many studies have looked at how optimism and pessimism are used as a form of coping mechanism, few have examined how different bio-psycho-social reactions to stress shape the level of optimism and pessimism. Using a sample of adult individuals aged 18 and older in the United States (n = 3361), this study addressed the following research questions: (1) What types of stress are predictive of optimism and pessimism? (2) Which responses to stress and coping mechanisms are most predictive of optimism and pessimism? (3) Do optimism and pessimism share the same stress-related risk and protective factors? Overall, this study found that while optimism and pessimism share conceptual similarities, they are not necessarily influenced by the same stress mechanisms. Stress, whether personal or financial, was associated with a negative outlook on life. This study showed that having good sleep quality and a lower number of psychological stress symptoms was linked to increasing optimism and reducing pessimism, while overeating or eating unhealthily was connected to both optimism and pessimism. Additionally, this study found that exercise/walking and emotional support mediated the effects of the responses to stress on the respondents’ level of optimism and pessimism.
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22549625
|
PSYCHOLOGY
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10.3390/educsci14101073
|
The Role of Growth Mindset on the Relationships between Students’ Perceptions of English Language Teachers’ Feedback and Their ESL Learning Performance
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The importance of growth mindset and teachers’ feedback has been widely recognised to improve the English language performance of students; however, the impact of growth mindset as a mediator is least explored. Therefore, the study aimed to empirically analyse the interrelationships between growth mindset and teachers’ feedback levels on secondary school students’ English as a Second Language (ESL) performance and to study the mediation effects of growth mindset in the relationships. The research model examined growth mindset along with four types of feedback. The levels of feedback include task, process, self-regulation and self-based feedback that teachers provide to improve the ESL performance of students. Survey questionnaires were administered to 301 secondary school students in Class 9 from two private schools in India. The data were analysed using PLS-SEM 4.0 software. The results indicated that the direct effect of feedback that emphasised process and self-regulation fosters a growth mindset in ESL students. Feedback levels focused on task, process, self-regulation, and growth mindset significantly impact ESL performance. Moreover, growth mindset mediated the relationships between process and self-regulation-focused feedback and ESL performance. However, no evidence supports the relationship between self-focused feedback, growth mindset, and ESL performance. The study concludes with implications and directions for future research.
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22277102
|
EDUCATION
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10.1186/s40359-024-02016-w
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Pursuing beauty: socio-cultural and labor-economic determinants of cosmetic surgery consideration among female college students in China
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Cosmetic surgery has a profound impact on health and other aspects. As a means of enhancing physical attractiveness, it is increasingly being considered by female college students in China. However, current knowledge about the determinants of cosmetic surgery consideration among Chinese female college students still needs to be improved due to the lack of systematic perspectives and large-scale representative data sets. This study aimed to contribute to the literature in these two aspects. We framed cosmetic surgery consideration as a function of two broad sets of determinants: socio-cultural and labor-economic. We used data from a large, nationally representative sample of female college students in China (N = 6658, mean age = 20.3 years). In terms of socio-cultural oriented factors, we found that family socioeconomic status, peers' cosmetic surgery practices, and media exposure were positively associated with the likelihood of considering cosmetic surgery. In terms of labor-economic oriented factors, we found that self-rated physical appearance, higher grades, and expected income after graduation were positively associated with a higher likelihood of considering cosmetic surgery. These findings suggest that the decision-making process for cosmetic surgery among Chinese female college students goes beyond personal factors and is significantly influenced by structural factors.
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20507283
|
PSYCHOLOGY
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10.3389/frai.2024.1393903
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Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow
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Introduction: Recent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT’s responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students.Methods: To explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses.Results: Our analysis revealed that ChatGPT’s answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans.Discussion: The findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.
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26248212
|
AI
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10.3389/fpsyg.2024.1427514
|
How depression and ADHD relate to exercise addiction: a cross-sectional study among frequent exercisers
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Background: To date, there are no official diagnostic criteria for the frequently reported phenomenon of exercise addiction. Therefore, the aim of the present study was to investigate how mental disorders, specifically depression and attention-deficit hyperactivity disorder (ADHD), are related to exercise addiction (EA).Methods: A total of 173 participants aged between 18 and 70 years, who reported exercising more than 10 h a week and continued to exercise despite injury or illness, answered questionnaires including the Exercise Dependence Scale, the Beck Depression Inventory, and the Homburger ADHD scale for adults. Multiple linear regression analyses were performed adjusting for relevant confounders (age, gender) and stepwise regression was used to identify which of the two mental disorders is the more influential predictor of EA.Results: Pearson correlation analysis showed that depressive symptoms [r (171) = 0.422, p < 0.00] and ADHD symptoms [r (171) = 0.308, p < 0.001] were positively correlated with EA symptoms. The relation between depressive symptoms and EA remained after adjusting for confounders in the regression model (B = 20.531; t(170) = 5.950; 95% CI [13.719, 27.343]; p < 0.001). Similarly, the positive link between ADHD symptoms and EA persisted after controlling for confounders (B = 15.507; t(170) = 3.771; 95% CI [7.389, 23.625]; p < 0.001). Additionally, a stepwise regression model identified that depressive symptoms are a stronger predictor for EA than ADHD symptoms.Conclusion: Depressive symptoms seem to be a stronger predictor for EA compared to ADHD symptoms in frequent exercisers. Although individuals with ADHD May exercise extensively, they might be less at risk for EA than individuals with depression. These results contribute to the complex characterization of the psychiatric profile of individuals with exercise addiction, and underline the need for further research elucidating the interplay between mental disorders and EA.
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16641078
|
PSYCHOLOGY
|
10.3389/feduc.2024.1354621
|
I’m not half and half: navigating being a “both” in discipline-based education research
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Introduction and methods: Through years of conversations, three discipline-based education researchers used a duoethnographic process to interrogate their own discipline-based education research (DBER) identities. We present a description of how these individuals navigate being a “both,” gathered through reflections, discussions, and deeper research to explore perspectives of our professional identities and what we perceive those identities look like to our peers, supervisors, and trainees.Results: Our own definitions and eventually realized identities as a “both” emerged through this research process. We envision that science faculty have multiple roles, demands, and identities; at the most basic level, they are “both” an educator and a researcher. In the unique case of discipline-based education research (i.e., scholars studying the teaching and learning of science often in science departments), some faculty find an overlap between complementary yet sometimes competing research agendas (i.e., biology research (BR) and discipline-based education research (DBER)), of which they do “both.”Discussion: This article has two key contributions. First, it articulates this side-glancing process of our navigation of being a DBER “both,” leveraging each of our unique perspectives and the literature. Second, it represents how such an exploration may be useful to other interdisciplinary researchers in understanding and embracing all parts of their identities.
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2504284X
|
EDUCATION
|
10.3389/frai.2024.1381290
|
Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages
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The data-hungry statistical machine translation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languages. This paper proposes a novel approach to integrate the best features of the NMT and SMT systems for improved translation performance of low-resource English–Tamil language pair. The suboptimal NMT model trained with the small parallel corpus translates the monolingual corpus and selects only the best translations, to retrain itself in the next iteration. The proposed method employs the SMT phrase-pair table to determine the best translations, based on the maximum match between the words of the phrase-pair dictionary and each of the individual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-integrated incremental training demonstrates a substantial difference in translation performance as compared to the existing approaches for incremental training. The model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence. Empirical findings prove that the proposed model with BLEU scores of 19.56 and 23.49 outperforms the baseline NMT with scores 11.06 and 17.06 for Eng-to-Tam and Tam-to-Eng translations, respectively. METEOR score evaluation further corroborates these results, proving the supremacy of the proposed model.
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26248212
|
AI
|
10.3389/fonc.2024.1448502
|
Long non-coding RNA FAM87A is associated with overall survival and promotes cell migration and invasion in gastric cancer
|
Background: The role of long non-coding RNAs (lncRNAs) in the invasion and metastasis of gastric cancer remains largely unclear.Methods: Integrating transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes were identified in gastric cancer. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) database-curated gene set, lncRNAs associated with invasion and metastasis were identified. The Cox analyses were performed to identify prognostic lncRNAs. The competing endogenous RNA (ceRNA) regulation network was constructed to identify hub lncRNAs in gastric cancer. Functional and pathway analyses were used to investigate the function of identified lncRNAs. RT-qPCR and Transwell assays were used to investigate the expression in gastric cancer tissues and functions in gastric cancer cell lines.Results: Based on GEO and TCGA databases, 111 differentially expressed lncRNAs were identified between gastric cancer and normal samples. A total of 43 lncRNAs were significantly correlated with hallmark genes of cancer invasion and metastasis. Among them, as a hub lncRNA in the invasion-related ceRNA regulation network, FAM87A showed potential regulation on MAPK signaling and transforming growth factor (TGF) signaling cascade, such as TGFB2, TGFBR1, and TGFBR2. Furthermore, FAM87A also showed a significant correlation with cell adhesion molecules, such as Integrin alpha 6 (ITGA6) and Contactin-1 (CNTN1). RT-qPCR experiments showed that FAM87A expression was upregulated in gastric cancer tissues compared to normal samples (n = 30). Transwell assays showed that FAM87A knockdown inhibited the migration and invasion abilities of gastric cancer cells in vitro. Notably, clinical data analysis showed that lncRNA FAM87A could be an independent factor for the overall survival of patients with gastric cancer.Conclusion: LncRNA FAM87A may play a pivotal role in regulating migration and invasion of gastric cancer cells. FAM87A could be a potential biomarker for the overall survival of patients with gastric cancer.
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2234943X
|
ONCOLOGY
|
10.3389/fonc.2024.1477610
|
The role of extracellular vesicles in the pathogenesis of gynecological cancer
|
Gynecological cancer, the most common form of cancers in women worldwide, initiates in the reproductive organs of females. More often, the common treatment measures, i.e. surgery, radiation, and medical oncology are found to be unsuccessful in the treatment of gynecological tumors. Emerging evidence indicates that extracellular vesicles (EVs) play a significant role in the pathogenesis of gynecological cancers by distinct mechanisms. The present review highlights how EVs contribute to the progression of different types of gynecological cancers such as cervical cancer, endometrial cancer, ovarian cancer, vaginal cancer, uterine sarcoma, gestational trophoblastic disease (GTD), and vulvar cancer. The primary focus is to understand how EVs’ cargo alters the phenotypic response of the recipient cells, thereby contributing to the progression of the disease, thus can be considered as a prognostic and diagnostic biomarker. A brief discussion on the role of EVs in the diagnosis and prognosis of different gynecological cancer types is also highlighted. Targeting the biogenesis of the EVs, their inside cargo, and EVs uptake by the recipient cells could be a potential therapeutic approach in the treatment of gynecological cancer beside conventional therapeutic means.
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2234943X
|
ONCOLOGY
|
10.3389/fonc.2024.1414900
|
PD-1 expression in tumor infiltrating lymphocytes as a prognostic marker in early-stage non-small cell lung cancer
|
Introduction: Programmed death ligand – 1 (PD-L1) expression is a well-established predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). Programmed death – 1 (PD-1) serves as the target protein to PD-L1 and their interaction serves as a crucial pathway for immune evasion. This study aimed to investigate the expression pattern of PD-1 on Tumor-infiltrating lymphocytes (TILs) in early-stage NSCLC, and its potential role as prognostic biomarker.Materials & methods: PD-1 was evaluated in 474 surgical resected early-stage NSCLC specimens, using Tissue microarray and immunohistochemical staining. Expression was scored as negative (<1%) or positive. Positive PD-1 expression was further divided into low (<10%) and high (≥10%). None of the patients had received treatment with PD-1/PD-L1 inhibitors.Results: PD-1 expression ≥1% in TILs was observed in 83.5% of cases and was associated with pT stage (p=0.02), grade 3 (p=0.004), and adenocarcinoma subtype (p=0.05). Individuals with high PD-1 expression (≥10%) experienced reduced 10-year overall survival (Log-Rank test = 0.005). In addition, high PD-1 expression emerged as an independent factor associated with reduced survival on multivariate analysis (HR: 1.328 (95% CI: 1.074-1.641).Conclusions: Patients with early-stage NSCLC who exhibited PD-1 expression of ≥10% on TILs had an unfavorable 10-year OS rate. These findings indicate that elevated PD-1 expression on TILs can be associated with immune evasion during the early stages of malignancy evolution in the NSCLC setting and further research is required to further delineate the role of PD-1/PD-L1 pathway on tumor immune senescence. These results underline the potential role of PD-1/PD-L1 inhibitors in the treatment of early-stage NSCLC.
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2234943X
|
ONCOLOGY
|
10.1186/s40594-024-00507-1
|
Enhancing mathematical problem posing competence: a meta-analysis of intervention studies
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Mathematical problem posing, generally defined as the process of interpreting given situations and formulating meaningful mathematical problems, is academically important, and thus several interventions have been used to enhance this competence among students and teachers. Yet little is known about the interventions’ various components and their relative or combined effectiveness. In this meta-analysis of 26 intervention studies in mathematics, we identified nine intervention components and found that the interventions had a medium, positive, and significant mean weighted effect size. A stepwise meta-regression analysis revealed that intervention efficacy varied by moderators relevant to the research design, sample characteristics, and intervention characteristics. The findings obtained from this meta-analysis are expected to serve as a foundation for future efforts to design and implement (more) effective interventions to improve mathematical problem posing competence.
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21967822
|
EDUCATION
|
10.1186/s40594-024-00508-0
|
“Not a cookie cutter situation”: how neurodivergent students experience group work in their STEM courses
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Although group work is increasingly used in STEM courses and may lead to improved academic outcomes, there is evidence that some implementations of group work may lead to unintended barriers for certain students’ learning. Despite the growing number of neurodivergent undergraduate students, there is limited research on neurodivergent students’ experiences with group work in STEM courses. To address this knowledge gap, the current research investigated the experiences of 22 neurodivergent undergraduate students with group work in STEM courses at a range of institution types and in a variety of STEM disciplines. Participants shared experiences with in-class and out-of-class group work assignments for lecture and laboratory courses. Through inductive thematic coding of semi-structured interview transcripts, we identified seven themes impacting participants’ experiences. Three themes were individual level: personal characteristics that participants associated with their neurodivergence; strategies for academic success (with subthemes of organization/time management, adaptive communication, and self-advocacy); and beliefs on group work’s value. Four themes were group level/classroom level: group dynamics; role in group (including leadership roles); the competitive culture within STEM; and recommendations for instructors. Through a social-relational perspective on disability, we proposed a model showcasing how group and classroom factors serve as supports or barriers to neurodivergent students’ full participation in group work, as well as to their sense of belonging. Using the seven themes we articulated, we outlined a set of practices for designing group work assignments. In addition, we propose how pairing inclusive assignment design with instructor reflection and articulating anti-ableist values can support neurodivergent student belonging by disrupting discourses of normalcy in STEM. As one of the first studies exploring the impact that group work in STEM courses has on neurodivergent undergraduates, this work may inform reimaginations of group work practices to better address the needs of neurodivergent STEM students and support a more inclusive culture in STEM classrooms. In addition, our conceptual model may serve as the basis for future research regarding interactions between individual-level and group-level factors associated with neurodivergent students’ learning through group work and other active learning practices.
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21967822
|
EDUCATION
|
10.3389/feduc.2024.1455669
|
The mediating role of meaning in work in promoting teachers’ technology integration
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Teachers’ integration of technology has been a critical focus for both teachers and researchers over the past three decades. This emphasis has intensified due to the COVID-19 pandemic, where technology integration has become a key factor in the success of classroom teaching and learning processes. Despite this attention, previous studies have shown limited exploration of the relationship between teachers’ technology integration and meaning in work as an internal variable. Therefore, using AMOS-structural equation modeling (SEM) analysis, this study aimed to develop a conceptual model examining the mediating role of meaning in work in the relationship between digital leadership, self-efficacy, and teachers’ technology integration. The study involved 200 junior high school teachers from Balikpapan City, East Kalimantan Province, a region in eastern Indonesia projected to become the new capital. A total of four variables were analyzed in this study: meaning in work, digital leadership, self-efficacy, and teachers’ technology integration (Z, X, and Y, respectively). The results showed that (1) digital leadership affected meaning in work and teachers’ technology integration, (2) self-efficacy affected meaning in work and teachers’ technology integration, (3) meaning in work affected teachers’ technology integration, and (4) meaning in work could mediate the relationship between digital leadership and self-efficacy in teachers’ technology integration. These findings contribute to a deeper understanding of the relationships among digital leadership, self-efficacy, and meaning in work, and their collective impact on teachers’ technology integration. Furthermore, the study highlights the significant role of meaning in work as a mediator in these relationships, providing a foundation for the development of digital leadership strategies and training programs aimed at improving technology integration in education.
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2504284X
|
EDUCATION
|
10.1007/s44196-024-00646-x
|
Bayesian Optimization with Additive Kernels for a Stepwise Calibration of Simulation Models for Cost-Effectiveness Analysis
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A critical aspect of simulation models used in cost-effectiveness analysis lies in accurately representing the natural history of diseases, requiring parameters such as probabilities and disease burden rates. While most of these parameters can be sourced from scientific literature, they often require calibration to align with the model’s expected outcomes. Traditional optimization methods can be time-consuming and computationally expensive, as they often rely on simplistic heuristics that may not ensure feasible solutions. In this study, we explore using Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting structural properties within the solution space. Specifically, we investigate the impact of additive kernel decomposition and a stepwise approach, which capitalizes on the sequential block structure inherent in simulation models. This approach breaks down large optimization problems into smaller ones without compromising solution quality. In some instances, parameters obtained using this methodology may exhibit less error than those derived from naive calibration techniques. We compare this approach with two state-of-the-art high-dimensional Bayesian Optimization techniques: SAASBO and BAxUS. Our findings demonstrate that Bayesian optimization significantly enhances the calibration process, resulting in faster convergence and improved solutions, particularly for larger simulation models. This improvement is most pronounced when combined with a stepwise calibration methodology.
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18756883
|
AI
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10.3389/fonc.2024.1428802
|
Metabolic modulation of melanoma enhances the therapeutic potential of immune checkpoint inhibitors
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Introduction: Lactate is a pivotal molecule with diverse functions in the metabolic reprogramming of cancer cells. Beyond its role in metabolism, lactate exerts a modulatory effect within the tumor microenvironment; it is utilized by stromal cells and has been implicated in the suppression of the immune response against the tumor.Methods: Using in vitro assays (including flow cytometry, live-cell imaging and metabolic analyses), the impact of lactate dehydrogenase inhibitors (LDHIs) on melanoma cells were assessed. The therapeutic potential of LDHIs with immune checkpoint inhibitors (ICIs) were tested in vivo in murine models of melanoma tumors.Results: A potent anti-proliferative effect (via both cell cycle alterations and enhanced apoptosis) of LDHIs, Oxamate (Oxa) and methyl 1-hydroxy-6-phenyl-4-(trifluoromethyl)-1H-indole-2-carboxylate (NHI-2), was found upon treatment of melanoma cell lines. Using a combination of Oxa and NHI-2, a synergistic effect to inhibit proliferation, glycolysis, and ATP production was observed. Metabolic analysis revealed significant alteration in glycolysis and oxidative phosphorylation, while metabolite profiling emphasized consequential effects on lactate metabolism and induced energy depletion by LDHIs. Detection of increased RANTES and MCP-1, with Oxa and NHI-2 treatment, prompted the consideration of combining LDHIs with ICIs. In vivo studies using a murine B78 melanoma tumor model revealed a significant improvement in treatment efficacy when LDHIs were combined with ICIs.Conclusions: These findings propose the potential of targeting lactate metabolism to enhance the efficacy of ICI treatments in patients with melanoma.
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2234943X
|
ONCOLOGY
|
10.3389/fpsyg.2024.1466905
|
Validation and psychometric evaluation of the French version of the recovery experience questionnaire: internal consistency and validity assessment
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Background: Entrepreneurs often experience high levels of stress, anxiety, and burnout due to the demanding nature of their professional activities. Therefore, recovery from work-related stress is a relevant activity for entrepreneurs. The Recovery Experience Questionnaire (REQ) is a widely used 16-item self-reported measure covering four recovery factors: psychological detachment from work, relaxation, mastery, and control. The present study addresses the validation of a French version of the REQ.Methods: A total of 1,043 French entrepreneurs from various sectors participated in this study. Internal consistency and correlations were examined to assess the psychometric properties of the French version of the REQ. Confirmatory factor analysis (CFA) was used to validate the four-factor structure of the REQ, with seven error covariances added to improve model fit.Results: The French version of the REQ demonstrated good internal consistency (psychological detachment: α = 0.88, relaxation: α = 0.91, mastery: α = 0.90, control: α = 0.91). CFA supported that the four-factor structure was confirmed based on the following data: RMSEA = 0.071 (95% CI [0.066, 0.077]), CFI/TLI = 0.955/0.950, SRMR = 0.050, and χ2 (108) = 593.861, p < 0.001. Significant correlations were found between REQ scores and health indicators such as stress, loneliness, physical health, mental health, and sleep quality. The results confirm that the REQ is a valid and reliable measure for assessing recovery experiences among French entrepreneurs.Conclusion: We conclude that the REQ is a valid measure and a useful tool for research on entrepreneurs’ general health. Additionally, the validated French version of the REQ can be applied to other working populations, making it a versatile instrument for evaluating health and recovery in diverse occupational settings. To support this claim, we conducted the same validation analysis on a sample of 1,231 French agricultural employees, again showing that REQ is a valid and reliable measure for assessing recovery experiences.
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16641078
|
PSYCHOLOGY
|
10.3389/fonc.2024.1462997
|
The value of adjusted PSAD in prostate cancer detection in the Chinese population
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Objective: To investigate the value of adjusted prostate-specific antigen density (PSADadj) in the diagnosis of prostate cancer (PCa).Methods: Data from 410 patients who underwent transrectal ultrasound-guided prostate biopsy were retrospectively analyzed in Beijing Tsinghua Changgung Hospital between November 2014 and March 2024. All patients were divided into PCa and benign prostatic hyperplasia (BPH) groups according to pathological results. Multivariate logistic regression analyses were performed to evaluate the odd ratios (ORs) of predictors for PCa occurrence. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) values were used to assess and compare the diagnostic accuracies of total PSA (tPSA), free-to-total (f/t) PSA, free PSA (fPSA), PSAD, and PSADadj (PSAD×weight).Results: There were 166 patients in the PCa group and 244 in the BPH group. Multivariate analyses demonstrated that PSAD was positively correlated with the presence of PCa, with the highest OR value among all PSA-related parameters (OR = 19.075, p<0.001). tPSA, fPSAD, PSAD, and PSADadj had high accuracy in predicting PCa, with AUC values of 0.633, 0.730, 0.778, and 0.780. Of note, PSADadj had the highest AUC with a sensitivity of 63.3% and specificity of 81.6%. Similarly, in patients with a PSA level in the gray zone, the diagnostic accuracy of PSADadj in predicting PCa (AUC, 0.709; 95% CI, 0.616–0.802) remained better than other PSA-related markers.Conclusion: PSADadj has an advantage over other PSA-related markers in detecting PCa and could be used for making biopsy decisions.
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2234943X
|
ONCOLOGY
|
10.3389/feduc.2024.1393070
|
Evaluating technology breaks on cell phone use in a college classroom
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Cell phones in the college classroom can be used to increase interaction between students and the professor; they can also distract from academic tasks and decrease academic performance. To decrease task-switching in the classroom, researchers have suggested the use of “technology breaks” (TB), in which students are provided periodic breaks to use cell phones throughout class. The purpose of the present study was to evaluate the use of technology breaks in a college classroom (N = 21). Cell phone use was evaluated over 22 class periods. Observers recorded how many students were using cell phones every 10 s. Three experiment conditions were alternated with yoked controls in a multi-element design: (A) 1 min technology breaks, (B) 2 min technology break, and (C) 4 min technology break. The control condition [question breaks, (QB)] provided breaks for students to ask the professor questions regarding class materials. No penalties or punishers were delivered for cell phone use under any conditions. The average rate of cellphone use in QB was 0.53 responses per min (range = 0.06–1.02), while the average rate for TB was 0.35 responses per min (range = 0.20–0.74). Overall, the study found that technology breaks were a promising way to utilize reinforcement-based strategies to reduce classroom cell phone use, though variability in the data weakened conclusions regarding the utility of technology breaks.
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2504284X
|
EDUCATION
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10.3389/frai.2024.1374162
|
Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing
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Introduction: Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision-making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which have been labeled automatically (self-supervised mode) and tend to overfit.Methods: In this study, we investigate the quality of uncertainty estimates from a range of current state-of-the-art predictive models applied to the problem of observation detection in radiology reports. This problem remains understudied for Natural Language Processing in the healthcare domain.Results: We demonstrate that Gaussian Processes (GPs) provide superior performance in quantifying the risks of three uncertainty labels based on the negative log predictive probability (NLPP) evaluation metric and mean maximum predicted confidence levels (MMPCL), whilst retaining strong predictive performance.Discussion: Our conclusions highlight the utility of probabilistic models applied to “noisy” labels and that similar methods could provide utility for Natural Language Processing (NLP) based automated labeling tasks.
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26248212
|
AI
|
10.1186/s40359-024-02019-7
|
The associations between self-rated autistic traits, social camouflaging, and mental health outcomes in Taiwanese anime, comics and games (ACG) doujin creators: an exploratory study
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Doujin (どうじん) is a Japanese term referring to a circle where people share the same interests, usually something that belongs to the Anime, Comics, and Games (ACG) subculture. Individuals who belong to it and create related works, known as ACG doujin creators, are usually described as socially awkward and at potential risk of isolation. In such a context, they may theoretically exhibit higher autistic traits and manifest camouflaging tendencies, which may consequently be associated with their mental health. Nonetheless, the impact of autistic traits and camouflaging on mental health in this subculture remains significantly underexplored. We recruited 183 Taiwanese ACG doujin creators (age ranges from 18 to 41, 146 female and 37 male) via social networking platforms. Participants completed Chinese online surveys assessing socio-demographic information, doujin activities, past psychiatric history, the 35-item Version of Autism-Spectrum Quotient (AQ-35), Chinese version Camouflaging Autistic Traits Questionnaire (CAT-Q-Ch), the General Anxiety Disorder-7 (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9). Linear regression analysis was employed to examine the associations between the aforementioned scales. Our findings revealed that among ACG doujin creators, descriptively higher level of AQ-35 and CAT-Q-Ch than previous studies were found. Moreover, we observed a positive association between camouflaging behaviours and most AQ-35 subscales, with the exception of the mindreading subscale. Additionally, we identified that both camouflaging and autistic traits were significantly linked to higher PHQ-9 and GAD-7 scores. Through this study, we gained insight into the distinctive characteristics of autistic traits, camouflaging behaviours, and mental health among Taiwanese ACG doujin creators, as the associations between the factors mentioned above are divergent compared to previous research. This topic demonstrated that camouflaging is also associated with adverse mental health in a subculture group.
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20507283
|
PSYCHOLOGY
|
10.1186/s40594-024-00509-z
|
Synergistic effects of students’ mathematics and science motivational beliefs on achievement, and their determinants
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Background: Students’ mathematics and science motivational beliefs are crucial determinants of their school academic achievement in math and science. The current study aimed to identify the group memberships of students’ motivational beliefs in math and science, which are closely related. Furthermore, this study probed the predictive effects of individual students’ experiences at school on forming group membership. We also tested the mean differences of the identified latent groups in math and science achievement. Results: Using latent profile analysis modeling, we examined data from 3857 Korean eighth-grade students participating in the 2019 Trends in International Mathematics and Science Study. The theoretical rationale and supplementary statistical indices showed a five-group membership as the optimal solution. The five groups are high motivation, medium motivation, low math/high science motivation, low motivation, and very low motivation. Students’ sense of school belonging was the most crucial predictor in forming group membership, whereas perceived student bullying did not predict group membership. Finally, students in distinct motivational belief groups exhibited differences in their math and science achievements. Conclusions: This study identified five subgroups of students based on their distinct motivational beliefs in math and science, and variations in their association with achievements. In terms of policy development and intervention, it is important to nurture students’ sense of school belonging. This study advances motivational theories in science, technology, engineering, and mathematics education, and provides practical suggestions for improving educational practices to enhance student math and science motivational beliefs.
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21967822
|
EDUCATION
|
10.1186/s40359-024-02025-9
|
Psychosocial impacts of post-disaster compensation processes: narrative systematic review
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After disasters, many people seek compensation for physical, psychological or economic damages. However, compensation processes can be perceived as arduous and unfair and potentially create stress for both individuals and communities. This systematic review explored the psychosocial impacts of post-disaster compensation processes, including compensation sought through both litigation and government assistance programmes. We searched seven databases, hand-searched reference lists of included studies, and used thematic analysis to synthesise results of included studies. We screened 6,532 papers, ultimately including 66 in the review. While we found mixed evidence regarding the relationship between individual mental health and the compensation process, many studies suggested the process placed demands on emotional resources and could cause stress. Numerous challenges of the compensation process were described, including complicated paperwork, lengthy processes, inadequate information, confusing eligibility criteria, lack of inter-agency cooperation, poor understanding of communities’ unique needs, insufficient pay-outs, and politicisation of the process. Inequities in compensation distribution introduced additional stress to already traumatised communities, who often experienced resentment, envy and conflict. The mixed nature of the relationship between mental health and the compensation process was evident in research trends where a small number of studies reported positive findings related to relating to gratitude, helpfulness of compensation and strengthened community relationships, while a substantial number of others reported negative impacts including higher mental health problems. Positive and negative impacts were reported for both litigation and non-litigation compensation-seeking. The nuanced dynamics of these findings are described in greater detail within the paper. It is important that compensation regulators consider the potential impacts on individuals and communities and take steps to address compensation inequities. This enhanced understanding of how those affected by disasters can rebuild their lives and furthering understanding of how to support them will enable evidence-based approaches to building resilience and planning for long-term recovery. Significant compensation process improvements could be realised by ensuring clear communication and transparent decision-making. Overall, this review underscores the importance of ensuring that compensation processes are fair and straightforward so they can repair material losses without deteriorating the social norms and relationships of affected communities.
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20507283
|
PSYCHOLOGY
|
10.3389/feduc.2024.1438322
|
Predictor of low academic achievement among Dilla university students, southern Ethiopia, 2024
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Introduction: In Ethiopia, despite its growing higher education sector, student achievement rates remain concerningly low. Understanding the multifaceted factors influencing academic performance is crucial for improving educational equity and quality. This study delves into potential predictors of academic achievement among Ethiopian higher education students, examining individual characteristics, institutional elements, and broader socioeconomic influences.Methodology: This survey enrolled 362 respondents and was conducted from December 7, 2023 till January 22, 2024. Simple random sampling, validated assessment tools and online data collection methods were employed to select and collect information from respondents. Data entry and analysis was done using Epi-info 7.0 and SPSS 25, respectively. Logistic regression analysis method was used to determine the association between the outcome and independent variable.Result: The current results show that 166 (45/9%) of participants have GPAs below 3.18. Gender, social sciences/humanities or business/economics majors, suboptimal class environments, inadequate laboratory facilities, chronic illness, class sizes, low emotional coping skills, poor academic self-perception, and high social media use emerged as significant predictors of low academic achievement.Conclusion: This study identified factors associated with academic achievement. Female students, optimal learning environments, and smaller class sizes were linked to better performance, while social sciences/humanities or business/economics, inadequate facilities, and high social media use increased the risk of low achievement. Personal characteristics like emotional coping, self-perception, and chronic illness also played a role. These findings suggest interventions targeting individual and environmental factors could improve student outcomes.
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2504284X
|
EDUCATION
|
10.3390/educsci14101094
|
Influence and Relationship of Physical Activity before, during and after the School Day on Bullying and Cyberbullying in Young People: A Systematic Review
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The aim of this systematic review was to analyze the influence of the practice of Physical Activity (PA) before, during and after school hours on bullying and cyberbullying in children and adolescents. Studies were identified in four databases (PubMed, SCOPUS, Web of Science, ERIC) from January 2013 to March 2024. A total of 29 studies met the inclusion criteria. Seventeen studies used a cross-sectional design to explore the association between these variables, and 12 articles had a longitudinal design with PA interventions. The review found that PA is associated with significant improvements in bullying and cyberbullying, reduced depressive symptoms, and strengthened social relationships, responsibility, and self-esteem. PA before the school day may be effective in reducing bullying victimization. During the school day, it promotes affective behaviors related to bullying, such as empathy and respect for others, and optimizes psychological factors such as self-concept and self-esteem. After-school PA reduces bullying and disruptive behaviors in non-educational contexts. It is recommended to implement PA programs that address social, emotional and behavioral aspects throughout the day, with Educational Centers and Physical Education as the central axis. Didactic recommendations for implementing PA programs against bullying/cyberbullying in school and extracurricular contexts are included.
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22277102
|
EDUCATION
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10.3390/ai5040091
|
A Bag-of-Words Approach for Information Extraction from Electricity Invoices
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In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.
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26732688
|
AI
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10.1186/s40359-024-02056-2
|
The effect of workplace bullying on knowledge sharing of the employees in scientific and technological enterprises: a moderated mediation model
|
This study aims to understand how workplace bullying affects knowledge sharing among employees in Chinese scientific and technological enterprises. A convenience sampling method was employed to survey 275 employees from scientific and technological enterprises of Yangtze River Delta, China. The survey utilized a general information questionnaire, a workplace bullying scale, an organizational belonging scale, a knowledge sharing scale, and a forbearance scale. A moderated mediation model was set up, and the hierarchical regression and the bootstrapping method were applied. The empirical results indicated that workplace bullying has a negative effect on the knowledge sharing, and organization belonging has played mediating effect. Furthermore, Forbearance not only moderated the effect of workplace bullying on organizational belonging, but also moderated the mediated effect of organization belonging, and the effect will be stronger when employees are at a lower level of forbearance. This study offers important implications for scientific and technological enterprises. The findings imply that enterprises should discourage person-related workplace bullying to increase employees’ intention to engage in knowledge-sharing behavior. Moreover, the manager of these firms should develop a culture of family so that they can care for the organization belonging.
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20507283
|
PSYCHOLOGY
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10.3389/feduc.2024.1411503
|
Multifaceted perception of school climate: association between students’ and teachers’ perceptions and other teacher factors
|
Introduction: This study aimed to investigate whether there is a significant association between teachers’ and students’ perceptions of school climate, and if not, whether teacher factors are associated with the respective perceptions.Methods: The participants included 1,831 students and 59 homeroom teachers from 11 public elementary and junior high schools in Japan. Multilevel models were used to examine the association between students’ and teachers’ perceptions of school climate.Results: Of the three teacher-rated school climate scales, only teacher-perceived disciplinary climate was associated with students’ perceptions of school climate. Teachers’ working conditions, such as self-efficacy and stress, were associated with teachers’ perceptions but not students’ perceptions of school climate. Disciplinary climate was associated with students’ perceptions of school climate, even after accounting for the teachers’ working conditions.Discussion: Items questioning specific student behaviors, such as those included in the disciplinary climate scale, may be effective in avoiding incongruence with student evaluations. Moreover, maintaining disciplinary climate itself is important for students’ positive perceptions of the school climate. A disciplinary climate in which teachers and students share responsibility for learning and classroom organization, and strategies that support positive student behavior are preferable to exclusionary discipline strategies. Incorporating feedback data gathered through classroom observations or student perceptions is also important in resolving the incongruence between teachers’ and students’ perceptions of the school climate.
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2504284X
|
EDUCATION
|
10.3389/fpsyg.2024.1387698
|
Parental conflict and adolescents’ socially adverse emotions: the mediating role of family functioning
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Objective: To examine the process of how parental conflict and family functioning influence adolescents’ socially adverse emotions (shyness and loneliness).Methods: Stratified cluster sampling was used to conduct a questionnaire survey among 1,100 junior high school students from three junior high schools in Beijing, Chongqing, and Shijiazhuang, China.Results: (1) The overall experience of adolescents’ socially adverse emotions was at the moderate level; boys’ experience of shyness and loneliness was significantly higher than that of girls; the experience of shyness and loneliness in the second grade was significantly higher than that in the first grade; (2) Parental conflict was significantly negatively correlated with family functioning and significantly positively correlated with adolescents’ socially adverse emotions, while family functioning was significantly negatively correlated with adolescents’ socially adverse emotions; (3) Family functioning partially mediates the relationship between parental conflict and adolescents’ shyness and completely mediates the relationship between parental conflict and adolescents’ loneliness.Conclusion: Compared to adolescents’ shyness, family functioning plays a more important mediating role in the relationship between parental conflict and adolescents’ loneliness.
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16641078
|
PSYCHOLOGY
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10.3389/fonc.2024.1466912
|
Electrolyte prognosis scoring system can predict overall survival in patients with osteosarcoma
|
Osteosarcoma stands as the most prevalent bone tumor, characterized by a heightened tendency for local recurrence and distant metastasis, resulting in a bleak prognosis. Presently, there exists a shortage of novel markers to effectively determine the prognosis of osteosarcoma patients. Recent research indicates that hematological markers partially mirror an individual’s microenvironment, offering potential insights into predicting patient prognosis. However, prior studies predominantly focused on the prognostic significance of singular hematological indices, failing to comprehensively represent the tumor microenvironment of patients. In our investigation, we meticulously gathered data on 22 hematological and electrolyte markers, utilizing LASSO Cox regression analysis to devise an Electrolyte Prognostic Scoring System (EPSS). The EPSS encompasses various indicators, including immunity, inflammation, coagulation, and electrolyte levels. Our findings indicate that the EPSS stands as an independent prognostic factor for overall survival among osteosarcoma patients. It serves as a valuable addition to clinical characteristics, adept at discerning high-risk patients from those deemed clinically low-risk. Furthermore, EPSS-based nomograms demonstrate commendable predictive capabilities.
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2234943X
|
ONCOLOGY
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10.3389/feduc.2024.1473353
|
Who gets to be an ELT course book author? Native speakerism in English for specific purposes and business English course books
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Introduction: Native speakerism has a profound influence on many aspects of ELT, for example negatively affecting job opportunities of those perceived as ‘non-native speakers’. Nevertheless, little is known about the effect of native speakerism on the recruitment of course book authors (CBAs).Methods: Therefore, this study analysed the linguistic and ethnic representation of 161 CBAs of 77 business English business English and English for specific purposes English for Specific Purposes course books (CBs) published globally by Pearson, OUP, CUP, Macmillan and NGL.Results: The data clearly show that publishers tend to hire white ‘native speakers’ from the UK as CBAs. More specifically, 90% of all CBA slots were taken by ‘native speakers’, 95% by white CBAs, and 78% by CBAs from the UK.Discussion: This indicates a profound native speakerist bias among publishers against not only ‘non-native speakers’, but also those ‘native speakers’ who are not white or do not come from the UK. It is thus suggested that business English and English for Specific Purposes publishers pay greater attention to the diversity of the author teams they hire.
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2504284X
|
EDUCATION
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10.3390/ai5040094
|
Causal Economic Machine Learning (CEML): “Human AI”
|
This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing.
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26732688
|
AI
|
10.1186/s40594-024-00511-5
|
STEM career expectations across four diverse countries: motivation to learn mathematics mediates the effects of gender and math classroom environments
|
We tested the broad generality of a model for predicting 9th–10th grade students’ STEM career expectations by age 30, focusing on hard science, mathematics and engineering professions only, known for driving innovation, research and development. The model’s predictors included motivation to learn mathematics, gender, and math classroom environments (disciplinary climate, teacher support and instructional strategies fostering conceptual understanding). We used data from the Programme for International Student Assessment (PISA) 2022. Four countries were selected based on the percentage of students expecting STEM careers, representing high vs. low groups (Qatar and Morocco vs. Czech Republic and Lithuania, respectively). Analysis began with computing correlations between the variables, followed by path analyses for each country to determine both direct and indirect effects of the predictors on students’ STEM career expectations. We found that motivation to learn mathematics not only directly predicted STEM career expectations but also mediated the influence of the remaining variables: gender (boys show higher motivation to learn math), and math classroom environments (students in well-disciplined math classes with supportive teachers who employ instructional strategies fostering math reasoning also demonstrate higher motivation to learn math). Remarkably, our model consistently demonstrated robustness across all four countries, despite their significant economic, ethnic, and religious diversity. Theoretically, the model reveals that 9th–10th grade students’ transitory long-term STEM career expectations are shaped by their interest in mathematics, their perceived importance of the subject, confidence in their self-efficacy to succeed in math tasks, perceptions of classroom disciplinary climate, teacher support, and their exposure to instructional strategies aimed at enhancing math reasoning. Practically, it suggests widespread potential for informing interventions aimed at increasing student motivation to pursue STEM careers through improved mathematics education practices.
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21967822
|
EDUCATION
|
10.3390/educsci14101107
|
Evaluation of the Implementation of Project-Based-Learning in Engineering Programs: A Review of the Literature
|
Project-Based Learning (PBL), as an experiential methodology, improves learning outcomes and competencies (technical and non-technical) in engineering students. The Conceive–Design–Implement–Operate (CDIO) approach, adopted globally in engineering education, is based on PBL but expands the curriculum framework. Developed by MIT and the Royal Institute of Technology (KTH) in Sweden, CDIO focuses on the entire life cycle of engineering projects to train engineers so that they are capable of applying knowledge in real-life situations. Integrating CDIO and PBL into engineering curricula requires changes in teaching methodologies, teacher training and workspaces. The literature has explored their combination, highlighting shared values and mutual reinforcements. An assessment model is crucial for implementing PBL and evidencing improvement in student and course skills. Only through assessment and the cycle of continuous improvement will the adoption of PBL in engineering programs be advanced. A systematic review of the literature is proposed to identify effective methods in the evaluation of educational programs based on PBL, analyzing related research areas and evaluations according to the CDIO approach.
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22277102
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EDUCATION
|
10.3389/fonc.2024.1437347
|
Comparison of the diagnostic efficacy between imaging features and iodine density values for predicting microvascular invasion in hepatocellular carcinoma
|
Background: In recent years, studies have confirmed the predictive capability of spectral computer tomography (CT) in determining microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Discrepancies in the predicted MVI values between conventional CT imaging features and spectral CT parameters necessitate additional validation.Methods: In this retrospective study, 105 cases of small HCC were reviewed, and participants were divided into MVI-negative (n=53, Male:48 (90.57%); mean age, 59.40 ± 11.79 years) and MVI-positive (n=52, Male:50(96.15%); mean age, 58.74 ± 9.21 years) groups using pathological results. Imaging features and iodine density (ID) obtained from three-phase enhancement spectral CT scans were gathered from all participants. The study evaluated differences in imaging features and ID values of HCC between two groups, assessing their diagnostic accuracy in predicting MVI occurrence in HCC patients. Furthermore, the diagnostic efficacy of imaging features and ID in predicting MVI was compared.Results: Significant differences were noted in the presence of mosaic architecture, nodule-in-nodule architecture, and corona enhancement between the groups, all with p-values < 0.001. There were also notable disparities in IDs between the two groups across the arterial phase, portal phase, and delayed phases, all with p-values < 0.001. The imaging features of nodule-in-nodule architecture, corona enhancement, and nonsmooth tumor margin demonstrate significant diagnostic efficacy, with area under the curve (AUC) of 0.761, 0.742, and 0.752, respectively. In spectral CT analysis, the arterial, portal, and delayed phase IDs exhibit remarkable diagnostic accuracy in detecting MVI, with AUCs of 0.821, 0.832, and 0.802, respectively. Furthermore, the combined models of imaging features, ID, and imaging features with ID reveal substantial predictive capabilities, with AUCs of 0.846, 0.872, and 0.904, respectively. DeLong test results indicated no statistically significant differences between imaging features and IDs.Conclusions: Substantial differences were noted in imaging features and ID between the MVI-negative and MVI-positive groups in this study. The ID and imaging features exhibited a robust diagnostic precision in predicting MVI. Additionally, our results suggest that both imaging features and ID showed similar predictive efficacy for MVI.
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2234943X
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ONCOLOGY
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10.1007/s44196-024-00656-9
|
AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem
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This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.
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18756883
|
AI
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10.3390/ai5040096
|
Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis
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Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by OpenAI, the Generative Pre-trained Transformer 3.5 model (GPT-3) may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra-indications. Our objective was to determine whether GPT-3.5, when provided high-throughput clinical, laboratory, and radiological text-based information, will come to clinical decisions similar to those of a machine learning model and a board-certified surgeon (reference standard) in decision-making for appendectomy versus conservative treatment. Methods: In this cohort study, we randomly collected patients presenting at the emergency department (ED) of two German hospitals (GFO, Troisdorf, and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0 + 386. Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. Statistical significance was defined as p < 0.05. Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. The estimated model training accuracy was 83.3% (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0% (95% CI: 66.4, 97.2). This is in comparison to the GPT-3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (p = 0.21). Conclusions: This study, the first study of the “intended use” of GPT-3.5 for surgical treatment to our knowledge, comparing surgical decision-making versus an algorithm found a high degree of agreement between board-certified surgeons and GPT-3.5 for surgical decision-making in patients presenting to the emergency department with lower abdominal pain.
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26732688
|
AI
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10.3390/ejihpe14100184
|
FOBism Unveiled: Quantifying Assimilative Racism within Asians in the United States
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FOB (fresh-off-the-boat) is a term used to refer to unassimilated immigrants or sojourners, which has created a divide within the Asian community. In this study, we coined the term FOBism, a form of internalized racism (or appropriated racial oppression) that intersects with assimilation, and we developed a measure of FOBism. We created a 14-item, 3-factor FOBism Scale and evaluated its psychometric properties among a sample of 296 Asians in the United States. Exploratory structural equation modeling (ESEM) was utilized to select items and evaluate the factorial validity. Results yielded a strong factor structure, internal consistency reliability, and construct validity. Construct validity was demonstrated through FOBism scores’ positive correlations with measures of within-group discrimination and internalized racism, and negative associations with an Asian cultural orientation. The FOBism Scale is a promising measure that could be used as an assessment tool and to raise awareness of the phenomenon.
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22549625
|
PSYCHOLOGY
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10.3389/frai.2024.1460364
|
Large language models for whole-learner support: opportunities and challenges
|
In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.
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26248212
|
AI
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10.3390/cancers16203510
|
Targeting the Leloir Pathway with Galactose-Based Antimetabolites in Glioblastoma
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Background: Glioblastoma (GBM) uses Glut3 and/or Glut14 and the Leloir pathway to catabolize D-Galactose (Gal). UDP-4-deoxy-4-fluorogalactose (UDP-4DFG) is a potent inhibitor of the two key enzymes, UDP-galactose-4-epimerase (GALE) and UDP-Glucose 6-dehydrogenase (UGDH), involved in Gal metabolism and in glycan synthesis. The Gal antimetabolite 4-deoxy-4-fluorogalactose (4DFG) is a good substrate for Glut3/Glut14 and acts as a potent glioma chemotherapeutic. Methods: Primary GBM cell cultures were used to examine toxicity and alterations in glycan composition via lectin binding in fixed cells and by Western blots. Toxicity/efficacy in vivo data was performed in mouse flank and intracranial models. The effect of 4DFG on D-glucose (Glc) metabolism in GBM cells was assessed by using 13C NMR-based tracer studies. Results: 4DFG is moderately potent against GBM cells (IC50: 125–300 µM). GBM glycosylation is disrupted by 4DFG. Survival analysis in an intracranial mouse model showed that treatment with 4DFG (6 × 25 mg/kg of 4DFG, intravenously) improved outcomes by three-fold (p < 0.01). Metabolic flux analysis revealed that both glycolytic and mitochondrial metabolic fluxes of [U-13C]Glc were significantly decreased in the presence of 4DFG in GBM cells. Conclusion: A functional Gal-scavenging pathway in GBM allows Gal-based antimetabolites to act as chemotherapeutics. 4DFG is metabolized by GBM in vitro and in vivo, is lethal to GBM tumors, and is well tolerated in mice.
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20726694
|
ONCOLOGY
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10.3390/educsci14101128
|
The Integration of Mixed Reality Simulation into Reading Literacy Modules
|
The reading literacy crisis, among learners, in countries throughout the world and in South Africa seems to be reaching pandemic levels. Hence, the quality of teaching and the preparation that pre-service teachers receive at initial teacher education institutions is under the spotlight. A proactive action research design is used to integrate mixed reality simulation into reading literacy modules. Our data collection methods included professional conversations, WhatsApp voice notes and video calls, reflective journal entries and reflections on observing video recordings of lesson segments in the MRS environment. The data was analyzed using content analysis. The main themes emanating from the data included: lack of focus on high leverage teaching practices, limited use of pedagogies of enactment, add-on to existing content, experimentation, perceptions, planning and preparation, content-method integration, pedagogies of enactment, assessment, resources and feedback. Grounded in a Community of Practice framework, we narrate our experiences of re-imagining mixed reality simulation as a core component of initial teacher education programs. The authors conclude by sharing insights and recommendations for policymakers, faculty leaders, and curriculum designers, contributing to informed decisions regarding integrating and potentially upscaling mixed reality simulation within reading literacy modules in initial teacher education programs.
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22277102
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EDUCATION
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10.3389/frai.2024.1435895
|
A review on the efficacy of artificial intelligence for managing anxiety disorders
|
Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.
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26248212
|
AI
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10.3389/feduc.2024.1449363
|
Synchronous online learning and career readiness in higher education: student perceptions, challenges, and solutions
|
Synchronous Online Learning (SOL) environments have rapidly transformed the educational landscape. However, there is limited research on their efficacy in equipping students with the necessary skills to succeed in the workforce, particularly in developing essential professional skills like digital literacy, interpersonal communication, and practical experience. This study explores how SOL impacts students’ readiness for the workforce and the development of these critical skills. The research employed a qualitative methodology involving in-depth interviews with 27 third- and fourth-year students from a South African university. Purposive sampling was used to capture diverse experiences regarding SOL and its influence on professional skill development. Thematic analysis was performed to identify critical patterns and insights from the interviews. Findings reveal that SOL environments effectively enhance students’ technical skills and digital adaptability, essential for navigating a digital workforce. However, SOL is inadequate in developing interpersonal skills and providing practical, hands-on experiences. Students reported a lack of networking opportunities and expressed concerns about their preparedness for the demands of real-world employment, particularly in fields requiring strong interpersonal skills and practical experience. The study highlights the need for educational innovations that combine the benefits of digital learning with comprehensive skill development strategies, particularly in soft skills and experiential learning.
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2504284X
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EDUCATION
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10.1007/s00432-024-05946-5
|
Feasibility of a complex psychosocial intervention for families with parental cancer: acceptability, suitability, implementability, and perceived support
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Purpose: This study aimed to assess the feasibility of a comprehensive psychosocial intervention for families coping with parental cancer. Methods: A quasi-experimental trial with intervention and control group, employing a mixed-methods approach, was conducted. A total of 472 families affected by parental cancer participated. The feasibility of the intervention was evaluated based on study monitoring measures (on-site visits, team supervision meeting observations, case conference observations, best practice workshops, coordinating information exchange between intervention sites, and reviewing intervention documentation), process evaluation (semi-structured interviews, focus group discussion) and survey data. Data analysis involved thematic coding and descriptive statistics. Results: The intervention was well-received by the participating families, with a high degree of acceptance observed. The feasibility of the intervention was found to be associated with specific dynamics within each family system and the motivation of the family members. The success of the intervention was described as dependent on the family-centered arrangement of the encounters, including factors such as frequency, duration, and mode, which greatly influenced its overall acceptability. Conclusion: The family-scout intervention demonstrates its feasibility as an effective intervention to reduce the burden experienced by families coping with parental cancer. Psychosocial oncology services should continue to develop and implement family-centered interventions to offer support to families during their cancer journey. Trial registration: ClinicalTrials.gov, NCT04186923. Retrospectively registered on 4 December 2019.
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14321335
|
ONCOLOGY
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10.3389/frai.2024.1497705
|
The impact of pedagogical beliefs on the adoption of generative AI in higher education: predictive model from UTAUT2
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Artificial Intelligence in Education (AIEd) offers advanced tools that can personalize learning experiences and enhance teachers’ research capabilities. This paper explores the beliefs of 425 university teachers regarding the integration of generative AI in educational settings, utilizing the UTAUT2 model to predict their acceptance and usage patterns through the Partial Least Squares (PLS) method. The findings indicate that performance expectations, effort expectancy, social influence, facilitating conditions, and hedonic motivation all positively impact the intention and behavior related to the use of AIEd. Notably, the study reveals that teachers with constructivist pedagogical beliefs are more inclined to adopt AIEd, underscoring the significance of considering teachers’ attitudes and motivations for the effective integration of technology in education. This research provides valuable insights into the factors influencing teachers’ decisions to embrace AIEd, thereby contributing to a deeper understanding of technology integration in educational contexts. Moreover, the study’s results emphasize the critical role of teachers’ pedagogical orientations in their acceptance and utilization of AI technologies. Constructivist educators, who emphasize student-centered learning and active engagement, are shown to be more receptive to incorporating AIEd tools compared to their transmissive counterparts, who focus on direct instruction and information dissemination. This distinction highlights the need for tailored professional development programs that address the specific beliefs and needs of different teaching philosophies. Furthermore, the study’s comprehensive approach, considering various dimensions of the UTAUT2 model, offers a robust framework for analyzing technology acceptance in education.
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26248212
|
AI
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10.1186/s40359-024-02065-1
|
Improving cognitive function in Chinese children with ADHD and/or RD through computerized working memory training
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Prior research has found that children with attention-deficit hyperactivity disorder (ADHD) and reading difficulties (RD) are at an elevated risk of developing further cognitive deficits and developmental challenges [1]. ADHD and RD are characterized by a deficit in working memory, which negatively affects learning and behavior. The main aims of this study were to design a working memory training app and examine its effectiveness through a 5-week training program in Chinese children with ADHD and/or RD. There were three experimental groups, with 26 participants in the ADHD group, 38 participants in the RD group, and 24 participants in the ADHD + RD group. The typically developing (TD) control group had 32 participants. All participants completed the pretest and posttest assessments on executive function and reading performance. The findings indicate that the experimental groups improved performance in verbal and visual-spatial working memory as well as Chinese word reading. There was an overall reduction in functional impairment following the training, in contrast to the TD group. This study showed that working memory can be improved through computerized training in children with ADHD and/or RD. The implications of future research in working memory are discussed. Clinical Trials Identifier: NCT06567444 (retrospectively registered) on 20 August 2024.
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20507283
|
PSYCHOLOGY
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10.3389/frai.2024.1414122
|
Heuristic machine learning approaches for identifying phishing threats across web and email platforms
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Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. The sole intention is to collect personal information through malicious program-based attempts embedded in URLs, emails, and website-based attempts. Notably, this proposed technique detects URL, email, and website-based phishing attacks, which will be beneficial and secure us from scam attempts. Subsequently, the data are pre-processed to identify phishing attacks using data cleaning, attribute selection, and attacks detected using machine learning techniques. Furthermore, the proposed techniques use heuristic-based machine learning to identify phishing attacks. Admittedly, 56 features are used to analyze URL phishing findings, and experimental results show that the proposed technique has a better accuracy of 97.2%. Above all, the proposed techniques for email phishing detection obtain a higher accuracy of 97.4%. In addition, the proposed technique for website phishing detection has a better accuracy of 98.1%, and 48 features are used for analysis.
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26248212
|
AI
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10.3389/fpsyg.2024.1365180
|
The limits of personal experience
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This article examines how three types of experience—personal, related others, and unrelated others—influence decision-making. We present the complexities and nuances in using these experiential sources to suggest that personal experience is preferred to the other two sources. We discuss the implications of this preference for decision-making processes, especially in contexts involving transformative outcomes. To conclude, we discuss how people rely on other experiential sources when their preferred source is limited.
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16641078
|
PSYCHOLOGY
|
10.3390/ai5040097
|
Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting
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This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.
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26732688
|
AI
|
10.1007/s00432-024-06001-z
|
RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients
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Background Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. Methods We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. Results Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. Conclusions This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.
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14321335
|
ONCOLOGY
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10.3389/fpsyg.2024.1449629
|
Mindfulness and mental health: the importance of a clinical intervention to prevent the effects of a traumatic event. A pilot study
|
Numerous research studies show that mindfulness can mitigate the negative impact of trauma on mental health by reducing symptoms of anxiety, mediating the relationship between trauma exposure and mental health, and treating symptoms resulting from traumatic events. During the COVID-19 pandemic, which was considered a traumatic event, the wellbeing of adults and children was severely compromised. Although children seem less vulnerable to the physical effects of the virus, this does not seem to be true for the psychological effects. Indeed, a prolonged period of loss of family activities and routines can have a negative impact on the mental health of children and adolescents. To investigate how mindfulness can help preschool children cope with the effects of COVID-19, a study was conducted on 46 children aged 4–5 years. The programme, based on the work of Jon Kabat-Zinn and adapted to the age of the participants, consisted of eight weekly 45-min sessions. Qualitative and quantitative results showed positive feedback, indicating that mindfulness helps children make sense of their experiences and achieve functional post-traumatic growth. This approach is seen as a challenge to guide children toward the restoration of psychological wellbeing, which is essential for good psychological balance.
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16641078
|
PSYCHOLOGY
|
10.1186/s40359-024-02074-0
|
A scoping review of well-being measures: conceptualisation and scales for overall well-being
|
This study aims to identify the conceptualisation of overall well-being used for well-being assessment through a review of the characteristics and key components and/or dimensions of well-being scales as presented in current literature. Scopus and Web of Science were searched, and thematic analysis was conducted inductively to analyse the identified components within scales, as well as the types of well-being these scales measure. 107 peer-reviewed articles from 2003 to 2022 were included, and 69 well-being scales were identified covering nine areas of well-being. Four final themes were identified as the foundational dimensions of overall well-being: hedonic; eudaimonic; physical health; and generic happiness. Notably, these 69 scales are mainly validated and adopted in the Western context. ‘4 + N’ frameworks of overall well-being are recommended for assessing overall well-being. This review provides researchers with a synthesis of what types of well-being have been measured and which measures have been used to assess these types of well-being for which research participants. Non-Western-based well-being research is called for that incorporates a broader range of research participants and cultural contexts in contributing to a more inclusive understanding of well-being.
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20507283
|
PSYCHOLOGY
|
10.3389/frai.2024.1446640
|
Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography
|
Objective: This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).Methods: A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.Results: The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466–11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003–1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387–1.918).Conclusion: AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.
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26248212
|
AI
|
10.3390/educsci14111161
|
The Role of Cognitive Learner Prerequisites for Cognitive Load and Learning Outcomes in AR-Supported Lab Work
|
Augmented Reality (AR) can enhance student-centered lab work by bridging the spatial and temporal split between virtual information and observed real-world phenomena. While the Cognitive Theory of Multimedia Learning and the Cognitive Load Theory suggest that AR can reduce extraneous cognitive load (ECL) and foster learning, the empirical results remain inconsistent. This re-analysis of three related studies with different target groups and AR devices explores whether learners’ spatial abilities and verbal working memory capacity moderate the effect of AR support in lab work settings on ECL and conceptual knowledge gains. Although these moderators could not be confirmed consistently, the results indicate that tablet-based AR holds the potential to support learners with low spatial abilities. Moreover, low verbal working memory learners were demonstrated to be particularly vulnerable to the spatial contiguity failure that can be caused by smartglasses AR. Moderation effects were only observed for ECL but not for conceptual knowledge gains. The findings highlight that the benefit of AR support can depend on learners’ cognitive prerequisites and additional contextual factors, such as the AR device used and the age of the target group. The design and implementation of AR-supported lab work environments should account for these factors to optimize the learning outcomes.
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22277102
|
EDUCATION
|
10.3389/feduc.2024.1253671
|
Tax credit for support of university-community partnerships in low-income urban school districts
|
Tying public school funding to property taxes has prevented low-income school districts in the United States from garnering adequate financial and social resources. As a result of this regressive funding system, millions of children find themselves trapped in underfunded schools and neighborhoods that perpetuate intergenerational trauma, tenuous employment, poor health, and poverty. However, in many underserved neighborhoods, including in cities like Philadelphia and Chicago, where poverty rates have been as high as 25 and 40%, respectively, many of the most under-resourced schools border or are adjacent to wealthy universities. Given this proximity of many universities and their wealth of resources spanning medical centers, community organizations, faculty, and students, the potential for mutual benefit, long-term structural change, and the ability to fulfill shared missions is significant, and partnerships that breakdown historical siloes must be encouraged. Therefore, this policy brief advocates for a tax credit at the federal level to incentivize and catalyze scaling of successful university-community partnership models that have been transformative in their respective communities.
|
2504284X
|
EDUCATION
|
10.3390/educsci14111165
|
Developing Doctoral Theses in Education: The Role of Systematic Reviews in the Spanish Context
|
The production and development of doctoral theses have grown exponentially with the advent of the Internet and the democratization of access to information and education. In the field of education, this production is no stranger to this trend, so it is interesting to analyze the implications, causes and scientific–academic contributions of this increase. To this end, a systematic literature review (SLR) was carried out using the PRISMA 2020 protocol, with the Dialnet database as the documentary source, based on a previous study that justified its use and the availability of documents. Thus, a set of pre-established criteria are defined to identify doctoral theses carried out in Spanish universities and related to the education area that have been published in the last 17 years, finding a total of (n = 120) publications whose analysis answered the researchers’ questions focused on identifying patterns and strategies in the publication and methodological design of this type of document and what is the role of systematic reviews of the literature in them. In this sense, this research process aimed to analyze this kind of production and facilitate the process of designing new theses and research projects in the field of education. In this sense, this research process aimed to analyze this sort of output and facilitate the process of designing new theses and research projects in the field of education. The results make it possible to identify the increased importance of SLR in the development of doctoral theses and reveal the predominant models related to this type of production. Additionally, other aspects, such as the most common universities or research fields, the quantity and nature of subsequent studies, etc., concerning doctoral theses that incorporate an SLR were determined. Thus, conducting an SLR represents a solid and structured approach to initiate and build up the research process of doctoral theses, being essential to address students’ potential training needs in these regards.
|
22277102
|
EDUCATION
|
10.3390/cancers16213626
|
A Systematic Review of the Use of Surgical Checklists in Transurethral Resection of Bladder Tumour
|
Context: Surgical checklists have previously been shown to improve surgical quality and patient outcomes. However, their use in transurethral resection of bladder tumour (TURBT), one of the most commonly performed urological procedures, has yet to be explored in depth. Objective: To evaluate the effect of surgical checklist implementation in TURBT on documentation quality, specimen quality, and oncological outcomes according to the existing literature. We then hope to develop an optimised TURBT checklist by identifying the most pertinent parameters for inclusion. Evidence acquisition: A literature search using PubMed was performed to identify literature pertaining to the use of surgical checklists in the context of TURBT. A systematic review was then performed on the 41 identified studies, of which six were included in the final analysis. Evidence synthesis: We explored three primary outcomes that arose from the literature, namely: (1) comprehensiveness of documentation; (2) resection quality; and (3) recurrence rates and recurrence-free survival (RFS). We found agreement in the literature that surgical checklist implementation does lead to an overall improvement in documentation. The effect of surgical checklists on resection quality and recurrence rates, however, was mixed in the literature, with some studies showing statistically significant improvements and others showing no significant change. Conclusions: There are multiple benefits to surgical checklist implementation in TURBT procedures. We propose an optimised 14-item surgical checklist that should be implemented in every TURBT report to ensure sufficient information documentation for risk stratification and post-operative management.
|
20726694
|
ONCOLOGY
|
10.3390/ai5040100
|
Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD)
|
Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.
|
26732688
|
AI
|
10.3390/ai5040101
|
Deep Learning in Finance: A Survey of Applications and Techniques
|
Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.
|
26732688
|
AI
|
10.3390/ai5040102
|
Airfoil Shape Generation and Feature Extraction Using the Conditional VAE-WGAN-gp
|
A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and Wasserstein generative adversarial network with gradient penalty (WGAN-gp), is proposed for an airfoil generation method, and then, it is compared with the WGAN-gp and VAE models. The VAEGAN model couples the VAE and GAN models, which enables feature extraction in the GAN models. In airfoil generation tasks, to generate airfoil shapes that satisfy lift coefficient requirements, it is known that VAE outperforms WGAN-gp with respect to the accuracy of the reproduction of the lift coefficient, whereas GAN outperforms VAE with respect to the smoothness and variations of generated shapes. In this study, VAE-WGAN-gp demonstrated a good performance in all three aspects. Latent distribution was also studied to compare the feature extraction ability of the proposed method.
|
26732688
|
AI
|
10.3390/educsci14111181
|
A Systematic Review of Digital Competence Evaluation in Higher Education
|
University students’ digital skills depend significantly on educators’ proficiency, necessitating regular assessments. Tools like DigComp and the TPACK model are provided in this technological context. A systematic review, following PRISMA criteria, aims to evaluate digital competencies through globally used tools. DigCompEdu is prominent, with Spain leading the research, while unvalidated instruments from Asia highlight global disparities. This review will identify key tools and expose geographical and validation gaps, stressing the need for standardized assessments. Understanding the predominance of DigCompEdu and seeing the variation that is generated in Asia highlights the poor ability to transmit self-perceived competencies to learners.
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22277102
|
EDUCATION
|
10.1007/s00432-024-06005-9
|
Dysregulation of SIGLEC1 in non-small cell lung cancer: prognostic implications and immunomodulatory role-a multicenter cohort study
|
Purpose To investigate the clinical significance and functional role of SIGLEC1-positive cells in non-small cell lungcancer (NSCLC) patients, focusing on their prognostic impact and therapeutic response. Methods A multicenter retrospective cohort analysis was conducted, integrating data from multiple sources. Weanalyzed SIGLEC1 expression in NSCLC tissues, clinicopathological features, overall survival outcomes,chemotherapy responsiveness, and sensitivity to targeted therapies. We also developed a prognostic model basedon SIGLEC1 expression and clinical variables. Results SIGLEC1 expression was significantly downregulated in NSCLC tissues, and the density of SIGLEC1-positivecells was inversely correlated with various clinicopathological features. Notably, patients with high infiltration ofSIGLEC1-positive cells exhibited significantly better overall survival outcomes. Furthermore, elevated SIGLEC1expression was associated with improved responsiveness to chemotherapy and demonstrated distinct patterns ofsensitivity to targeted therapies. A robust prognostic model was developed by integrating SIGLEC1 expression andclinical variables. Conclusions This study highlighted the downregulation of SIGLEC1 in NSCLC tissues and its significant associationwith patient prognosis and therapeutic response. The findings suggested that SIGLEC1 played a critical role inmodulating the tumor immune microenvironment and has potential as both a prognostic biomarker and therapeutictarget in NSCLC.
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14321335
|
ONCOLOGY
|
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