mdpi-ai/citelens huggingface dataset:
Effect of social support on Muslim women’s sporting activities: mediating effect of psychological adjustment
Field: PSYCHOLOGY

Objective: This study explores the relationship between social support and sporting activities of Muslim women and constructs a mediation model through role of psychological adjustment.Methods: Using stratified cluster sampling, 301 Muslim women were measured in group psychology using the Social Support Scale and the Sports Activities and Psychological Adjustment Scale. The statistical software SPSS 24.0 and SPSS PROCESS 3.3 were used for statistical processing. The common-method variation test was carried out using the Harman single-factor control test. Finally, the Bootstrap sampling test method and process plug-in were used to test the significance of the intermediary effect.Results: (1) Social support has a significant predictive effect on sports activities (β = 0.32, p < 0.001); (2) psychological adjustment (β = 0.552, p < 0.001) mediates the relationship between social support and sporting activities [social support → psychological adjustment → sporting activities (95% Cl, 0.093, 0.323)].Conclusion: Social support positively influences sporting participation among Muslim women, and psychological adjustment mediates this relationship. Strengthening social support for Muslim women can enhance their psychological adjustment, thereby improving their participation in sporting activities and offering valuable theoretical and practical guidance.
Facial Recognition Using Hidden Markov Model and Convolutional Neural Network
Field: AI

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.
Transparency and precision in the age of AI: evaluation of explainability-enhanced recommendation systems
Field: AI

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.
Noise-induced modality-specific pretext learning for pediatric chest X-ray image classification
Field: AI

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.
Piloting puberty content books and a teacher training guide in Sierra Leone: a qualitative assessment
Field: EDUCATION

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.
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma
Field: ONCOLOGY

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.
Paclitaxel hyperthermia suppresses gastric cancer migration through MiR-183-5p/PPP2CA/AKT/GSK3β/β-catenin axis
Field: ONCOLOGY

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.
Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT for Classifying Focused Ultrasound-Related Articles
Field: AI

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.
Prediction of accident-proneness among a sample of Iranian workers: usefulness of an adjusted version of the Health Belief Model with spiritual health
Field: PSYCHOLOGY

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.
Slovenian validation of the Capacity to Love Inventory: associations with clinical measures and mindfulness
Field: PSYCHOLOGY

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.
The transfer effect of computational thinking (CT)-STEM: a systematic literature review and meta-analysis
Field: EDUCATION

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.
Creating Personalized Higher Education Teaching System Using Fuzzy Association Rule Mining
Field: AI

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%).
Multi-version interactive assessment through the integration of GeoGebra with Moodle
Field: EDUCATION

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.
Relationship between cultural diversity awareness and achievement motivation of medical students at the undergraduate level in Pakistan
Field: EDUCATION

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.