mdpi-ai/citelens huggingface dataset:
From contemplation to serenity: how yoga meditation improves the mental health of female college students?
Field: PSYCHOLOGY

Objective: This study aims to investigate the impact of yoga meditation on the mental health of female college students, focusing on how meditation improves emotional regulation, alleviates stress and strengthens psychological resilience.Methods: Employing a combination of quantitative assessment and qualitative analysis, the study measured participants’ emotional states, stress levels, and psychological resilience across multiple time points to track participants’ mental health changes dynamically. In-depth interviews and analysis of meditation journals were also conducted.Results: Yoga meditation significantly reduced anxiety, depression, and perceived stress while enhancing emotional regulation and self-awareness. Meditation positively influenced neuroplasticity, inducing beneficial changes in brain regions associated with emotional control and cognitive flexibility. Additionally, improved autonomic nervous system function was observed, with increased parasympathetic activity and reduced sympathetic response. Meditation strengthened psychological resilience in female college students, improved stress-coping strategies, and sustained positive mental health benefits even after the intervention.Conclusion: Yoga meditation is an effective mental health intervention, bolstering emotional regulation and reducing stress among female college students. Integrating yoga meditation into campus mental health programs is recommended to provide students with greater practice opportunities and personalized guidance.
Technologies applied to education in the learning of English as a second language
Field: EDUCATION

This systematic review, conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, evaluates the efficacy of emerging digital technologies—namely virtual reality (VR), augmented reality (AR), and adaptive learning technologies (ALT)—in enhancing vocabulary acquisition within English as a second language (ESL) education. By addressing a notable gap in the literature, this review explores how these technologies mitigate common learning challenges and improve educational outcomes. Through a critical analysis of recent empirical studies across diverse educational stages, it synthesizes findings to assess their impact on vocabulary retention and overall academic performance. The results indicate that these technologies enhance vocabulary acquisition and increase student motivation and engagement, significantly impacting educational practices and policymaking. This review highlights the transformative potential of VR, AR, and ALT in ESL education by providing immersive and personalized learning experiences that address traditional barriers in language acquisition.
InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification
Field: AI

Alzheimer's disease (AD) is an incurable neurodegenerative disorder that slowly impair the mental abilities. Early diagnosis, nevertheless, can greatly reduce the symptoms that are associated with the condition. Earlier techniques of diagnosing the AD from the MRI scans have been adopted by traditional machine learning technologies. However, such traditional methods involve depending on feature extraction that is usually complex, time-consuming, and requiring substantial effort from the medical personnel. Furthermore, these methods are usually not very specific as far as diagnosis is concerned. In general, traditional convolutional neural network (CNN) architectures have a problem with identifying AD. To this end, the developed framework consists of a new contrast enhancement approach, named haze-reduced local-global (HRLG). For multiclass AD classification, we introduce a global CNN-transformer model InGSA. The proposed InGSA is based on the InceptionV3 model which is pre-trained, and it encompasses an additional generalized self-attention (GSA) block at top of the network. This GSA module is capable of capturing the interaction not only in terms of the spatial relations within the feature space but also over the channel dimension it is capable of picking up fine detailing of the AD information while suppressing the noise. Furthermore, several GSA heads are used to exploit other dependency structures of global features as well. Our evaluation of InGSA on a two benchmark dataset, using various pre-trained networks, demonstrates the GSA's superior performance.
Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?
Field: AI

Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. This study introduces an innovative approach to crop yield prediction by incorporating spatially lagged spectral data (SLSD) through the spatial-lagged machine learning (SLML) model, an enhanced version of the spatial lag X (SLX) model. The research aims to show that SLSD improves prediction compared to traditional vegetation index (VI)-based methods. Conducted on a 19-hectare cornfield at the ARS Grassland, Soil, and Water Research Laboratory during the 2023 growing season, this study used five-band multispectral image data and 8581 yield measurements ranging from 1.69 to 15.86 Mg/Ha. Four predictor sets were evaluated: Set 1 (spectral bands), Set 2 (spectral bands + neighborhood data), Set 3 (spectral bands + VIs), and Set 4 (spectral bands + top VIs + neighborhood data). These were evaluated using the SLX model and four decision-tree-based SLML models (RF, XGB, ET, GBR), with performance assessed using R2 and RMSE. Results showed that incorporating spatial neighborhood data (Set 2) outperformed VI-based approaches (Set 3), emphasizing the importance of spatial context. SLML models, particularly XGB, RF, and ET, performed best with 4–8 neighbors, while excessive neighbors slightly reduced accuracy. In Set 3, VIs improved predictions, but a smaller subset (10–15 indices) was sufficient for optimal yield prediction. Set 4 showed slight gains over Sets 2 and 3, with XGB and RF achieving the highest R2 values. Key predictors included spatially lagged spectral bands (e.g., Green_lag, NIR_lag, RedEdge_lag) and VIs (e.g., CREI, GCI, NCPI, ARI, CCCI), highlighting the value of integrating neighborhood data for improved corn yield prediction. This study underscores the importance of spatial context in corn yield prediction and lays the foundation for future research across diverse agricultural settings, focusing on optimizing neighborhood size, integrating spatial and spectral data, and refining spatial dependencies through localized search algorithms.
Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification
Field: AI

Background: This study investigates the application of machine learning models to classify electrocardiogram signals, addressing challenges such as class imbalances and inter-class overlap. In this study, “normal” and “abnormal” refer to electrocardiogram findings that either align with or deviate from a standard electrocardiogram, warranting further evaluation. “Borderline” indicates an electrocardiogram that requires additional assessment to distinguish benign variations from pathology. Methods: A hierarchical framework reformulated the multi-class problem into two binary classification tasks—distinguishing “Abnormal” from “Non-Abnormal” and “Normal” from “Non-Normal”—to enhance performance and interpretability. Convolutional neural networks, deep neural networks, and tree-based models, including Gradient Boosting Classifier and Random Forest, were trained and evaluated using standard metrics (accuracy, precision, recall, and F1 score) and learning curve convergence analysis. Results: Results showed that convolutional neural networks achieved the best balance between generalization and performance, effectively adapting to unseen data and variations without overfitting. They exhibit strong convergence and robust feature importance rankings, with ventricular rate, QRS duration, and P-R interval identified as key predictors. Tree-based models, despite their high performance metrics, demonstrated poor convergence, raising concerns about their reliability on unseen data. Deep neural networks achieved high sensitivity but suffered from overfitting, limiting their generalizability. Conclusions: The hierarchical binary classification approach demonstrated clinical relevance, enabling nuanced diagnostic insights. Furthermore, the study emphasizes the critical role of learning curve analysis in evaluating model reliability, beyond performance metrics alone. Future work should focus on optimizing model convergence and exploring hybrid approaches to improve clinical applicability in electrocardiogram signal classification.
Elevated platelet distribution width and diabetes may serve as preoperative predictors of microvascular invasion in primary hepatocellular carcinoma
Field: ONCOLOGY

Background and objective Hepatocellular carcinoma (HCC) is one of the malignancies with increasing incidence globally, and microvascular invasion (MVI) is a crucial determinant of prognosis in patients. This study aimed to investigate platelet distribution width (PDW) and diabetes mellitus as indicators for predicting preoperative MVI in HCC, providing more accurate predictive tools for clinicians to guide treatment strategies and improve patient survival and quality of life. Methods A retrospective study was conducted, including 1357 patients who underwent hepatectomy for HCC between January 2008 and December 2014 at the Eastern Hepatobiliary Surgery Hospital in China. Clinical, pathological, and radiological data, including PDW and diabetes status, were collected. Univariate and multivariate logistic regression analyses were performed to identify risk factors for MVI and establish a predictive model. The predictive performance of the model was evaluated through nomograms and internal validation. Results Univariate analysis revealed significant associations between MVI and diabetes mellitus, presence of liver cirrhosis, prealbumin level, tumor diameter, number of tumors, HBV DNA viral load > 104, and PDW ≥ 17. Multivariate logistic regression analysis identified diabetes mellitus, liver cirrhosis, prealbumin level, tumor diameter, number of tumors, HBV DNA viral load > 104, and PDW ≥ 17 as independent risk factors for MVI. Based on these findings, a predictive model was established, demonstrating high predictive accuracy and stability in both the training and validation cohorts. Conclusion This study confirmed PDW and diabetes mellitus as reliable indicators for predicting preoperative MVI in HCC and established a corresponding predictive model. Future research should further explore the underlying mechanisms and enhance clinical validation to advance the field of HCC treatment.
Psychometric properties of the Persian version of the ambivalent ageism scale (benevolent and hostile) in the adult population in Iran
Field: PSYCHOLOGY

With the growing population of older adults and the prevalence of negative attitudes towards them, the issue of ageism and its health and economic impacts in both benevolent and hostile contexts warrants special attention. It is crucial to examine the attitudes of other age groups towards older adults across different societies. Particularly, the benevolent dimension of ageism, which has been less explored in research, requires more focus. Therefore, this study aims to conduct a psychometric evaluation of The Ambivalent Ageism Scale among the adult population in Iran. This methodological study was conducted in comprehensive health centers in Gorgan city in 2023. A total of 381 eligible adults participated. The Ambivalent Ageism Scale (AAS) was utilized, and the psychometric assessment included translation, face validity, and content validity. Additionally, exploratory factor analysis and confirmatory factor analysis were performed. The reliability of the scale was evaluated using the internal consistency method. The research findings were analyzed using SPSS and AMOS software version 24. Qualitative face and content validity assessments led to textual and editorial modifications of the items. The Content Validity Ratio (CVR), Item-Content Validity Index (I-CVI), and Kappa (K*) scores were acceptable for all items. In the exploratory factor analysis (EFA), similar to the original questionnaire, three factors were extracted, accounting for approximately 54% of the total variance. The fit indices in the confirmatory factor analysis (CFA) indicated an acceptable model fit. During CFA, four items were eliminated. The reliability of the entire questionnaire was deemed acceptable with a Cronbach’s alpha coefficient of 0.763. Consequently, the Persian version of the Ambivalent Ageism Scale was confirmed with nine items. The Persian version of The Ambivalent Ageism Scale demonstrates sufficient validity and reliability for measuring attitudes towards aging within Iranian society. Given the cultural adaptation of this tool, the questionnaire can be utilized to assess adults’ views and attitudes towards older adults in both hostile and benevolent dimensions. Furthermore, it can aid in formulating family-oriented policies for older adult care and facilitate improvements in the quality of care for this population group.
AI-Driven Telerehabilitation: Benefits and Challenges of a Transformative Healthcare Approach
Field: AI

Artificial intelligence (AI) has revolutionized telerehabilitation by integrating machine learning (ML), big data analytics, and real-time feedback to create adaptive, patient-centered care. AI-driven systems enhance telerehabilitation by analyzing patient data to personalize therapy, monitor progress, and suggest adjustments, eliminating the need for constant clinician oversight. The benefits of AI-powered telerehabilitation include increased accessibility, especially for remote or mobility-limited patients, and greater convenience, allowing patients to perform therapies at home. However, challenges persist, such as data privacy risks, the digital divide, and algorithmic bias. Robust encryption protocols, equitable access to technology, and diverse training datasets are critical to addressing these issues. Ethical considerations also arise, emphasizing the need for human oversight and maintaining the therapeutic relationship. AI also aids clinicians by automating administrative tasks and facilitating interdisciplinary collaboration. Innovations like 5G networks, the Internet of Medical Things (IoMT), and robotics further enhance telerehabilitation’s potential. By transforming rehabilitation into a dynamic, engaging, and personalized process, AI and telerehabilitation together represent a paradigm shift in healthcare, promising improved outcomes and broader access for patients worldwide.
Psychometric properties of the Persian version of the suicidal intrusions attributes scale (SINAS) in patients with suicidal attempt
Field: PSYCHOLOGY

The Suicidal Intrusions Attributes Scale (SINAS) is a brief self-report measure designed to assess the frequency, distress, and controllability of suicidal intrusions—vivid, uncontrollable mental images and thoughts related to suicide or its aftermath. Despite its clinical relevance, its psychometric properties remain underexplored. This study aimed to evaluate the psychometric properties of the Persian version of the SINAS. A cross-sectional design was employed. 304 outpatients (aged 18 to 65, M = 27.27, SD = 8.53) including 243 males and 61 females with a history of suicide attempts were recruited using a convenience sampling method from psychiatric clinics and hospitals in Tehran. Participants completed the SINAS along with the Beck Depression Inventory-II (BDI-II) to assess depressive symptoms, the Beck Hopelessness Scale (BHS) to measure negative expectations about the future, the Beck Scale for Suicide Ideation (BSSI) to evaluate suicidal thoughts and intentions, and the Suicide Behaviors Questionnaire-Revised (SBQ-R) to assess past suicidal behaviors and future risk. Confirmatory factor analysis supported a one-factor structure of the SINAS, which was invariant across gender groups. The scale demonstrated strong internal consistency and good test-retest reliability over a two-week interval. Additionally, the SINAS showed significant associations with depressive symptoms, hopelessness, suicide ideation, and suicide risk behaviors, supporting its convergent validity. Overall, the findings indicate that the Persian version of the SINAS is a valid and reliable instrument for assessing suicidal intrusions in both clinical and research settings in Iran.
A Deep Learning Model Leveraging Time-Series System Call Data to Detect Malware Attacks in Virtual Machines
Field: AI

A Tenant Virtual Machine (TVM) user in the cloud may misuse its computing power to launch malware attack against other tenant VMs, Host OS, Hypervisor, or any other computing devices/resources inside the cloud environment of a Cloud Service Provider. The security solutions deployed within the TVM may not be reliable, as malware can disable them or remain undetected due to its hidden nature. Therefore, security solutions deployed outside the virtual machine are necessary. This research proposes deploying an Intrusion Detection System (IDS) at the Hypervisor layer, utilizing time series system call data and employing a Convolutional Neural Network (CNN) model to accurately detect the presence of malicious (malware) computer programs within virtual machines. The raw VMM system call traces are transformed into novel Time Series System Call patterns and utilized by a deep learning algorithm for training and building the classifier model. A deep learning model, CNN, is used to build the classifier model for detecting intrusions with high accuracy. It is capable of detecting both known and unknown malware. The CNN model is compared with machine learning algorithms for the results and discussions, and it outperforms ML algorithms in terms of intrusion detection accuracy when utilizing novel time series system call data..
Linguistic and Cognitive Abilities in Children with Dyslexia: A Comparative Analysis
Field: PSYCHOLOGY

Introduction: Dyslexia is a prevalent learning disorder that significantly affects the child population. It is often accompanied by deficits in language processes, cognition, and executive functioning, all of which are crucial for reading development. Children with dyslexia frequently exhibit difficulties in phonological processing, semantics, morphosyntax, and also in cognitive areas such as working memory, inhibition, planning, and attention. Objective: The primary objective of this study was to compare the linguistic, cognitive, and executive functioning abilities between children diagnosed with dyslexia and those with typical reading development. Methodology: A total of 120 children were selected and divided into two groups: the G-DYSLEXIA group (n = 60), consisting of children diagnosed with dyslexia, and the G-CONTROL group (n = 60), with typical reading development. Language, cognition, and executive functions were assessed using standardized tests: CELF-5, WISC-V, and ENFEN. Statistical analyses included descriptive statistics, independent sample t-tests, and Chi-square tests to compare the performance between these two groups. Results: The study revealed significant differences between the two groups in all dimensions assessed. Specifically, children with dyslexia showed markedly lower performance in linguistic, cognitive, and executive functioning measures compared with their peers with typical development. Conclusion: Children with dyslexia present a distinct clinical profile characterized by significant difficulties in language processing, cognition, and executive functions. These challenges interfere with their reading acquisition and academic performance, limiting their integration into educational environments and impacting their overall quality of life.
Technology-Enhanced Language Learning: Subtitling as a Technique to Foster Proficiency and Intercultural Awareness
Field: EDUCATION

Computer-Assisted Language Learning (CALL) is an umbrella term that encompasses diverse technologies with the purpose of enhancing language learning. In the existing literature on CALL, intercultural awareness and the pedagogical use of multimedia products have received less attention. This study explores how the process of creating subtitles for short clips may enhance language skills and intercultural awareness when implemented through lesson plans designed following the framework proposed by the TRADILEX project. A pre-experimental longitudinal design was implemented. The sample consisted of 43 participants who were enrolled in a B2 English course at the University of Córdoba (Spain). During the course, participants consistently attended theoretical sessions. The intervention took place during the practical sessions from February to April 2024, involving four subtitling-based lesson plans on literature and gender. After the intervention, the practical sessions shifted to a traditional, textbook-based format from April to June 2024. The instruments employed to assess the effectiveness of the intervention consisted of a commercial test by MacMillan and the ERI scale on interculturality. The results showed that after the intervention, there was a significant improvement in language proficiency, which increased at a slower rate during the traditional sessions. However, when it comes to intercultural awareness, there was a peak of attainment after the intervention, but attrition rapidly took place. Regarding the pedagogical implications of this study, subtitling could be an appropriate technique that allows contact with the L2 culture and shows positive effects in terms of proficiency.
The Moving Mandala: Exploring the Pro-Social Effects of Musical and Non-Musical Synchrony in Children in a Virtual World
Field: PSYCHOLOGY

Synchronous movement between individuals has been shown to increase pro-sociality, such as closeness and generosity. To date, synchrony research tests these effects using a variety of movement tasks, including musical and non-musical coordination. However, musical versus non-musical synchrony may have separable pro-social effects. To test this, we had 60 children immersed in an augmented reality space called the ‘Moving Mandala’ where they moved asynchronously with only visual cues, synchronously with only visual cues or synchronously with musical and visual cues. We then tested for differences in pro-social effects using sharing and proxemics tasks. Results showed that while the synchrony version of the mandala led to greater closeness in the proxemics task, the musical synchrony led to more pro-sociality on the sharing task. The implications of these findings are discussed.