mdpi-ai/citelens huggingface dataset:
Risk Assessment Profiles for Caregiver Burden in Family Caregivers of Persons Living with Alzheimer’s Disease: An Exploratory Study with Machine Learning
Field: PSYCHOLOGY

Alzheimer’s disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them.
Creativity and Preservice Teachers: A Literature Review of an Underexplored Field (2014–2024)
Field: EDUCATION

This systematic literature review examines the relationship between creativity and preservice teachers in scientific publications from 2014 to 2024. Using the PRISMA methodology, 27 empirical articles were selected based on their relevance to the research focus. The study provides both a bibliometric overview of the field and a substantive analysis of existing knowledge. Key findings reveal significant dispersion within the field, a proliferation of diverse definitions of creativity, and limited attention to the specific characteristics of preservice teachers in the research. Four central themes emerged: beliefs about creativity, personal characteristics, the creative processes, and teaching for creativity. These themes highlight the fragmented yet evolving nature of the discourse. The paper underscores the necessity of more comprehensive research approaches that transcend methodological individualism and better capture the domain-specific nature of creativity in preservice teachers. By integrating these perspectives, the study aims to advance a more cohesive understanding of how creativity can be cultivated in teacher preparation.
FedBirdAg: A Low-Energy Federated Learning Platform for Bird Detection with Wireless Smart Cameras in Agriculture 4.0
Field: AI

Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital agriculture has advanced these efforts through Internet of Things (IoT) and artificial intelligence (AI) technologies. With recent advancements in hardware and processing chips, connected devices can now utilize deep convolutional neural networks (CNNs) for on-field image classification. However, training these models can be energy-intensive, especially when large amounts of data, such as images, need to be transmitted for centralized model training. Federated learning (FL) offers a solution by enabling local training on edge devices, reducing data transmission costs and energy demands while also preserving data privacy and achieving shared model knowledge across connected devices. This paper proposes a low-energy federated learning framework for a compact smart camera network designed to perform simple image classification for bird detection in crop fields. The results demonstrate that this decentralized approach achieves performance comparable to a centrally trained model while consuming at least 8 times less energy. Further efficiency improvements, with a minimal tradeoff in performance reduction, are explored through early stopping.
Adenoid Cystic Carcinoma of the Breast: A Narrative Review and Update on Management
Field: ONCOLOGY

Rare breast malignancies represent a challenge for treatment decision making given the lack of evidence-based guidelines. As a particularly uncommon tumor, adenoid cystic carcinomas are especially challenging. Although, histopathologically, they share the same tumor molecular profile as hormone receptor-negative and HER2 nonamplified carcinomas with aggressive physiology, adenoid cystic carcinomas generally have a favorable prognosis. Thus, there is evidence to suggest that more aggressive treatment regimens may not provide better therapeutic effects. In this review, we discuss ACCB with the goal of highlighting pathophysiology, clinical features, and treatment strategies. Existing data support consideration for adjuvant radiation with the omission of axillary staging and systemic therapies.
A socially prescribed creative play intervention for new parents: investigating post traumatic stress around birth and changes in postnatal depression and reflective function
Field: PSYCHOLOGY

Background: Parenthood is a key transition period which involve emotional, social and physical adjustments. Social prescribing is a method that connects people to community-based activities, groups, and services to addressing various needs impacting their health and wellbeing. This pilot investigation aimed to assess whether a curated socially prescribed creative play programme would impact upon new parents’ social connection, mental health and reflective function through a programme designed to support these changes. Methods: This study was part of a 5-week long socially prescribed creative play programme at a family theatre company in the North of England, aimed at providing social capital to families while teaching creative play. In total, 57 parents (M = 30.73, SD = 6.20) completed baseline and post-intervention measures of birth trauma experiences (City Birth Trauma Scale), postnatal depression (Edinburgh Postnatal Depression Scale) reflective function (Reflective Functioning Questionnaire), and qualitative, open-ended questions on social opportunities. Descriptive analyses were completed using t-tests and chi-square tests, while repeated measures ANOVAs were used to answer questions around the main analyses. Results: The participants experienced a statistically significant reduction in postnatal depression scores following the intervention, but no changes were found in reflective function or birth trauma scores; secondly, birth trauma scores predicted later depression scores as well as reflective functioning uncertainty scores (but not certainty scores). Qualitative analysis found social opportunities were not why parents came but was, after attending, their favourite part of the socially prescribed programme. Those parents reporting on social opportunities were more likely to reference their own needs while non-social activities were associated with their child’s needs. Conclusions: Socially prescribed creative play programmes for new parents could be a “waiting well” intervention. A longer duration and trauma informed focus would need to be considered in future cohorts.
SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine
Field: AI

With the rise of e-commerce and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions to assist with any task under consideration. While various machine learning algorithms have been developed for recommendation tasks, existing systems still face limitations. This research focuses on advancing context-aware recommendation sytems by leveraging the capabilities of Large Language Models (LLMs) in conjunction with real-time data. The research exploits the integration of existing real-time data APIs with LLMs to enhance the capabilities of the recommendation systems already integrated into smart societies. The experimental results demonstrate that the hybrid approach significantly improves the user experience and recommendation quality, ensuring more relevant and dynamic suggestions.
Adapting to crisis and unveiling the digital shift: a systematic literature review of digital competence in education related to COVID-19
Field: EDUCATION

Nowadays, with technology penetrating into every aspect of our life, the ways to acquire knowledge has been greatly revolutionized. The outbreak of the Coronavirus (COVID-19) has accelerated digital informatization in education and the educational model has been transformed substantially. The demand for digital competence is at record high. The purpose of this study is to systematically explore digital competence in different national educational contexts during the COVID-19 pandemic (2019–2021), to provide academics with the current state of digital competence in education and main research trends in digital competence in education during this period, elucidate the impact of pandemic on digital competence, and explore the limitations in the implementation of digital competence in educational research. The results indicate that most research on digital competence in educational contexts related to COVID-19 focused on the current state of the digital competence of teachers and students, especially those in higher education and formal learning context. Still, with the situation compounded, the researchers furthered their study by investigating the factors that influenced digital competence in order to address educational challenges in a pandemic context. In addition, teachers and students were still not well equipped as for digital competence though their digital awareness and digital readiness in the teaching and learning process increased. It is recommended to promote and enhance digital competence training in order to improve students’ achievement and the quality of education.
Client Violence Against Educational Workers: A Systematic Review
Field: EDUCATION

Client-initiated workplace violence in educational settings is a global issue affecting both teaching and non-teaching employees, such as instructional assistants, counselors, and administrators, among other school workers. Although studies on violence in educational settings have primarily focused on students, there has been growing interest in examining violence against teachers and, more recently, against teaching assistants and other educational professionals. This systematic review aims to analyze studies from diverse educational settings to examine the characteristics, causes, effects, and coping strategies associated with violence perpetrated by students, parents, or guardians, with the goal of informing and advancing prevention strategies. Following the PRISMA 2020 guidelines, a systematic literature review was conducted, analyzing studies across various educational environments to examine the characteristics, causes, effects, and coping strategies of violence perpetrated by students, parents, or guardians. This review revealed a significant prevalence of physical, psychological, and verbal assaults. However, most studies originated from Anglo-Saxon contexts, limiting their generalizability to diverse cultural and educational settings. The lack of research in other languages and in underrepresented regions highlights critical gaps in understanding this issue globally. The revision conclude that workplace violence in educational settings demands urgent and comprehensive responses involving all stakeholders. Implementing targeted prevention strategies and fostering a culture of respect are essential to ensure safe and healthy learning environments.
One-Shot Autoregressive Generation of Combinatorial Optimization Solutions Based on the Large Language Model Architecture and Learning Algorithms
Field: AI

Large Language Models (LLMs) have immensely advanced the field of Artificial Intelligence (AI), with recent models being able to perform chain-of-thought reasoning and solve complex mathematical problems, ranging from theorem proving to ones involving advanced calculus. The success of LLMs derives from a combination of the Transformer architecture with its attention mechanism, the autoregressive training methodology with masked attention, and the alignment fine-tuning via reinforcement learning algorithms. In this research, we attempt to explore a possible solution to the fundamental NP-hard problem of combinatorial optimization, in particular, the Traveling Salesman Problem (TSP), by following the LLM approach in terms of the architecture and training algorithms. Similar to the LLM design, which is trained in an autoregressive manner to predict the next token, our model is trained to predict the next node in a TSP graph. After the model is trained on random TSP graphs with known near-optimal solutions, we fine-tune the model using Direct Preference Optimization (DPO). The tour generation in a trained model is autoregressive one-step generation with no need for iterative refinement. Our results are very promising and indicate that, for TSP graphs up to 100 nodes, a relatively small amount of training data yield solutions within a few percent of the optimal. This optimization improves if more data are used to train the model.
Understanding the Use of Social and Emotional Learning in Elementary Schools: A Theory of Planned Behaviour Perspective
Field: PSYCHOLOGY

Research has demonstrated that social–emotional learning (SEL) positively influences myriad domains of children’s development. However, the underlying mechanisms influencing teachers’ adoption of SEL remain underexplored. Guided by the Theory of Planned Behaviour (TPB), this quantitative cross-sectional study sought to elucidate the factors that motivate teachers to adopt SEL teaching practices. The study’s sample included 166 volunteer teachers in Luxembourg, recruited as part of a nationwide educational survey. Of these, 82.5% were women. Participants were recruited through convenience sampling, ensuring diversity in socio-economic backgrounds, grade levels, and student needs. Although these findings are based on self-reported data, they offer novel insights by quantifying teachers’ engagement with SEL, with over 50% already implementing related activities. Structural equation modelling shows that the TPB model accounted for 49% of the variance in teachers’ intentions and 44% of the variance in the adoption of SEL practices. Higher intention and self-efficacy predicted more frequent SEL implementation. Teachers with positive SEL attitudes and higher self-efficacy showed greater intention to implement SEL. These findings underscore the significance of cultivating positive attitudes and self-efficacy to facilitate the effective implementation of SEL in educational settings. The role of teacher gender and audience, as well as implications for teaching, professional development, and SEL research, are discussed.
Enhancing structured data generation with GPT-4o evaluating prompt efficiency across prompt styles
Field: AI

Large language models (LLMs), such as GPT-4o, provide versatile techniques for generating and formatting structured data. However, prompt style plays a critical role in determining the accuracy, efficiency, and token cost of the generated outputs. This paper explores the effectiveness of three specific prompt styles–JSON, YAML, and Hybrid CSV/Prefix–for structured data generation across diverse applications. We focus on scenarios such as personal stories, receipts, and medical records, using randomized datasets to evaluate each prompt style's impact. Our analysis examines these prompt styles across three key metrics: accuracy in preserving data attributes, token cost associated with output generation, and processing time required for completion. By incorporating structured validation and comparative analysis, we ensure precise evaluation of each prompt style's performance. Results are visualized through metrics-based comparisons, such as Prompt Style vs. Accuracy, Prompt Style vs. Token Cost, and Prompt Style vs. Processing Time. Our findings reveal trade-offs between prompt style complexity and performance, with JSON providing high accuracy for complex data, YAML offering a balance between readability and efficiency, and Hybrid CSV/Prefix excelling in token and time efficiency for flat data structures. This paper explores the pros and cons of applying the GPT-4o LLM to generate structured data. It also provides practical recommendations for selecting prompt styles tailored to specific requirements, such as data integrity, cost-effectiveness, and real-time processing needs. Our findings contribute to research on how prompt engineering can optimize structured data generation for AI-driven applications, as well as documenting limitations that motivate future work needed to improve LLMs for complex tasks.
Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: the mediating role of confidence and AI readiness
Field: PSYCHOLOGY

This study aims to explore the mediating role of confidence and artificial intelligence (AI) readiness in university teachers’ behavioral intention to adopt AI technology, providing empirical support for enhancing teachers’ willingness to use AI technology from both theoretical and practical perspectives. This study used a random sampling method to conduct an online survey of 504 university teachers, assessing the impact of subjective norms on behavioral intention. The survey included scales for subjective norms, confidence, AI readiness, and behavioral intention. Data analysis was performed using AMOS 26, SPSS Statistics 27 software and Model 6 from the PROCESS 4.0 plugin, aiming to investigate the mediating role of confidence and AI readiness between subjective norms and behavioral intention. Subjective norms were found to have a significant positive correlation with behavioral intention. Subjective norms indirectly influenced behavioral intention through confidence or AI readiness. Confidence and AI readiness played a chain-mediating role in the relationship between subjective norms and behavioral intention (β = 0.0324, 95% CI: [0.0129, 0.0551]), accounting for 12.87% of the total effect. This study reveals the positive role of subjective norms in university teachers’ behavioral intention to adopt AI technology, indicating that subjective norms not only directly enhance behavioral intention but also exert indirect effects through both single and chain mediation of confidence and AI readiness. The findings highlight the critical role of confidence and AI readiness in the relationship between subjective norms and behavioral intention, suggesting that to effectively increase university teachers’ willingness to use AI technology, it is important to focus on improving their confidence in and readiness for AI technology, thereby strengthening the positive impact of subjective norms.
Voice-AttentionNet: Voice-Based Multi-Disease Detection with Lightweight Attention-Based Temporal Convolutional Neural Network
Field: AI

Voice data contain a wealth of temporal and spectral information and can be a valuable resource for disease classification. However, traditional methods are often not effective in capturing the key features required for the classification of multiple disease classes. To address this challenge, we propose a voice-based multi-disease detection approach with a lightweight attention-based temporal convolution neural network (Voice-AttentionNet) designed to analyze speech data for multi-class disease classification. Our model utilizes the temporal convolution neural network (CNN) architecture to extract high-resolution temporal features, while incorporating attention mechanisms to highlight disease-related patterns. Extensive experiments have been conducted on our dataset, including speech samples from patients with multiple illnesses. The results show that our method achieves the most advanced performance with an average classification accuracy of 91.61% on six datasets and is superior to the existing classical models. These findings highlight the potential of combining attention mechanisms with temporal CNNs in the use of speech data for disease classification. Moreover, this study provides a promising direction for deploying AI-driven diagnostic tools in clinical scenarios.
Analyzing handwriting legibility through hand kinematics
Field: AI

Introduction: Handwriting is a complex skill that requires coordination between human motor system, sensory perception, cognitive processing, memory retrieval, and linguistic proficiency. Various aspects of hand and stylus kinematics can affect the legibility of a handwritten text. Assessing handwriting legibility is challenging due to variations in experts' cultural and academic backgrounds, which introduce subjectivity biases in evaluations.Methods: In this paper, we utilize a deep-learning model to analyze kinematic features influencing the legibility of handwriting based on temporal convolutional networks (TCN). Fifty subjects are recruited to complete a 26-word paragraph handwriting task, designed to include all possible orthographic combinations of Arabic characters, during which the hand and stylus movements are recorded. A total of 117 different spatiotemporal features are recorded, and the data collected are used to train the model. Shapley values are used to determine the important hand and stylus kinematics features toward evaluating legibility. Three experts are recruited to label the produced text into different legibility scores. Statistical analysis of the top 6 features is conducted to investigate the differences between features associated with high and low legibility scores.Results: Although the model trained on stylus kinematics features demonstrates relatively high accuracy (around 76%), where the number of legibility classes can vary between 7 and 8 depending on the expert, the addition of hand kinematics features significantly increases the model accuracy by approximately 10%. Explainability analysis revealed that pressure variability, pen slant (altitude, azimuth), and hand speed components are the most prominent for evaluating legibility across the three experts.Discussion: The model learns meaningful stylus and hand kinematics features associated with the legibility of handwriting. The hand kinematics features are important for accurate assessment of handwriting legibility. The proposed approach can be used in handwriting learning tools for personalized handwriting skill acquisition as well as for pathology detection and rehabilitation.