Search Results (1,176)

Background: This research explores the complex relationship between Attention Deficit Hyperactivity Disorder (ADHD) and language skills, focusing on the impact of the disorder on children’s language development. It is designed as a systematic literature review to synthesize and evaluate existing evidence on this topic. Based on the existing literature, ADHD affects multiple dimensions of language, including phonological awareness, pragmatic comprehension, morphosyntactic structure, narrative skills, and written expression. The difficulties that children with ADHD exhibit at the language level are directly related to their deficits in working memory, attention, and organization, which make it challenging for them to acquire and use language at both educational and social levels. Methods: This study followed the PRISMA methodology, with a systematic selection process across four stages (identification, screening, eligibility, and inclusion). During the identification phase, 475 records were identified (450 from database searches and 25 through reference screening). After screening and applying inclusion criteria, 15 studies met all eligibility requirements and were included in the final synthesis. Results: The present research highlighted the important role that occupational therapists and psychologists can play in the language development of children with ADHD. Strategic interventions to alleviate the language difficulties of children with ADHD are designed to enhance phonological awareness, executive function, speech and language, the use of technological tools, and social skills training. Conclusions: The importance of early diagnosis and implementation of holistic, individualized interventions targeting the language, executive, and social difficulties manifested by children with ADHD is considered influential in addressing the barriers to improving language skills as effectively as possible. Full article

Depression represents a major global health challenge, yet traditional clinical diagnosis faces limitations, including high costs, limited coverage, and low patient willingness. Social media platforms provide new opportunities for early depression screening through user-generated content. However, existing methods often lack systematic integration of clinical knowledge and fail to leverage multi-modal information comprehensively. We propose a DSM-5-guided methodology that systematically maps clinical diagnostic criteria to computable social media features across three modalities: textual semantics (BERT-based deep semantic extraction), behavioral patterns (temporal activity analysis), and topic distributions (LDA-based cognitive bias identification). We design a hierarchical architecture integrating BERT, Bi-LSTM, hierarchical attention, and multi-task learning to capture both character-level and post-level importance while jointly optimizing depression classification, symptom recognition, and severity assessment. Experiments on the WU3D dataset (32,570 users, 2.19 million posts) demonstrate that our model achieves 91.8% F1-score, significantly outperforming baseline methods (BERT: 85.6%, TextCNN: 78.6%, and SVM: 72.1%) and large language models (GPT-4 few-shot: 86.9%). Ablation studies confirm that each component contributes meaningfully with synergistic effects. The model provides interpretable predictions through attention visualization and outputs fine-grained symptom assessments aligned with DSM-5 criteria. With low computational cost (~50 ms inference time), local deployability, and superior privacy protection, our approach offers significant practical value for large-scale mental health screening applications. This work demonstrates that domain-specialized methods with explicit clinical knowledge integration remain highly competitive in the era of general-purpose large language models. Full article

Body fluid identification at crime scenes is the first step in the forensic biology workflow, leading to the identification of the perpetrator and/or, in some cases, the victim. Current methods that are regularly used in forensic criminal evidence analysis utilize well-studied properties of each fluid as the foundation of the protocol. Among these approaches, alternative light sources, chemical reactions, lateral flow immunochromatographic tests, and microscopic detection stand out to identify the main body fluids encountered at crime scenes: blood, semen, and saliva. However, these often come with limits for specificity and sensitivity. There is also difficulty with fluid mixtures, environmental degradation, and destruction of the sample by the method used. Other fluids, like vaginal fluid and fecal matter, lack standardized protocols and require innovative ideas for accurate analysis without compromising the sample. Emerging technologies based on molecular methods have been the focus of body fluid research, with emphasis on topics such as mRNA, microRNA, epigenetics, and microbial analysis. Additional information alongside the determination of fluid origin could be an advantage from new molecular techniques, such as the identification of donors from SNP analysis, if regular STR analysis is not possible. Validation studies and the integration of such research have the potential to expand and enhance the laboratory practices of forensic science. This article will provide an overview of the current methods applied in the crime lab for body fluid identification before exploring active research in this field, pointing out the potential of these techniques for application in forensic cases to overcome present issues and expand the variety of body fluids identified. Full article

Machine learning (ML) algorithms are reshaping academic research. However, there is a lack of systematic impact analysis in specific domains. We propose a framework for evaluating the knowledge landscape of domain-specific ML research. It consists of three key components: LDA (Latent Dirichlet Allocation) for topic identification, co-occurrence network construction, and influential algorithm scoring using centrality metrics. In a case study on COVID-19 research, we analyze 30,664 ML-related papers. We identify 13 research topics. We construct a symmetric undirected network to quantify algorithm influence. This analysis employs six centrality metrics: mention frequency, weighted degree, degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality. Results were obtained following linear normalisation. The framework highlights the top ten most influential algorithms for each topic. It reveals the evolving impact of algorithms in COVID-19 research. The methodology is adaptable to other domains. It provides a systematic approach to understanding ML domain-specific impact. Full article

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics. Full article

Background: Eating disorders (EDs) are important mental illnesses that are often associated with depression and anxiety, leading to significant negative consequences. However, research on this topic in Saudi Arabia remains limited. This study aims to examine the risk of EDs among male and female medical students at King Saud University (KSU) and assess their risk factors and association with anxiety and depression. Methods: A cross-sectional study involving 425 participants was conducted, using a convenience sampling method. The study tools consisted of a questionnaire developed by the research team, the Eating Attitudes Test-26 (EAT-26), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 scale (GAD-7). Results: Almost half (49.6%) were classified as high risk for EDs. Obesity was much higher among high-risk students than low-risk students (p < 0.001). Anxiety and depression were greater among high-risk students than low-risk ones. A higher body mass index (BMI) and depression greatly increased the risk of EDs (p < 0.001). Conclusions: The findings support the notion that medical students have a significant likelihood of developing EDs, especially if they have a high BMI and are depressed. The results show the importance of early identification and offering appropriate interventions to this vulnerable group. Full article

Background/Objectives: Telemedicine has emerged as a pivotal approach to improving access to diabetic retinopathy (DR) screening, diagnosis, management, and monitoring. Over the past two decades, rapid advancements in digital imaging, mobile health technologies, and artificial intelligence have substantially expanded the role of teleophthalmology in DR, resulting in a large volume of pertinent publications. This study aimed to provide a scientific overview of telemedicine applied to DR through bibliometric analysis. Methods: A search of the Web of Science Core Collection was conducted on 15 November 2025 to identify English-language original research and review articles regarding telemedicine for DR. Bibliographic data from relevant publications were extracted and underwent quantitative analysis and visualization using the tools Bibliometrix and VOSviewer. Results: A total of 515 articles published between 1998 and 2025 were included in our analysis. During this period, the research field of telemedicine for DR exhibited an annual growth rate of 13.14%, with publication activity markedly increasing after 2010 and peaking in 2020–2021. Based on the number of publications, United States, China, and Australia were the most productive countries, while Telemedicine and e-Health, Journal of Telemedicine and Telecare, and British Journal of Ophthalmology were the most relevant journals in the field. Keyword co-occurrence analysis revealed three major thematic clusters within the broader topic of telemedicine and DR, namely, public health-oriented work, telehealth service models, and applications of artificial intelligence technologies. Conclusions: The role of telemedicine in DR detection and care represents an expanding multidisciplinary field of research supported by contributions from multiple authors and institutions worldwide. As technological capabilities continue to evolve, ongoing innovation and cross-domain collaboration could further advance the applications of teleophthalmology for DR, promoting more accessible, efficient, and equitable identification and management of this condition. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

Driven by the urgent need for industrial transformation and emerging technologies, the construction engineering market is rapidly evolving toward intelligent building systems. This study employs latent Dirichlet allocation (LDA) methodology to analyze 474 blockchain-related research abstracts from Web of Science and Scopus databases, identifying eight key research topics: (1) industry adoption and implementation challenges; (2) smart contracts and payment mechanisms; (3) emerging technologies and digital transformation; (4) construction supply chain integration and optimization; (5) building modeling and technology integration; (6) modular integrated construction (MIC) applications; (7) project data and security management; and (8) construction industry sustainability and circular economy (CE). Using the autoregressive integrated moving average (ARIMA) model, the study forecasts trends for the top three research topics over the next 36 months. The results indicate strong positive growth trajectories for industry adoption and implementation challenges (Topic 1) and project data and security management (Topic 7), while emerging technologies and digital transformation (Topic 3) demonstrate sustained growth. This study offers a thorough examination of the present landscape and emerging research trends of blockchain in construction, and establishes an overall framework to comprehensively summarize its research and application in the construction industry. The results provide actionable insights for both practitioners and researchers, facilitating a deeper understanding of blockchain’s evolution and implementation prospects, and supporting the advancement of innovation within the industry. Full article

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system. Full article

Substance-based medical devices (SBMDs) are increasingly used in wound care due to their favorable safety profile, physicochemical mechanisms of action, and therapeutic effectiveness. These products often incorporate biopolymers such as hyaluronic acid or chitosan, alone or in combination with antimicrobial agents like silver nanoparticles (AgNPs) or silver sulfadiazine (SSD), offering hydration, tissue protection, and control of microbial burden in both acute and chronic wounds. Despite their widespread clinical use, the regulatory classification of SBMDs under Regulation (EU) 2017/745 (MDR) remains one of the most challenging and debated areas within the current European framework. This review analyzes the scientific and regulatory context of topical SBMDs, with particular emphasis on borderline products that share similarities with medicinal products in terms of formulation, composition, or claimed effects. The discussion focuses on the application of MDR Annex VIII, specifically Rule 21 for substance-based devices and Rule 14 for devices incorporating medicinal substances with ancillary action, together with interpretative guidance provided by MDCG 2022-5 Rev.1 and the Association of the European Self-Care Industry (AESGP) Position Paper. Particular attention is given to the identification of the critical role of the primary mode of action (MoA) as the determining criterion for regulatory qualification, especially for products containing antimicrobial substances. Through selected examples and case analyses, the review highlights inconsistencies in classification across Member States and underscores the need for a more harmonized, evidence-based, and proportionate regulatory approach. Overall, SBMDs challenge traditional regulatory boundaries and call for a framework capable of accommodating complex, multifunctional products while ensuring patient safety and regulatory coherence. Full article

Exploring the design of beneficial nonlinear restoring force structures has become a highly popular topic due to their extensive applications in energy harvesting, actuation, energy absorption, robotics, etc. However, the current literature lacks a systematic review and classification that addresses the design, modeling, and parameter identification of nonlinear restoring forces. Thus, the present paper provides a thorough examination of the latest advancements in the design of nonlinear restoring forces, as well as modeling and parameter identification in contemporary beneficial nonlinear designs. The seven design methodologies, namely magnetic coupling, oblique spring linkages, static or dynamic preloading, metamaterials, bio-inspired, MEMS (Micro-Electromechanical Systems) manufacturing, and dry friction applied approaches, are classified. The polynomial, hysteretic, and piecewise linear models are summarized for nonlinear restoring force characterization. The system parameter identification methods covering restoring force surface, Hilbert transform, time-frequency analysis, nonlinear subspace identification, unscented Kalman filter, optimization algorithms, physics-informed neural networks, and data-driven sparse regression are reviewed. Moreover, possible enhancement strategies for nonlinear system identification of nonlinear restoring forces are presented. Finally, broader implications and future directions for the design, characterization, and identification of nonlinear restoring forces are discussed. Full article

Background/Objectives: Violence against older adults is a rising public health issue. Though older adults may not openly disclose such experiences, they are often willing to discuss them when given the opportunity. Healthcare providers in hospital settings can play a crucial role in the early identification and care. However, effective screening and response require comprehensive guidance. Methods: A pilot, multicentric feasibility study with a single-group intervention was implemented at three Belgian geriatric departments. The aim was to assess the feasibility and acceptability of a new guide for identifying older adults (≥75 years), without major cognitive deficits, who have experienced violence, in order to subsequently provide them with adequate care. Admitted older adults were screened using the guidance, and healthcare providers who conducted the screenings completed questionnaires to evaluate their feasibility and acceptability. The Trial is registered in Clinicaltrials.gov [NCT06780540]. Results: A total of 104 admitted older adults (mean age: 83 years) were recruited across two Dutch-speaking and one French-speaking hospital in Belgium. One in five participants (20.2%) disclosed experiences of violence, either recent or throughout their lives. Healthcare providers (n = 12) positively evaluated the guidance, suggesting improvements in question formulation, protocol adaptability, and the need for further training. Conclusions: This guidance is feasible, acceptable, and holds potential for improving disclosure rates. To ensure the provision of appropriate and equitable care, it is essential to first equip healthcare providers with education and training on this topic. Future interventional research is required to implement the guide on a larger scale and to measure health-related outcomes. Full article

Many countries around the world require some or all schools to develop and implement a school improvement plan (SIP), which is a tool intended to guide the identification of school-specific needs for improvement along with a series of priorities, goals, and actions to help address those needs. Yet, the literature on this topic remains rather sparse. In this article, we conducted a systematic review of the international literature on SIPs published from 2010 through 2025, identifying 62 relevant articles for analysis. We organized this review’s findings around six areas related to SIP development and implementation: assessing current conditions, determining needs, setting direction, organizing resources, taking action, and evaluating progress. Findings suggest that while divergences exist between contexts with respect to these six areas, there are considerable convergences in how educators and others conceptualize and interact with SIPs. We close with recommendations for future research that both strengthens and broadens the extant literature on SIPs. Full article

The global transition to renewable energy sources is essential to carbon neutrality and ensuring energy security. First, the paper presents a comprehensive literature review of the main technological breakthroughs in bioenergy, hydro energy, solar energy, onshore and offshore wind energy, ocean energy, and geothermal energy, selecting the latest papers published. Next, key scientific challenges, environmental and economic constraints, and future research priorities for each of the six renewable energies were outlined. Then, to emphasize the important contribution of renewable energies to total energy production and the proportions of each type of renewable energy, the evolution of global electricity generation from all six renewable sources between 2000 and 2023 was analyzed. Thus, in 2023, the global electricity generation weight of each renewable energy in total renewable energy ranks hydro energy (47.83%) first, followed by onshore and offshore wind energy (25.8%), solar energy (18.19%), bioenergy (7.07%), geothermal energy (1.1%), and ocean energy (0.01%). After that, the bibliometric analysis, conducted between 1 January 2021 and 1 October 2025 on the Web of Science (WoS) database and using the PRISMA approach and VOSviewer version 1.6.20 software, enabled the identification of the most cited papers, publications and citation number by WoS categories, topics, correlation with Sustainable Development Goals, authors’ affiliation, publication title, and publisher. Furthermore, the paper presents a network visualization of the link between co-occurrences and all keywords, imposing minimum thresholds of 10, 20, and 30 occurrences per keyword, and computes the network density based on the number of edges and nodes. Finally, additional analysis included the most used keywords in different co-occurrences, a word cloud of occurrences by total link strength, regression of occurrences versus total link strength, and correlations between citations and documents and between citations and authors. Carbon neutrality and a resilient energy future can only be achieved by integrating renewable sources into hybrid systems and optimized smart grids. Each technological progress stage will bring new challenges that must be addressed cost-effectively. Full article