Search Results (481)

Background: End-stage renal disease (ESRD) presents a substantial and growing global health challenge, where hemodialysis serves as an essential life-sustaining therapy for countless individuals. Despite its physiological necessity, the demanding treatment regimen can profoundly impact mental health and overall well-being, though gender-specific data and correlates within the Saudi population remain insufficiently explored. Methods: This cross-sectional study aimed to investigate this gap by assessing the prevalence of anxiety and depression, evaluating health-related quality of life (HRQoL), and analyzing associations with gender and treatment duration in a cohort of 250 hemodialysis patients from multiple centers in Madinah, Saudi Arabia. Validated instruments, namely, the Hospital Anxiety and Depression Scale (HADS) and the 36-Item Short Form Health Survey (SF-36), were employed. Results: The findings revealed a significant psychological burden, with 38% of patients exhibiting anxiety and 32% depression, with females disproportionately affected. HRQoL scores were severely diminished across all domains compared to healthy population norms. Furthermore, a longer dialysis vintage demonstrated a significant positive correlation with worsening psychological scores and a decline in physical HRQoL. Conclusions: These results underscore the critical need for a paradigm shift in standard care, advocating for the systematic integration of routine mental health screenings and the development of tailored, gender-sensitive psychosocial interventions to mitigate this considerable burden. Full article

The high spatiotemporal resolution of optically pumped magnetometers (OPMs) makes them an essential tool for functional brain imaging, enabling accurate recordings of neuronal activity. However, physiological signals such as eye blinks and cardiac activity overlap with neural magnetic signals in the frequency domain, resulting in contamination and creating challenges for the observation of brain activity and the study of neurological disorders. To address this problem, an automatic physiological artifact removal method based on OPM magnetic reference signals and a channel attention mechanism is proposed. The randomized dependence coefficient (RDC) is employed to evaluate the correlation between independent components and reference signals, enabling reliable recognition of artifact components and the construction of training and testing datasets. A channel attention mechanism is subsequently introduced, which fuses features from global average pooling (GAP) and global max pooling (GMP) layers through convolution to establish a data-driven automatic recognition model. The backbone network is further optimized to enhance performance. Experimental results demonstrate a strong correlation between the magnetic reference signals and artifact components, confirming the reliability of magnetic signals as artifact references for OPM-MEG. The proposed model achieves an artifact recognition accuracy of 98.52% and a macro-average score of 98.15%. After artifact removal, both the event-related field (ERF) responses and the signal-to-noise ratio (SNR) are significantly improved. Leveraging the flexible and modular characteristics of OPM-MEG, this study introduces an artifact recognition framework that integrates magnetic reference signals with an attention mechanism. This approach enables highly accurate automatic recognition and removal of OPM-MEG artifacts, paving the way for real-time, automated data analysis in both scientific research and clinical applications. Full article

Aim: As the global population ages, the number of bilingual individuals living with dementia is increasing, yet their communication needs remain underrepresented in both clinical practice and research. This evidence review examines the intersection of language regression, communication challenges, and cultural–linguistic identity in bilingual dementia, with a particular focus on the role of speech–language pathologists (SLPs). Methods: Twelve peer-reviewed studies were critically reviewed and thematically analysed across four domains: (1) language regression and retention in bilingual dementia, (2) communication challenges in bilingual dementia care, (3) the marginal role of speech–language pathology, and (4) cultural–linguistic identity and health equity. The included studies span clinical case reports, experimental research, qualitative caregiver studies, and systematic reviews, with bilingual populations across Asia, Europe, North America, and the Middle East. Results: Findings reveal that language deterioration in bilingual dementia is dynamic and highly individualised, often influenced by language history, emotional context, and usage patterns. Caregivers and clinicians face persistent communication breakdowns, particularly in linguistically mismatched settings. Despite their specialised expertise in communication, SLPs remain largely peripheral in dementia care, constrained by systemic, educational, and methodological barriers. Moreover, linguistic and cultural identity play a critical role in how dementia is experienced and managed, yet are rarely integrated into care frameworks. Conclusions: This review highlights a significant knowledge–practice gap in bilingual dementia care and underscores the need to embed culturally and linguistically responsive communication practices, especially through speech–language therapy, at the centre of bilingual dementia care and support. It outlines key research and practice directions to advance equity, accuracy, and relational care in this growing population. Full article

This theoretical article examines how belongingness, defined as the sense that one’s participation is legitimate and valued, interacts with cultural value orientations to help explain persistent disparities in U.S. technology engagement, including emerging technologies, across racial and ethnic groups. While structural barriers (e.g., racism, poverty, linguistic bias, etc.) remain essential to understanding such inequity, we argue that engagement patterns in technology also reflect how different cultural communities may define and experience belongingness in relation to digital domains. Drawing on Triandis and Gelfand’s framework, and focusing specifically on educational contexts, we propose the Belongingness through Cultural Value Alignment (BCVA) model, whereby belongingness serves as a catalyst between cultural value orientations and technology engagement, with vertical collectivism deriving belongingness primarily through structured skill development and validation while horizontal collectivism focusing instead on belonging based on community integration. When technological environments value practices that are consistent with vertical collectivist norms, individuals from horizontal collectivist cultures may experience cultural misalignment not from disinterest in technology or exclusionary efforts but, instead, because dominant engagement modes conflict with their familiar frameworks for fostering a sense of belonging. By examining how cultural value orientations mediate the sense of belonging in contexts involving modern technologies, the proposed perspective offers a novel framework for understanding why access alone may have proven insufficient to address technological participation gaps, and suggests directions for creating technology spaces where individuals from a wider range of communities can experience the authentic sense of belonging. Full article

Mineral prospecting from vast geological text corpora is impeded by challenges in domain-specific semantic interpretation and knowledge synthesis. General-purpose Large Language Models (LLMs) struggle to parse the complex lexicon and relational semantics of geological texts, limiting their utility for constructing precise knowledge graphs (KGs). Our novel framework addresses this gap by integrating a domain-specific LLM, GeoGPT, with a lightweight retrieval-augmented generation architecture, LightRAG. Within this framework, GeoGPT automates the construction of a high-quality mineral-prospecting KG by performing ontology definition, entity recognition, and relation extraction. The LightRAG component then leverages this KG to power a specialized geological question-answering (Q&A) system featuring a dual-layer retrieval mechanism for enhanced precision and an incremental update capability for dynamic knowledge incorporation. The results indicate that the proposed method achieves a mean F1-score of 0.835 for entity extraction, representing a 17% to 25% performance improvement over general-purpose large models using generic prompts. Furthermore, the geological Q&A model, built upon the LightRAG framework with GeoGPT as its core, demonstrates a superior win rate against the DeepSeek-V3 and Qwen2.5-72B general-purpose large models by 8–29% in the geochemistry domain and 53–78% in the remote sensing geology domain. This study establishes an effective and scalable methodology for intelligent geological text analysis, enabling lightweight, high-performance Q&A systems that accelerate knowledge discovery in mineral exploration. Full article

Background: Pre-retrofit auditing and pre-demolition auditing (PRA/PDA) are important in material reuse, waste reduction, and regulatory compliance in the building sector. An emphasis on sustainable construction practices has led to a higher requirement for PRA/PDA. However, traditional auditing processes demand substantial time and manual effort and are more easily to create human errors. As a developing technology, artificial intelligence (AI) can potentially assist PRA/PDA processes. Objectives: This scoping review aims to review the potential of AI in assisting each sub-stage of PRA/PDA processes. Eligibility Criteria and Sources of Evidence: Included sources were English-language articles, books, and conference papers published before 31 March 2025, available electronically, and focused on AI applications in PRA/PDA or related sub-processes involving structured elements of buildings. Databases searched included ScienceDirect, IEEE Xplorer, Google Scholar, Scopus, Elsevier, and Springer. Results: The review indicates that although AI has the potential to be applied across multiple PRA/PDA sub-stages, actual application is still limited. AI integration has been most prevalent in floor plan recognition and material detection, where deep learning and computer vision models achieved notable accuracies. However, other sub-stages—such as operation and maintenance document analysis, object detection, volume estimation, and automated report generation—remain underexplored, with no PRA/PDA specific AI models identified. These gaps highlight the uneven distribution of AI adoption, with performance varying greatly depending on data quality, available domain-specific datasets, and the complexity of integration into existing workflows. Conclusions: Out of multiple PRA/PDA sub-stages, AI integration was focused on floor plan recognition and material detection, with deep learning and computer vision models achieving over 90% accuracy. Other stages such as operation and maintenance document analysis, object detection, volume estimation, and report writing, had little to no dedicated AI research. Therefore, although AI demonstrates strong potential in PRA/PDA, particularly for floor plan and material analysis, broader adoption is limited. Future research should target multimodal AI development, real-time deployment, and standardized benchmarking to improve automation and accuracy across all PRA/PDA stages. Full article

Mental health disorders are increasingly being recognized as a major global challenge. In the Arabic-speaking Middle East and North Africa (MENA) region, this challenge is compounded by sociocultural stigma, political instability, and limited mental health infrastructure, all of which restrict access to effective care. While neurostimulation modalities such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) have proven effective and are gaining traction, their use in the MENA region remains limited and underexplored. This narrative review aims to bridge critical gaps by examining knowledge levels, attitudes, perceptions, and the clinical application and accessibility of ECT and rTMS across Arabic-speaking countries. We searched multiple databases using keywords related to neurostimulation and psychiatry, covering all 22 Arabic-speaking MENA countries. Studies were included if they were published in English and were related to psychiatric applications of ECT or rTMS. Findings were categorized by geography and grouped into four thematic domains: knowledge, perception, availability, and clinical use. The findings revealed an uneven distribution of neurostimulation research and services across the region; ECT is more established than rTMS. Additionally, public awareness remains low, and high levels of stigma persist. Among clinicians, psychiatrists tend to support neurostimulation, while general medical staff show mixed opinions. rTMS is gaining clinical interest but remains limited in accessibility due to high costs and limited infrastructure. Although neurostimulation should be more widely implemented in psychiatry in the MENA region, it is still underrecognized and underused. Region-specific strategies addressing stigma, training gaps, and policy standardization are essential to optimize neurostimulation use and its public acceptance. Full article

Flash floods are increasingly frequent and severe, yet standard risk communication messages are often too generic and lack actionable guidance, causing them to be ignored. This research aims to enhance flood risk communication by first, developing a robust flood ontology using a novel semi-automated approach, and second, demonstrating its potential as a semantic foundation for translating complex data into clear, personalized public alerts. We introduce a semi-automated, human-in-the-loop ontology engineering strategy that integrates expert-defined schemas with Large Language Model (LLM)-driven expansion and refinement from authoritative sources. Evaluation results are twofold: (1) Technical metrics confirm our LLM-constructed ontology achieves superior relationship richness and expressiveness compared with existing disaster ontologies. (2) A proof-of-concept case study demonstrates the ontology’s potential by showing how its specific classes and relations (e.g., ‘neededForElderly’ relation linking the class ‘SpecialConsideration’ to ‘ElderlyCommunityMember’) can be used to generate targeted advice like “check on elderly neighbors”, transforming a generic alert into a clear and actionable message. Consequently, this research delivers two key contributions: a replicable and domain-adaptable methodology for semi-automated ontology construction and a practical demonstration of how such an ontology can bridge the critical gap between flood data and public understanding, empowering communities to respond more effectively. Full article

This study investigates urban–remote disparities in the science performance of Taiwanese eighth-grade students, particularly in matter-related domains, using an explanatory–sequential mixed-methods design. For the quantitative phase, we applied differential item functioning (DIF) analysis with Mantel–Haenszel statistics and logistic regression to the TIMSS 2019 science assessment, while in the qualitative phase, we employed think-aloud interviews and the repertory grid technique (RGT) with 12 students (6 urban, 6 remote) to explore cognitive structures. The quantitative phase identified 26 items (12.3% of 211) disadvantaging remote students, with DIF most pronounced in constructed-response formats and matter-related domains: “Composition of Matter”, “Physical States and Changes in Matter”, and “Properties of Matter”. The follow-up qualitative analyses revealed fragmented, associative cognitive structures in remote learners, marked by reliance on observable (macroscopic) properties rather than microscopic explanations, terminological confusion, microscopic gaps, and misconceptions, contrasting with urban students’ hierarchical integration. Triangulation suggests that the observed disparities are linked to experiential constraints, potentially accounted for by hindered micro–macro connections. Our findings suggest that resource inequities may play a role in sustaining certain biases, indicating that targeted measures could help to make science education more inclusive. Based on these results, we tentatively outline possible educational interventions to improve equity in science education. Full article

Background/Objectives: Historically, humankind has consistently regarded death as an uncomfortable topic. Although death and dying are unescapable, they are frequently overlooked in formal education, as discussing or acknowledging them is believed to provoke emotional or psychological discomfort. To the best of our knowledge, little is known about the influence of the fear of death on the lives of university students. To fill this gap, this study aimed to examine the relationship between the concept of a good death, fear of death and self-compassion among university students in Portugal. Methods: This cross-sectional study was conducted in Portugal between November 2024 and January 2025 with 310 university students using an e-survey. Personal questionnaire and the Portuguese versions of the Good Death Concept Scale, the Collett-Lester Fear of Death Scale, and the Self-Compassion Scale were used. JAMOVI statistical software (version 2.7.6.) was used for descriptive analysis, independent sample t-tests, one-way ANOVA with post hoc analysis, and Pearson correlation analysis. To identify the factors associated with fear of death, a multiple linear regression analysis was conducted. This study adhered to the STROBE checklist for reporting. Results: A total of 310 students were included. The average age was 25 ± 8.52 years, and 75.2% were female. The total mean score for fear of death was 99.22 ± 21.97, indicating relatively low fear levels. However, health sciences students presented higher fear of death rates compared with non-health counterparts. Age and gender differences were also found, with female and younger students reporting significantly higher levels of fear of death (p < 0.01). The Pearson correlation matrix indicated that fear of death is positively correlated with the concept of a good death, while negatively correlated with self-compassion (p < 0.01). Key factors influencing fear of death include age, gender, closure and control domains, and the overidentification subscale (adjusted R-Squared valued [R2] = 0.352). Conclusions: The results suggest that students are often poorly prepared to deal with death-related issues (revealing fear) and with negative thoughts and feelings about mortality. In this vein, it is necessary to implement curricular educational interventions focusing on death education as well as actively involving students in compassionate community initiatives, increasing their awareness and self-confidence about EoL care. Full article

Traditional perceptual loss functions, primarily designed for photographic images, often perform poorly in the sketch domain due to significant differences in visual representation. To address this domain gap, we propose an improved perceptual loss specifically designed for sketch images. Our method fine-tunes a pre-trained VGG-16 model on the ImageNet-Sketch dataset while strategically replacing max-pooling layers with spatial and channel attention mechanisms. We comprehensively evaluate our approach across three dimensions: generation quality, sketch retrieval performance, and feature space organization. Experimental results demonstrate consistent improvements across all evaluation metrics, with our method achieving the best generation performance, over 10% improvement in sketch retrieval accuracy, and 6-fold improvement in class separability compared to baseline methods. The ablation studies confirm that both fine-tuning and attention mechanisms are essential components that work together effectively. Our domain-specific perceptual loss effectively bridges the gap between photographic and sketch domains, providing enhanced performance for various sketch-related computer vision applications, including generation, retrieval, and recognition. Full article

Social workers are required to have the capacity to effectively support welfare recipients to restore their labor participation for social inclusion. However, a systematic method for process evaluation of this capacity has not yet been established. In this study, we developed the Index of Social Work Process in Promoting Social Participation of Welfare Recipients (SWP-PSP) to address this gap. Item domains and pools were prepared by referring to existing social work guidelines and human capital management theories, and content and face validity were confirmed by an expert panel review. The initial 75 items were revised to 44. We conducted a cross-sectional survey with 139 social workers working in public livelihood support at various municipal authorities in Japan. Item response theory analysis was performed for item selection, followed by the criterion-related validity test for convergent validation using Utrecht Work Engagement (UWE) scale scores as a reference. The selected 20 items with four domains were moderately correlated with UWE scores (Pearson’s correlation coefficient r = 0.35). Certified social workers demonstrated a stronger correlation with UWE (r = 0.44) than social workers without certification (r = 0.26). Cronbach’s alpha coefficients in each domain were over 0.77. These results indicate the reliability and validity of the SWP-PSP. This measure may be helpful for the evaluation of social workers’ capacity to promote social inclusion of welfare recipients. Full article

Background/Objectives. Quality of nursing care (QNC) is a central concept in healthcare systems worldwide, with growing emphasis on developing reliable and contextually appropriate instruments for its assessment. Over recent decades, there has been a shift from outcome-based evaluation toward more holistic, patient-centered frameworks that consider both clinical indicators and interpersonal dimensions of care. This scoping review aimed to map the range, nature, and characteristics of self-report instruments used to assess the quality of nursing care, including their psychometric properties and contextual applications across different clinical settings. Methods. A systematic search was conducted in CINAHL Complete, MEDLINE (via PubMed), Scopus, Web of Science, and ProQuest Dissertations & Theses, alongside gray literature sources, following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines. Studies were included if they reported on the development, validation, adaptation, or application of QNC assessment tools in hospital or community nursing contexts, and were published in English, Portuguese, or Spanish. Results. Fifty-nine studies were included, spanning from 1995 to 2025. The instruments identified were predominantly structured around Donabedian’s structure-process-outcome model, and many emphasized relational domains such as empathy, communication, and respect. Tools like the Good Nursing Care Scale (GNCS), the Quality of Oncology Nursing Care Scale (QONCS), and the Karen Scales demonstrated strong internal consistency (Cronbach’s α ranging from 0.79 to 0.95). Conclusions. Organizational factors, including leadership and staffing, and predictors such as burnout and work intensity, were found to influence perceived care quality. Important gaps remain regarding longitudinal use and integration of patient-reported outcome measures. Full article

Industry 4.0 produces large volumes of sensor and machine data, offering new possibilities for manufacturing analytics but also creating challenges in combining domain knowledge with visual analysis. We present a systematic review of 13 peer-reviewed knowledge-assisted visual analytics (KAVA) systems published between 2014 and 2024, following PRISMA guidelines for the identification, screening, and inclusion processes. The survey is organized around six predefined building blocks, namely, user group, industrial domain, visualization, knowledge, data and machine learning, with a specific emphasis on the integration of knowledge and visualization in the reviewed studies. We find that ontologies, taxonomies, rule sets, and knowledge graphs provide explicit representations of expert understanding, sometimes enriched with annotations and threshold specifications. These structures are stored in RDF or graph databases, relational tables, or flat files, though interoperability is limited, and post-design contributions are not always persisted. Explicit knowledge is visualized through standard and specialized techniques, including thresholds in time-series plots, annotated dashboards, node–link diagrams, customized machine views from ontologies, and 3D digital twins with expert-defined rules. Line graphs, bar charts, and scatterplots are the most frequently used chart types, often augmented with thresholds and annotations derived from explicit knowledge. Recurring challenges include fragmented storage, heterogeneous data and knowledge types, limited automation, inconsistent validation of user input, and scarce long-term evaluations. Addressing these gaps will be essential for developing adaptable, reusable KAVA systems for industrial analytics. Full article

To address the practical needs for data sharing and exchange in the bridge engineering domain, this study creatively fills the definitional gap of IFC entities for steel bridges. In response to the deficiencies arising from the absence of domain-layer entity information in the IFC standard architecture, an extension strategy is proposed that integrates new entity definitions with customized property sets to enrich and formalize the steel bridge domain. On this basis, a foundational data framework for steel bridge structures is established, encompassing extended definitions for spatial structural units, assemblies, components, and parts. The customized property sets further expand the entity attributes related to the design and fabrication stages, thereby developing an IFC-based manufacturing information model for steel bridges. Furthermore, a parametric BIM modeling approach for steel bridges is introduced on the 3DEXPERIENCE platform, employing IfcOpenShell to inject semantic information and export models in standard IFC format. The proposed IFC extension and modeling methodology is demonstrated through its application to the Chengdu Q7 North Pedestrian Bridge project, confirming its practical value in enhancing the completeness and transferability of steel bridge BIM model information from the design phase through to fabrication. Full article