Search Results (2,608)

The Australian University Departments of Rural Health (UDRHs) promote the health and wellbeing of people in rural and remote Australia through health education, research, and advocacy. This narrative review evaluated the extent to which Indigenous health intervention research conducted by UDRHs over a 12-year period (2010–2021) aligned with the Principles and Priorities of the National Aboriginal and Torres Strait Islander Health Plan 2021–2031. The purpose was to reflect on past UDRH research contributions to identify existing strengths and areas for improvement in line with current policy. Thirty-three relevant UDRH publications were identified from a broader database of UDRH research outputs. Each paper was independently coded by at least two authors as demonstrating “yes”, “partial”, or “not evident” alignment with the twelve priorities of the Health Plan. UDRH intervention research demonstrated strengths in genuine shared decision making and partnerships with Indigenous communities, workforce development, health promotion, and identifying and addressing racism. However, gaps were evident in research addressing social and emotional wellbeing, mental health and suicide prevention, promotion of healthy environments, sustainability and preparedness, and transparency regarding shared access to data and information. UDRHs play a key role in building research capacity among staff and communities in rural settings and often maintain long-standing, respectful relationships with local Indigenous communities. While UDRH research aligns with many domains of the national Health Plan, future efforts should prioritise social and emotional wellbeing and mental health. Improved reporting of shared data access represents an immediate opportunity for enhancement. Full article

Adaptive recommendation mechanisms are widely used to personalise digital learning environments; however, many existing approaches prioritise algorithmic optimisation while providing limited insight into how recommendation behaviour aligns with pedagogically structured instructional artefacts, such as worksheets. To address this gap, this paper proposes a hybrid recommendation approach for adaptive worksheet generation that integrates content-based and collaborative filtering with explicit pedagogical constraints derived from Bloom’s Revised Taxonomy. The system ranks and selects learning and evaluation objects across cognitive levels by combining learner profiles, behavioural signals, and similarity-based information within a unified scoring framework. A simulation-based evaluation was conducted to examine the internal behaviour, stability, and instructional alignment of the recommendation engine under controlled conditions, using Bloom-aligned worksheets and synthetic learner profiles. The analysis focuses on expected–actual alignment and adaptive variation across cognitive levels rather than learning outcomes. Results indicate strong alignment with the intended instructional structure at lower cognitive levels, while bounded and interpretable adaptive variation emerges at higher levels. Evaluation object recommendations showed high agreement with the instructional design, exceeding 95% across simulated conditions. Overall, the study demonstrates how hybrid recommendation mechanisms can support adaptive content selection in pedagogically structured learning scenarios, offering a transparent and robust foundation for information-driven educational systems. Full article

The X-minute city has gained prominence as a planning paradigm for promoting sustainable local living, yet its operationalisation remains methodologically heterogeneous. This paper conducts a systematic review (Scopus, last search 4 March 2026) of 45 quantitative, proximity-based studies to examine how modelling decisions shape X-minute city assessments. Using reflexive thematic analysis, this review shows that proximity “as-modelled” is not an inherent property of urban space but a construct produced through a sequence of interdependent decisions concerning: analysis scope; functional inclusion; analysis approach; spatial representation; modelling variables; and assessment logic. These decision cascades, often implicit or inconsistently reported, generate substantial variation in results and limit the comparability and transferability of existing X-minute city analyses. The paper identifies connections between decision domains, examines how upstream assumptions influence downstream analytical possibilities, and highlights dominant modelling pathways and methodological divergences. Beyond proposing future directions (e.g., use of multi-threshold scenarios, equity-sensitive parameters, hybrid data strategies, and uncertainty/sensitivity reporting), the study provides a grounded baseline framework and guidance for documenting modelling processes. This research ultimately supports more transparent, reproducible, and context-sensitive proximity assessments, thereby contributing both conceptually and practically to the robustness and policy relevance of X-minute city studies. Full article

We systematically analyze the evolutionary characteristics of China’s public health policies, focusing on the dynamic changes in policy content, stage-specific differences, and inter-subject collaborative relationships. Based on 137 public health policy documents issued by the central government, the analysis is conducted from a dual perspective: first, the BERTopic model is employed to identify prominent policy themes and track their evolutionary paths; second, Social Network Analysis (SNA) is utilized to deconstruct the collaborative mechanisms and network structural characteristics among policy actors, goals, and tools. The findings indicate: (1) Collaboration among core policy actors is close, yet inter-departmental transparency and collaborative inclusivity remain limited for certain organizations. (2) Policy goals show a diversifying trend, with the strategic focus shifting from infectious disease prevention and control to comprehensive public health services. (3) There are significant preferences in the selection of policy tools for balancing rapid emergency response with sustainable long-term health governance. These findings reveal the evolutionary laws of the public health policy system and provide a theoretical basis for optimizing the policy framework and enhancing governance efficacy. Full article

Background/Objectives: Artificial intelligence (AI) models are increasingly applied across the assisted reproductive technology (ART) workflow, including male-factor assessment, ovarian stimulation, endometrial receptivity evaluation, embryo selection and prediction of pregnancy outcomes. However, many systems remain difficult to interpret, raising concerns regarding transparency, clinical integration and patient communication. Explainable artificial intelligence (XAI) aims to address these limitations by making model behavior more accessible to clinicians and embryologists. This review aimed to provide a narrative, concept-driven synthesis of how XAI has been implemented in ART, to critically examine methodological quality and clinical relevance and to outline priorities for responsible translation into practice. Methods: A structured narrative review was conducted using PubMed/MEDLINE as the primary database, supplemented by targeted reference-list screening of key primary studies and recent cross-disciplinary reviews relevant to AI in ART. Studies were curated and classified according to stage of the ART workflow, data modality, model family, explanation technique and validation strategy. Methodological features, performance reporting and implementation considerations were qualitatively appraised. Results: Most XAI applications in ART fall into two dominant categories: (i) feature-attribution methods such as SHAP and LIME applied to tabular clinical and laboratory data and (ii) saliency-based approaches, including Grad-CAM and related techniques, applied to embryo and ultrasound imaging. These methods can improve transparency and support counselling by clarifying which variables or image regions influence predictions. However, the majority of studies are retrospective and single centre, with limited external validation and heterogeneous outcome definitions, often prioritising clinical pregnancy over live birth. Calibration, decision-analytic evaluation and prospective assessment remain uncommon. XAI outputs are frequently interpreted as biologically causal despite being derived from observational data, highlighting the need for cautious clinical framing. Conclusions: XAI in ART has progressed from proof-of-concept demonstrations to early clinically oriented tools, but robust validation, standardised reporting and thoughtful workflow integration are still needed. Explanations can enhance auditability and communication, yet they do not compensate for methodological weakness. Future progress will depend on higher-quality multi-centre data, evaluation beyond discrimination metrics and governance frameworks that ensure transparency, fairness and sustained performance in real-world practice. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

The rapid integration of electric vehicles (EVs) and decentralized renewable energy sources is transforming urban power systems, while simultaneously increasing the complexity of real-time coordination across charging infrastructure, distributed energy resources, and grid-support devices. This systematic review synthesizes recent research on multi-agent reinforcement learning (MARL) for EV–grid coordination, with emphasis on four emerging dimensions: forecasting-informed control, safety-constrained learning, explainability and interpretability, and trustworthy decentralized coordination. A systematic literature search was conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, MDPI, and arXiv, covering primarily the period 2016–2025, with selected early-2026 studies retained where relevant, with selected earlier foundational studies retained for context. The review was conducted and reported in accordance with the PRISMA 2020 framework. A total of 412 records were identified through database searching; after duplicate removal and screening, 58 studies were included in the final qualitative synthesis. The reviewed literature shows that MARL is increasingly being applied to EV charging coordination, demand-side management, community energy systems, transactive energy, and ancillary grid services. The evidence further indicates that forecasting integration improves anticipatory control, safety-aware formulations enhance operational reliability, and explainability-oriented designs help address transparency and trust barriers in safety-critical grid environments. However, the literature remains limited by heterogeneous benchmarks, inconsistent evaluation metrics, and a lack of real-world deployment evidence. This review provides a structured synthesis of current methodologies, identifies critical research gaps, and outlines future directions for the development of safe, interpretable, and deployment-ready MARL frameworks for urban energy systems. Full article

Background/Objectives: Severe brain injuries generate complex, long-term needs requiring intensive physical, cognitive and relational care. These conditions also profoundly affect families, who often experience emotional distress, uncertainty and a heavy caregiving burden. Although neuro-palliative care is increasingly recognised, the early integration of palliative care for this population remains limited. This study aimed to explore healthcare professionals’ perceptions of the palliative care needs of patients with severe brain injuries and their caregivers and to identify factors that hinder or facilitate early palliative care implementation in specialised settings. Methods: An interpretive qualitative study was conducted using Reflexive Thematic Analysis. Fifteen semi-structured narrative interviews were carried out with healthcare professionals working in specialised hospital units in Northern Italy. Data were analysed inductively through an iterative and reflexive process following Braun and Clarke’s six phases. Methodological rigour and transparency were ensured using the COREQ checklist. Results: Five themes were identified: (1) intensive, individualised patient care needs with complex communication issues; (2) palliative needs centred on dignity, quality of life and early integrated management; (3) caregivers’ involvement and expectation-related difficulties; (4) continuous or anticipatory grief requiring structured psychological support; (5) facilitators and barriers influencing care pathways. Conclusions: Healthcare professionals identify intertwined and evolving palliative care needs in both patients with severe brain injuries and their families. The findings highlight the perceived importance of early, integrated and multidisciplinary neuro-palliative care models focused on dignity, symptom relief and sustained emotional support. Full article

Gem-quality garnets exhibit significant potential for U-Pb geochronological applications due to their advantageous characteristics, including high closure temperatures (750–850 °C), optical transparency, chemical homogeneity, and low inclusion content. This study focuses on the gem-quality yellow-green grossular garnet variety (commonly termed Mali garnet), a unique gemstone exclusively occurring in contact metamorphic deposits of Western Africa’s Republic of Mali. Despite its mineralogical significance, fundamental aspects, including precise age determination and chromophore mechanisms of Mali garnet, remain poorly constrained. Here, we conducted standard gemological characterization, spectroscopic analyses (UV–Vis, FTIR, and Raman), electron probe microanalysis (EPMA), micro-X-ray fluorescence (μ-XRF) elemental mapping, and in situ trace element and laser ablation U-Pb geochronological analysis on Mali garnets. The spectral data and chemical composition studies reveal that the coloration of Malian garnets is primarily attributed to the presence of iron and chromium. Our U-Pb geochronological results yield a crystallization age of 197 ± 3 Ma for the Mali garnet samples. The robustness of garnet U-Pb systems in preserving crystallization ages through multiple thermal events supports their application to Precambrian polymetamorphic terranes, where zircon systems are frequently reset. Full article

Authentic Digital Interaction (ADI) refers to citizens’ direct, secure, and independent engagement with e-government services without reliance on intermediaries. This systematic literature review applies ADI as an organizing lens to synthesize recent empirical evidence on determinants shaping citizen interaction with e-government. Following PRISMA, 178 peer-reviewed studies published between January 2020, and October 2025 were identified across five databases, and 43 met the inclusion criteria. Descriptive mapping and ADI-guided narrative synthesis were used to consolidate related determinants and interpret their associations and contextual conditions. The review identifies three dominant patterns: perceived usefulness and performance expectancy are most frequently associated with intention, use, and continuance; trust and confidence shape whether perceived benefits translate into engagement; and policy and governance condition service consistency and the effects of usability and accessibility. Theoretically, the review shows that ADI provides a useful lens for interpreting e-government research beyond adoption and satisfaction by emphasizing direct, trustworthy, inclusive, and independent citizen interaction. Practically, the findings suggest that public agencies should prioritize accessible design, transparent processes, visible safeguards, and supportive governance arrangements. However, no formal risk-of-bias assessment was conducted. In addition, the evidence base remains limited by the sparse examination of participation, value co-creation, autonomy, and empowerment, and the review protocol, although prepared in advance, was not registered. Full article

Carbon nanotube (CNT) materials exhibit ultrahigh electrical and thermal conductivity. Upon electrical excitation, CNT-based transparent conductive films (TCFs) can emit far-infrared radiation (FIR) and provide certain physiotherapeutic efficacy, making them ideal candidates for thermotherapy applications. This work systematically tests and analyzes the fundamental physical properties and physiotherapeutic performance of CNT flexible thin-film heaters (TFHs) for potential use in health physiotherapy. Two types of TFHs with different electrode connection modes were fabricated via the prepared TCFs. Experimental characterizations were conducted on their response time, electrothermal performance, and heat transfer characteristics. The results showed that the temperature rise per unit input power for TFH1 was 16.71 °C/W, while that of TFH2 was 4.29 °C/W at the same voltage of 10 V. In addition, the variation trends of maximum temperature with power density were highly consistent for the two films. This demonstrates that TFHs fabricated using the same TCFs exhibit excellent and high electrothermal conversion efficiency as well as outstanding comprehensive electrothermal performance. In addition, smaller L/W ratio leads to lower resistance of TFHs, resulting in a stronger thermal effect under identical applied voltage. After the temperature stabilized, the surface temperature of the TFHs decreased by approximately 5 °C when attached to the human arm, confirming that the heat generated by the TFHs under electrical excitation could be effectively absorbed by the human body. The TFHs emitted rapid FIR upon electrification, and the peak wavelength ranged from 8 to 12 µm, which fell within the range of 6–14 µm that was easily absorbable by the human body. The heat can be rapidly absorbed by the skin and distributed throughout the body via blood circulation, yielding favorable physiotherapeutic efficacy. This study provides key physical parameters for the application of TFHs in wearable medical devices and physiotherapy equipment. Full article

Obesity and depression in childhood and adolescence represent major public health concerns, yet the nature and direction of their association remain incompletely understood. In the present study, we conducted a systematic review and stratified meta-analysis of epidemiological studies examining the relationship between obesity and depression in youth populations. A total of 945 records were identified through database searches, of which 18 studies met the inclusion criteria for the review. To ensure greater conceptual consistency, quantitative synthesis was restricted to studies examining categorical obesity (typically defined as body mass index (BMI) ≥ 95th percentile) and binary depression outcomes, which formed the longitudinal and cross-sectional meta-analytic cores, analyzed separately. Our longitudinal meta-analysis (k = 4; N = 5581) indicated that obesity was associated with an increased likelihood of subsequent depression (OR = 2.05, 95% CI: 1.40–2.99), whereas the cross-sectional meta-analysis (k = 6; N = 20,278) showed a weaker and non-significant association (OR = 1.40, 95% CI: 0.98–2.00) with moderate heterogeneity. Additional studies that could not be pooled due to differences in exposure or outcome definitions were integrated through narrative synthesis and showed mixed and generally less consistent patterns, broadly supporting the distinction between longitudinal and cross-sectional evidence. Overall, our findings suggest that obesity during childhood and adolescence is associated with an increased likelihood of subsequent depression, while concurrent associations appear more heterogeneous and more difficult to interpret. By distinguishing between study designs and prioritizing comparable effect estimates, this study provides a more transparent synthesis of the current evidence on the relationship between obesity and depression in youth. Full article

Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied in traditional predictive models, less is known about how explainability is implemented within agentic architectures. Objective: To map the emerging literature on explainable agentic AI (XAAI) in healthcare and characterize the types, scope, and forms of explainability used in these systems. Methods: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, IEEE Xplore, and ACM Digital Library were searched through November 2025. Eligible studies described healthcare-related agentic AI systems incorporating explicit explainability mechanisms. Data were extracted on system architecture, explainability type (intrinsic, post hoc, hybrid), explanation scope (local, global), explanation form, and reported clinical outcomes. Results: Nine studies met the inclusion criteria. All systems demonstrated core agentic features, including autonomy, task decomposition, and tool integration, often within multi-agent frameworks. Explainability was predominantly intrinsic and workflow-native, typically delivered through textual reasoning traces and example-based grounding in retrieved clinical evidence. Feature-based and global explanations were comparatively rare and largely confined to hybrid architectures. Across domains including radiology, neurology, psychiatry, and biomedical research, XAAI systems were reported to improve performance and interpretability relative to baseline models in the included studies. However, these findings were derived from heterogeneous, predominantly experimental or retrospective studies, and structured human-in-the-loop oversight was infrequently described. Conclusions: Current XAAI systems appear to emphasize process transparency and evidence grounding rather than mechanistic model-level attribution. The available evidence remains limited and heterogeneous, and findings should be interpreted as early trends rather than established characteristics. Further progress will require standardized evaluation frameworks, clearer reporting of oversight mechanisms, and validation in real-world clinical settings to support safe and trustworthy integration of agentic AI into healthcare practice. Full article

Morocco is a leading African nation in renewable energy, with growing interest in expanding offshore wind energy. As offshore wind projects have gained momentum worldwide, public acceptance, particularly regarding their environmental and visual impacts, has become a critical consideration. This exploratory study examines the social acceptance of offshore wind farms (OWFs) in Morocco by integrating social acceptance analysis with a visual impact assessment based on three-dimensional (3D) image modeling in an emerging offshore wind context. Social perceptions were first assessed through a small-scale survey, with findings interpreted descriptively and considered alongside results from a public perception survey conducted in Japan, which served as a contextual reference. A hypothetical offshore wind installation along the Moroccan coast was then simulated, followed by a small-scale exploratory perception survey to examine initial reactions to different visual configurations. Given the limited sample size, the findings are indicative rather than generalizable. Nevertheless, they provide preliminary insights into the prominent role of environmental considerations, particularly ecological protection and visual integration, in shaping attitudes toward OWFs. This study highlights the relevance of careful site selection, transparent communication, and early stakeholder engagement as context-sensitive considerations for offshore wind development in Morocco. Full article