Search Results (271)

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

Wind turbine blade defect detection requires accurate identification of small and irregular defects while maintaining low computational cost for practical inspection scenarios. However, lightweight detectors often suffer from insufficient local feature extraction, limited multiscale feature fusion, and weak responses to critical defect regions. To address these issues, this study proposes a Receptive-Field-Enhanced You Only Look Once model (RFE-YOLO), a lightweight defect detection model based on You Only Look Once version 10 nano (YOLOv10n).The proposed model introduces three task-oriented improvements. First, C2f-RFAConv is embedded into the backbone to enhance receptive field aware local feature representation for fine grained defects. Second, a Compact Cross-scale Feature Fusion Module, termed CCFM, is designed in the neck to improve the integration of low-level detail information and high-level semantic features with reduced computational complexity. Third, an Efficient Local Attention module is inserted before the detection head to strengthen defect-related spatial responses after feature fusion. Experiments were conducted on a wind turbine blade defect dataset containing three categories, namely Crack, Oil leakage, and Peel. The results show that RFE-YOLO achieves 89.9% mean Average Precision at an Intersection over Union threshold of 0.5, namely mAP@0.5, and 64.73% mAP@0.5:0.95. Compared with YOLOv10n, RFE-YOLO improves mAP@0.5 by 2.8 percentage points while reducing the number of parameters from 2.70M to 1.91M and giga floating point operations from 8.4 to 5.3. The inference speed reaches 88.8 frames per second on an NVIDIA GeForce RTX 3090 GPU. These results indicate that RFE-YOLO achieves a favorable balance between detection accuracy and model efficiency under the current experimental setting. Full article

Domestic Violence affects 1 in 3 women worldwide. Empirical evidence from India suggests that women and girls experience a continuum of violence and discrimination from prenatal stages till death in families that largely continue to operate within a dominantly patriarchal framework. However, the literature on domestic violence in India suffers from problems pertaining to reductive and episodic framing, focusing on short-term prevalence, and frames the impact on survivors largely in terms of clinical constructs such as anxiety, depression, and PTSD. This work argues for a broader, thematic framing of domestic and familial violence and contends that the psychological sequelae of this kind of chronic and systemic discrimination and violence cannot be captured using rigid clinical constructs that dominate psychological literature. We propose a conceptual framework, i.e., SPECTRA (Socially and Psychologically Embedded Continuous Trauma in Relational Architecture), which is partially aligned with the propositions of complex trauma. However, we also critique the origin of complex trauma within hegemonic psychiatry and highlight the need for creating a culturally adapted expansion—to shift the emphasis from an individually rooted, diagnostic framework to a culturally contextualized continuous trauma framework. We utilize seven illustrative case studies to define the tenets of the SPECTRA model. Full article

Intensive pig farming presents substantial challenges for individual health monitoring due to high stocking densities, complex occlusion scenarios, and the need for continuous real-time surveillance. Existing monitoring approaches rely heavily on manual inspection, which is labor-intensive and prone to delayed detection of abnormal behaviors and disease symptoms. This study proposes an embedded intelligent monitoring system integrating a pan-tilt gimbal platform with an improved multi-object tracking and anomaly detection framework for automated pig health surveillance. The system employs a modified Periodfill_DeepSORT algorithm that incorporates a ReID network with appearance features and motion prediction trajectories to maintain identity consistency under occlusion and re-entry scenarios. For anomaly detection, a lightweight YOLOv8-based network was trained on 772 abnormal samples across three behavioral categories: movement abnormalities, postural abnormalities, and disease-related abnormalities. Experimental results demonstrate that the Periodfill_DeepSORT algorithm achieves a Multiple Object Tracking Accuracy (MOTA) of 95.34%, a Multiple Object Tracking Precision (MOTP) of 94.77%, and an IDF1 score of 96.88%, with only 12 identity switches across 2000 frames involving 12 targets—27 fewer than the standard DeepSORT algorithm. In occlusion scenarios, MOTA improved from 61.1% to 78.3%. The anomaly detection network achieves an overall detection accuracy of 94.5%, representing an 8.8 percentage point improvement over the baseline model, with recognition accuracies of 96.2% for movement abnormalities, 94.1% for postural abnormalities, and 92.8% for disease-related abnormalities. The system operates at 90 frames per second on embedded hardware with a power consumption of 3.2 watts and a startup time of approximately 1 s, with gimbal angle errors maintained within 3°. These results demonstrate the system’s effectiveness and practical feasibility for real-time intelligent health monitoring in intensive livestock farming environments. Full article

Background: The role of religion and faith-based organisations in public health is increasingly examined through the framework of religious health assets (RHAs), defined as resources located in or held by religious entities that may be mobilised for health and development. Within this framework, church health assets (CHAs) are conceptualised as congregationally specific expressions of RHAs, namely, the tangible and intangible resources recognised within local church settings and interpreted by church leaders as relevant to adolescent and youth sexual and reproductive health (AYSRH). Despite growing interest, there remains limited empirical work examining how such assets are perceived in relation to young people’s sexual and reproductive health, particularly from an emic perspective in sub-Saharan Africa. Aim: This study explored how pastors in South Africa’s Vaal Triangle perceive church assets relevant to AYSRH. Methods: The article presents findings from a qualitative study based on in-depth semi-structured interviews with eleven purposively selected pastors from Vanderbijlpark, Vereeniging, and Sasolburg. Data were collected between August 2019 and February 2020, prior to the COVID-19 restrictions that later altered face-to-face engagement in South Africa. Data were analysed using thematic content analysis informed by interpretive description, employing iterative coding, constant comparison, memoing, and a clearly defined audit trail. Results: The findings identified ten perceived CHAs, comprising five tangible assets, interaction spaces, community resources, normative teaching materials, networks and partnerships, and financial resources—and five intangible assets—reputation, voice on sexuality, mission and vision, a ready audience, and embodied messages. Across these themes, pastors predominantly framed AYSRH in moral and pedagogical terms, emphasising abstinence, guidance, and restoration, rather than a broader continuum encompassing information, prevention, care, rights, and service access. Conclusions: The study concludes that pastors perceive churches to possess substantial AYSRH-related assets; however, the analysis reflects perceptions rather than demonstrated implementation or measurable impact. The findings highlight both potential and limitation, indicating that the same assets may function as facilitators or barriers depending on their interpretation and application. The study contributes a pastor-centred, emic account of CHAs within a South African context and underscores the need for future multi-stakeholder research to assess how faith-sensitive AYSRH interventions operate in practice. Full article

A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. To investigate this question systematically, we develop a lightweight intrusion-detection framework combining statistical traffic descriptors, structural identifier transition features, and graph topology representations extracted from sliding windows of CAN frames. Because CAN is a broadcast-only bus with no request–response mechanism, each ECU independently transmits its identifiers at fixed periodic rates; accordingly, the structural and graph-based features capture the temporal scheduling regularity of identifier broadcasts, not directed inter-ECU communication dependencies. Stress-testing the framework under cross-attack and cross-dataset transfer reveals a clear four-level hierarchy: (1) statistical features collapse under cross-attack transfer (ROC-AUC as low as 0.009), failing to generalise beyond the attack type seen during training; (2) structural transition features are the most robust form of representation, maintaining high cross-attack performance (ROC-AUC > 0.999) across all evaluated scenarios within the same vehicle platform; (3) graph topology features are scenario-dependent, achieving high robustness in DoS-trained scenarios but producing sub-random results in Fuzzy-trained scenarios, exposing a sensitivity to injection density profiles; and (4) the hybrid combination provides the strongest overall operational package, consistently across four classifiers. Cross-dataset transfer to the ROAD dataset reveals the precise boundary conditions of transferability: structural representations transfer only when an attack perturbs identifier transition regularity (correlated signal attacks, ROC-AUC = 0.81–0.83), while attacks that affect only payload semantics (speedometer) or exploit identifier–space novelty (fuzzing) lie outside the detection scope of transition-based features, regardless of the vehicle platform. A vehicle-specific calibration experiment further shows that the correlated-attack generalization gap can be closed with as little as 10% of target-vehicle normal traffic, whereas speedometer attacks remain structurally invisible by design. A key contribution of this work is therefore a transparent approach for identifying when relational CAN representations transfer and when they do not—a finding that is more scientifically valuable than a uniformly optimistic performance claim and which provides concrete guidance for practitioners designing cross-platform automotive IDS. Full article

The growing interest in visual methodologies within the educational field reflects the need to rethink teaching–learning processes from a participatory, multimodal, and inclusive perspective. Among these approaches, PhotoVoice emerges as a research–action and training strategy that combines photography and autobiographical narration, activating accessible expressive practices centred on subjectivity and lived experience. This contribution presents a theoretical–methodological analysis grounded in pedagogical and visual research literature, aiming to outline an operational framework for the educational application of PhotoVoice in inclusive pathways addressed to individuals with intellectual disabilities. Framed within the paradigm of Visual Education and a pedagogy oriented toward recognition and relationality, PhotoVoice is examined as a pedagogical device capable of fostering symbolic mediation, identity construction, and narrative agency. The photographic image, conceived as an embodied, situated, and relational language, enables access to forms of knowledge often excluded from dominant verbal codes, restoring visibility and epistemic dignity to marginalised subjectivities. The paper delineates key operational phases of the method and identifies core educational objectives, including the strengthening of narrative agency, self-determination, and reflective participation. From this perspective, visual narration is configured as a situated pedagogical practice integrating aesthetics, ethics, and social transformation, capable of generating equitable and meaning-generative learning environments. Within this framework, PhotoVoice shifts inclusion from an abstract principle to a concrete educational process, enabling participants to narrate, interpret, and actively reshape their own learning contexts. Full article

Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction. The proposed framework integrates YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced RULA model for continuous ergonomic risk quantification from video-derived motion signals. To support model development and evaluation, we constructed a dedicated wheelchair cabinet-operation dataset comprising 30 participants, including 14 experienced wheelchair users and 16 trained simulation participants, across five representative cabinet-operation scenarios. The raw dataset contained approximately 5 h of RGB video and about 150,000 original frames. To reduce redundancy caused by highly similar consecutive frames and to mitigate overfitting risk, representative frames were sampled from the continuous video sequences, resulting in 10,000 images for annotation and model development. Based on the proposed framework, raw visual sensor signals are transformed into temporally continuous kinematic representations and ergonomic risk scores, enabling non-contact and real-time health-state interpretation in assistive living environments. The proposed method achieved an average joint-angle estimation RMSE of 7.5°, representing an approximately 60% reduction compared with a Kinect v2-based motion capture baseline (18.6°), which is widely used for low-cost ergonomic evaluation. In benchmark evaluation, the proposed method achieved 84% risk-classification accuracy with a Cohen’s kappa of 0.66, outperforming representative baseline approaches. The results further indicated that low revolving-door and low-drawer operations were associated with higher and more sustained ergonomic risk exposure than sliding-door interaction. These findings demonstrate that vision-based sensor signal analysis can provide an effective solution for intelligent health management, ergonomic monitoring, and perception-driven assessment in accessible and assistive autonomous living systems. Full article

Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from model inference itself, but also from process scheduling, inter-process communication, and resource coordination overhead. To address this issue, this paper presents a task scheduling and management platform for multi-workload smart elderly care on a single pure-edge CPU–TPU heterogeneous node. The platform adopts a shared-memory and event-driven synchronization mechanism together with fine-grained process partitioning, thereby establishing a data-sharing and runtime-coordination framework for concurrent multi-workload execution. To evaluate the effectiveness of the proposed platform, experiments were conducted under single-workload, multi-workload, multi-resolution, and long-term runtime settings. The results show that, compared with two baseline schemes, the proposed platform improves the average frame rate by 66.7% and 71.1%, reduces net memory usage by 96.3% and 45.3%, and lowers net power consumption by 46.8% and 37.7%, respectively, under the single-workload setting. Under 10 concurrent workload instances, the system still maintains a stable frame rate of 42.03 ± 0.73 fps, demonstrating strong concurrency scalability. Multi-resolution experiments further indicate that the performance degradation at higher resolutions is mainly constrained by the front-end data supply stage. A continuous 10-day runtime experiment additionally verifies the sustained operating capability and resource stability of the platform under pure-edge deployment. These results demonstrate that node-level shared-memory and event-driven coordination can effectively improve the execution efficiency, scalability, and stability of real-time multi-workload analytics on such pure-edge heterogeneous nodes, providing a useful basis for future extensions to multi-node edge environments and edge–cloud collaborative task scheduling. Full article

Health is increasingly understood as a multidimensional phenomenon shaped by complex interactions among biological, psychosocial, environmental, and informational factors. Building on the human exposome and its extensions, this paper introduces the interpretive exposome, a conceptual framework that captures cumulative exposure to how health-related information is framed, recorded, interpreted, and communicated by clinicians, artificial intelligence (AI) mechanisms, and institutions across the life course. We argue that the interpretive process, including biased clinical health records, algorithmic decision-support outputs, and inequitable communication, operates as exposures that can accumulate and influence downstream health outcomes. We further describe how AI systems function as interpretive filters that may reproduce, alleviate, or amplify bias through training data and recursive deployment. While remaining conceptual in nature, this proposed framework outlines methodological pathways for operationalization using natural language processing (NLP), bias auditing, and multi-modal data integration. The interpretive exposome complements existing exposome models and offers a theoretical foundation for future empirical validation aimed at promoting equitable, transparent, and context-aware healthcare. Full article

Transboundary river basins (TRBs) sustain billions of livelihoods, yet they face enduring systemic challenges of cooperative water governance. Although collaborative governance models consistently yield acceptable outcomes, adversarial dynamics and zero-sum approaches continue to dominate transboundary water management. This systematic review synthesizes the peer-reviewed literature (2000–2026) to evaluate how four major governance dimensions—and the cross-cutting integration of the water–energy–food (WEF) nexus—shape the effectiveness of water diplomacy in international basins. Socio-economic analysis reveals that benefit-sharing arrangements grounded in joint investment outperform zero-sum volumetric allocation, though implementation remains constrained by institutional fragmentation and governance lock-in. Power relations analysis demonstrates that material, institutional, knowledge-based, and narrative-framing asymmetries systematically define the range of achievable agreements and the reliability of cooperative commitments, with case analysis from the Nile, Mekong, Tigris–Euphrates, and Central Asian basins showing that comparable hydrological conditions yield divergent diplomatic outcomes depending on how power is distributed. Stakeholder engagement findings indicate that formal participatory mechanisms frequently produce symbolic rather than substantive inclusion, particularly where structural imbalances limit procedural access. Gender analysis provides that women’s inclusion improves agricultural productivity, water-use efficiency, and adaptive capacity—functioning as a governance variable with measurable system-performance effects rather than solely an equity objective. The WEF nexus operates as the integrative mechanism binding these dimensions, reframing diplomacy from volumetric allocation toward adaptive benefit arrangements that coordinate interdependent services across sectors. This review concludes that effective transboundary governance emerges from the concurrent integration of socio-economic benefit-sharing, power-responsive institutions, meaningful stakeholder participation, gender equity, and nexus-based coordination in global TRBs. Full article

Introduction: Colorectal cancer remains a leading cause of cancer-related morbidity and mortality, with adenomatous polyps representing a common precursor. Post-polypectomy polyp recurrence represents a significant risk of colorectal cancer, driving periodic colonoscopy surveillance and polypectomy as needed. In this study, we explore a multimodal machine learning approach that integrates endoscopic imaging with clinical and pathology data to improve recurrence risk prediction and support individualized surveillance planning. Methods: We developed and evaluated a multimodal artificial intelligence (AI) model to predict post-polypectomy colorectal polyp recurrence using the ERCPMP-v5 dataset. The cohort included 217 patients with 796 high-resolution endoscopic RGB images and 21 endoscopic videos; video data were converted to still frames at 2 frames per second. Images and frames were resized to 224 × 224 pixels and normalized. Patient-level demographic, morphological (Paris, Kudo Pit, JNET), anatomical, and pathological variables were encoded using standard scaling for continuous features and one-hot encoding for categorical features. Visual representations were extracted using a pretrained Vision Transformer backbone (ViT-Base-Patch16-224) with frozen weights. Structured metadata (79 variables) was encoded using a multilayer perceptron. A late fusion framework used image and metadata representations to generate a recurrence probability via a sigmoid classifier; probabilities were thresholded at 0.5 for binary prediction. Model performance was evaluated on a held-out test set using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). We additionally compared fusion performance with image-only and metadata-only baselines. Predicted probabilities were translated to surveillance recommendations using risk tiers: low risk (0.00 ≤ p < 0.20), moderate risk (0.20 ≤ p < 0.50), and high risk (p ≥ 0.50). Results: On the test set, the multimodal fusion model achieved 90.4% accuracy, 86.7% precision, 83.1% recall, 84.9% F1-score, and an AUC of 0.920. The image-only model achieved 84.6% accuracy (AUC 0.880), and the metadata-only model achieved 81.9% accuracy (AUC 0.850), indicating improved performance with multimodal fusion. Risk stratification enabled surveillance recommendations of 1–3 years for low risk, 6–12 months for moderate risk, and 3–6 months for high risk. Conclusions: A late-fusion multimodal model integrating endoscopic imaging with structured clinical and pathology variables demonstrated excellent performance for predicting post-polypectomy recurrence and generated actionable risk-based surveillance intervals. This approach may support individualized follow-up planning and more efficient allocation of surveillance resources, while prioritizing timely evaluation for patients at higher predicted risk. Full article

This study investigates emotional patterns in state government disaster guideline documents using keyword-level emotion analysis and TF–IDF based topic modeling, framing disaster policy communication as an emotional–cognitive dual structure, drawing from Situational Crisis Communication Theory. The findings demonstrate a strong negative relationship between fear and neutrality, indicating a functional separation between risk awareness and administrative clarity. Nine topics were identified and organized into clusters centered on operational support, administrative structures, and policy frameworks, while content related to hazards and recovery emerged as a distinct semantic category based on cosine similarity analysis. In the integrated analysis of sentiment and topics, neutral language predominates, reflecting the cognitive dimension of government guidelines, with fear and sadness appearing as secondary but systematically patterned emotions. Fear concentrates in topics addressing hazardous conditions and risk-related content. Emotionally neutral language has traditionally been privileged in public administration, but the findings highlight disaster policy communication shaped by governance objectives that privilege specific emotional orientations aligned with coordination, participation, and risk management. State disaster guidelines function not only as technical instructions but also as structured communicative instruments that operate along a dual cognitive–emotional model, shaping public attention and response. Full article

Rural territories characterised by dispersed settlement systems face mounting challenges related to demographic decline, economic fragility, ecological degradation, and the erosion of local knowledge systems. In this context, rural self-sufficiency has re-emerged as a strategic objective; yet it remains inadequately operationalised within spatial planning and territorial assessment practices. This paper proposes a bioregional framework for operationalising rural self-sufficiency in dispersed territories, integrating ecological, morphological, socio-productive, cultural, and governance dimensions across multiple spatial scales. The framework is structured around a tiered system of 108 indicators, hierarchised into priority, secondary, and aspirational levels, combined with a multi-scalar territorial reading articulated through five nested frames—ranging from municipal systems to local productive units. Rather than constituting a mere checklist for immediate quantitative evaluation, the indicator system functions as a structured diagnostic universe, enabling progressive operationalisation based on data availability and governance capacity. To bridge the gap between diagnosis and action, the framework introduces 34 strategic drivers and 28 spatial artefacts, conceived as reversible and context-sensitive interventions. The framework is demonstrated through the case of Arbo (Galicia, Spain), illustrating its capacity to structure territorial diagnosis and articulate coherent pathways from analytical interpretation to strategic spatial intervention. The proposed approach contributes a replicable methodological tool for bioregional and rural planning in dispersed settlement systems. The study contributes to advancing bioregional planning by demonstrating how extensive indicator universes can be rendered operational through selective tiering and multi-scalar deployment. Full article

Building Information Modeling (BIM) supports information integration and coordination across the construction lifecycle, but benefits depend on collaboration that is shaped by the selected project delivery method (PDM). BIM-PDM evidence is difficult to consolidate due to heterogeneous terminology and fragmented, context-specific studies. This scoping review maps which PDMs are addressed in the BIM-related literature and how adequacy is framed. Following PRISMA-ScR, Web of Science and Scopus were searched and 71 studies met the eligibility criteria. Publications increased markedly after 2018 and were geographically concentrated, with the largest shares associated with author affiliations in China, the United Kingdom, Australia, Canada, Malaysia, and the United States. Integrated Project Delivery (IPD) was the most frequently examined (46 studies), followed by Design–Bid–Build (DBB) (29), Design–Build (DB) (29), Public–Private Partnership (PPP) (17), and Engineering, Procurement, and Construction (EPC) (14), while Alliancing, Lean-oriented delivery approaches, and Construction Management were comparatively underrepresented. A temporal analysis indicates a recent shift toward collaborative delivery methods in BIM research. Case-based studies are predominantly situated in public sector projects, with DBB, DB, EPC, and IPD examined across both infrastructure and building contexts, while PPP is limited to infrastructure. The literature is largely focused on design and construction phases, with limited attention to early project stages and operation and maintenance. Results indicate both traditional and relationship-based PDMs are studied in the existing literature, with research framing PDMs that allow for early contractor involvement as most compatible with BIM. Moreover, IPD, DB, and EPC show the best alignment compared to most used traditional DBB methods primarily due to the early involvement of the contractor in the project. EPC and DB achieve this through the allocation of responsibility to the contractor, whereas IPD relies on the early engagement of key participants and the systematic alignment of their objectives. Collaborative and relationship-based approaches are consistently presented as the most suitable for BIM, while DBB tends to constrain BIM benefits because of its fragmented nature. This study contributes by providing a systematic synthesis of BIM-PDM relationships in the scientific literature, identifying the key mechanisms underlying the suitability of different delivery methods for BIM implementation, and offering recommendations for future research based on the identified gaps. Full article