Search Results (1,693)

Background/Objectives: Online sexual violence is an increasingly prevalent form of gender-based harm facilitated by digital technologies, with significant consequences for the health, well-being, and rights of adult women. Despite growing attention to this phenomenon, women’s lived experiences remain underexplored, particularly regarding sexual consent and institutional responses. This study aimed to examine how adult women experience online sexual violence, how consent is negotiated or constrained in digital contexts, and how coping and institutional mechanisms are perceived. Methods: A qualitative study with a hermeneutic phenomenological approach was conducted. Data were collected through three focus groups with 23 women aged 21 to 42 years who were active users of social media. Results: Participants reported diverse forms of online sexual violence, including unsolicited sexual messages and images, persistent harassment, coercion, blackmail, and threats. Sexual consent was often undermined by emotional manipulation, social pressure, and fear, placing women in vulnerable positions. These experiences negatively affected well-being, contributing to anxiety, reduced self-esteem, fear, and difficulties in sexual and emotional relationships. Coping strategies were mainly individual, such as blocking perpetrators or reporting content, while social support was frequently perceived as insufficient. A generalized distrust of institutional responses emerged, with formal mechanisms viewed as ineffective or inaccessible. Conclusions: For the study participants, online sexual violence is increasingly normalized and concealed within digital environments, reinforced by anonymity and impunity. The findings highlight the need for continued research and the development of interventions that include early sexual and emotional education, awareness-raising initiatives, digital regulation, specialized professional training, and the strengthening of victim-centered support networks. Full article

Massive Open Online Courses (MOOCs) serve as catalysts for sustainable education by democratizing access to lifelong learning. While this potentially positions them as a key driver of the United Nations Sustainable Development Goal 4 (SDG 4), their long-term impact depends heavily on the implementation of inclusive design and ethical governance. This study evaluates the social sustainability of the AKADEMA platform—defined through equity of access, institutional trust, and long-term learner retention—using Badrul Khan’s e-learning framework. Employing a multi-layered mixed-methods design, the study triangulates subjective user perceptions—gathered via quantitative surveys (N = 209; a convenience sample of 6140 contacted users) and qualitative insights (n = 122)—with objective structural evidence from a technical accessibility audit. Although the results indicate high satisfaction with pedagogical quality, the findings reveal specific structural nuances regarding platform inclusivity and user diversity. Specifically, data triangulation highlights a notable ‘privacy awareness gap’—where working professionals demonstrate higher sensitivity regarding data governance than learners—alongside structural barriers hindering ‘Universal Design’ for learners with disabilities. Consequently, to strengthen the sustainability of open education models, future strategies should emphasize digital equity and institutional trust, ensuring that technical environments align with the promise of inclusive quality education. Full article

Background: To mitigate the negative impacts of the pandemic, it is essential to understand how the associations between different types of physical activity (PA) and adolescent well-being changed before and after the COVID-19 pandemic (defined here as the period marked by students’ return to stable in-person education). This study aimed to examine gender differences in the associations between school-related PA and subjective well-being before and after the pandemic. Methods: A cross-sectional design was used, including 430 boys and 571 girls from 22 high schools. Participants completed the Youth Activity Profile questionnaire to assess school-related and school-associated PA and the WHO-5 Well-Being Index to evaluate subjective well-being. Differences in participants’ PA across segments of the school day before and after the pandemic were evaluated using the Kruskal–Wallis test, and compliance with PA recommendations was analyzed using cross-tabulation and Pearson’s chi-square tests. Results: After the pandemic, both boys and girls reported significantly lower levels of active transportation to and from school compared with the pre-pandemic period. In addition, well-being levels were significantly lower in both genders after the pandemic. Before the pandemic, boys and girls with higher well-being met the recommendations for PA to school, from school, and outside of school significantly more often than their peers with lower well-being. Higher levels of well-being were observed both before and after the pandemic in boys and girls who participated in organized PA compared with non-participants. Conclusions: This study confirms lower levels of PA and well-being among adolescents after the pandemic. In particular, PA to and from school was at a lower level after the pandemic than before the pandemic. Participation in organized PA was significantly associated with higher well-being in both boys and girls before and after the pandemic. Supporting adolescents’ participation in organized PA should be a priority when addressing the negative consequences of societal crisis situations. Improved knowledge of the associations between PA and well-being may contribute to more effective support for adolescents’ PA and greater awareness of the importance of meeting PA recommendations. Full article

Accurate building roof classification from airborne LiDAR point clouds is fundamental to reliable three-dimensional (3D) urban reconstruction. While supervoxel-based methods offer efficiency and resilience to uneven point density, their performance is critically undermined by cross-boundary segmentation errors—a direct consequence of random seed initialization that merges geometrically similar yet semantically distinct objects. To address this root cause, this study proposes Interior-Anchored Mean-Shift (IAMS), a novel supervoxel segmentation framework that rethinks seed placement as a geometry-aware interior localization problem. By integrating local geometric consistency point density, and spatial correlation into a unified kernel density estimator, supplemented by density-adaptive voxel weighting and a semi-variogram-driven bandwidth, IAMS reliably anchors seeds within object interiors, yielding highly homogeneous supervoxels without post-processing. Extensive experiments on three diverse airborne LiDAR datasets demonstrated that IAMS consistently outperformed state-of-the-art baselines. On the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark, our approach improved roof classification completeness, correctness, and quality by up to 7.1% (per-object) over the conventional Voxel Cloud Connectivity Segmentation (VCCS) algorithm while being significantly faster than recent boundary-preserving alternatives. Critically, IAMS maintains robust performance under challenging conditions, including sparse sampling and dense vegetation occlusion, making it a practical solution for real-world urban remote sensing. Full article

Understanding the psychological underpinnings of environmental decision-making is crucial for addressing climate change. Responsible consumption and pro-environmental behaviors often involve complex trade-offs between individual and collective outcomes, as well as between immediate and long-term consequences. Drawing on the Behavior Change Wheel and its core COM-B model—a comprehensive behavioral framework integrating Capability, Opportunity, and Motivation—this study systematically examines how the COM-B model has been applied in research on responsible consumption and environmentally relevant behavior. Using a combined bibliometric and content-analytic review of peer-reviewed studies indexed in the Web of Science between 2018 and 2026, we explore the focus, the behavior targets, and the contextual factors in existing COM-B applications. The findings reveal a focus on individual-level awareness, such as dietary behavior and sustainable lifestyles, while meso- and macro-level applications addressing institutional and policy mechanisms remain limited. By identifying a structural misalignment between the COM-B framework and its empirical applications, we contribute to behavioral science by highlighting the need to integrate structural determinants with individual processes to better understand and address the psychological mechanisms underpinning responsible decisions using this theoretical breadth. In this context, we emphasize the importance of aligning behavioral research priorities with the objectives of the United Nations Sustainable Development Goal 12. Full article

Learning transferable region embeddings is a fundamental problem in urban computing, as such representations support a wide range of downstream prediction tasks. Existing methods leverage multi-view and multimodal urban data but often fail to explicitly model spatial relations across views or effectively align general region embeddings with task-specific objectives. In this paper, we propose a spatial-aware Transformer (RE-SAT) network with semantic-guided prompting for urban region embedding. RE-SAT adopts a two-stage learning paradigm. In the first stage, a spatial-aware Transformer encoder injects connectivity and distance-based spatial priors into the attention mechanism to learn task-agnostic region embeddings from multi-view urban data. In the second stage, RE-SAT adapts the learned embeddings to downstream tasks via a semantic-guided prompt learning mechanism, which generates task-aware soft prompts from textual task descriptions without modifying the universal embeddings. Extensive experiments on multiple urban prediction tasks across different cities demonstrate that RE-SAT consistently outperforms state-of-the-art methods, achieving maximum relative improvements of 12.2% in MAE, 12.2% in RMSE, and 6.7% in R², validating its effectiveness and generalizability. Consequently, this framework serves as a robust decision-support tool for urban planners and policymakers, facilitating efficient resource allocation and intelligent city management across diverse scenarios. Full article

Background: Oral health during adolescence is a key determinant of long-term well-being and health equity. Despite widespread recognition of its importance, disparities in knowledge, motivation, and access to care persist. This study was conducted in Salt (Catalonia, Spain), a municipality with a population of approximately 33,000, characterized by a low average household disposable income (€12,512 per capita) and a high proportion of immigrant residents (37.76%). These sociodemographic characteristics may influence adolescents’ oral health behaviour, perceptions, and access to dental care. The study aimed to explore adolescents’ knowledge, habits, and attitudes towards oral health in this context, with barriers and protective factors, to inform community-based health promotion strategies. Methods: A qualitative descriptive study was conducted using focus group discussions with Spanish adolescents aged between 12 and 16, following ethical approval and informed consent from legal guardians. Data were systematically analysed using thematic analysis. Results: The adolescents had moderate oral health literacy, with basic knowledge of dental caries and prevention, but notable gaps in their knowledge regarding systemic consequences and complementary resources. Oral health behaviours and practices were shaped by social, economic, and normative influences, while parental involvement, community support, and school-based initiatives emerged as key assets for the promotion of oral health. Conclusions: While adolescents in Salt show awareness of oral hygiene, structural, motivational, and informational barriers limit comprehensive oral health practices. Interventions should move beyond knowledge-based education towards culturally adapted, participatory, and asset-based approaches to promote sustainable improvements in adolescent oral health. Full article

The study of resilience has long focused on understanding how individuals positively adapt to adversity, a process that directly influences emotional stability. Resilience, defined as the capacity to confront, overcome, and transform complex challenges constructively while strengthening oneself in the process, represents a transversal trait in human development. It also entails engaging in a personal growth trajectory that fosters self-awareness and internal coherence. Within the context of high abilities, this construct assumes particular significance, as students with high cognitive potential, but they are not immune to socio-emotional and educational vulnerabilities. These vulnerabilities may arise from asynchronies between intellectual and emotional development, among other factors, and influence specific coping strategies that, in turn, affect academic and social outcomes. Furthermore, high abilities students often have unique educational needs that may be insufficiently recognized or supported within their socio-cultural environments. Consequently, resilience in high abilities students should be understood as a dynamic process shaped not only by individual cognitive resources but also by contextual factors. A thorough analysis of the specific vulnerabilities of this population, and their interactions with environmental influences, is essential for fostering resilience and designing psychoeducational interventions that enhance academic achievement, promote inclusive practices, and support overall well-being. Full article

Transthyretin amyloidosis (ATTR) is a progressive multisystem disorder characterized by extracellular deposition of misfolded transthyretin fibrils, leading to neurological, cardiac, gastrointestinal, urogenital, sexual, and ophthalmological involvement. While disease-modifying therapies have significantly improved survival and slowed disease progression, a substantial proportion of patients continue to experience a high symptomatic burden that markedly impairs quality of life. Symptomatic manifestations often occur early, may precede the diagnosis, and frequently persist despite etiological treatment. This review provides a comprehensive overview of the symptomatic management of ATTR, with particular emphasis on autonomic dysfunction and its systemic consequences. We discuss current therapeutic strategies for orthostatic hypotension, gastrointestinal dysmotility, nutritional impairment, sexual dysfunction, lower urinary tract dysfunction, and ophthalmological involvement, highlighting both pharmacological and non-pharmacological approaches. Special attention is given to treatment limitations related to cardiac involvement, autonomic failure, and drug tolerability. Despite the clinical relevance of symptom control in ATTR, evidence-based recommendations remain scarce, and no dedicated guidelines currently exist. Most therapeutic approaches are derived from observational studies, expert opinion, and clinical experience. Improved awareness of symptomatic manifestations, early intervention, and a multidisciplinary, individualized approach are essential to optimize patient outcomes. Future research should focus on prospective studies and the development of structured symptomatic treatment algorithms to complement disease-modifying therapies and enhance patient-centered care in ATTR. Full article

Using the data of Chinese listed companies from 2011 to 2021 and the Digital Inclusive Finance Index from Peking University, this study investigates the impact of digital finance on the quality of corporate environmental information disclosure from both internal and external perspectives. The findings indicate the following: (1) Digital finance significantly enhances corporate environmental information disclosure quality, a conclusion that remains valid after a series of robustness tests. (2) Mechanism analysis shows that digital finance boosts disclosure quality by enhancing corporate environmental awareness and strengthening external oversight. (3) Heterogeneity analysis shows that digital finance more strongly enhances environmental disclosure quality for state-owned enterprises, firms in non-heavy pollution industries, and those located in regions with well-developed digital infrastructure. (4) Economic consequences analysis demonstrates that better disclosure quality, driven by digital finance, boosts a firm’s capital attractiveness, R&D investments, financing conditions, and green innovation. This process also triggers significant environmental spillover effects. The findings enrich theoretical research in digital finance and expand the discussion on enhancing environmental information disclosure. Full article

Organizations have been moving on-premises data functionalities to cloud storage environments. The need for advanced hybrid cryptography is deemed a promising solution for securing data on cloud storage. This scoping review explores the application of hybrid cryptographic systems for data hiding in cloud storage. It focuses on identifying global research trends, technological approaches, and contextual gaps in implementation. The review systematically examines the literature from major scholarly databases to identify existing models that combine traditional and modern cryptographic techniques to enhance data confidentiality, integrity, and authenticity against cloud-based security threats. Out of the 8250 eligible papers, 24 were included in the review. The findings reveal that the majority of scholarly contributions originate from Asia, averaging 87.5%, as reflected in the distribution of included articles by continent. Particularly, India and China dominate in the space, with a complete absence of studies from Africa, including South Africa. This geographical disparity underscores a significant research gap in the contextualization of hybrid cryptographic frameworks suited to Africa’s unique infrastructural and regulatory environments. The review further reveals a limited focus on the development of lightweight, scalable, and adaptable hybrid cryptographic schemes. Such approaches are essential for addressing challenges related to bandwidth limitations, computational efficiency, and regulatory compliance in developing regions. Consequently, this study contributes by establishing a comprehensive knowledge map of hybrid cryptography for cloud security, emphasizing the necessity for region-specific, context-aware frameworks. The findings provide a foundation for future investigations aimed at developing robust efficient hybrid cryptographic models that can strengthen data security in African cloud infrastructures. Full article

The overreliance on chemical pesticides in sub-Saharan African (SSA) for agriculture poses major challenges to sustainable agriculture, ecosystem and human health, biodiversity, and environmental sustainability. While genetically modified (GM) crops have demonstrated potential to lower pesticide use and increase crop yield, their widespread adoption remains limited across SSA, with gaps in knowledge on their yield, benefits and policies impacting their uptake. In this study, a literature-based approach was used to synthesize evidence from peer-reviewed articles and government reports published between 2010 and 2025 on pesticide use, farm productivity, and wellbeing of farmers across three focus countries: Nigeria, South Africa, and Burkina Faso. The summary of approved GM crops, events and utilisation across the three focus countries was also retrieved from the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. Cross-country comparisons were conducted to highlight lessons learned from successful and stalled GM crop programs and to identify regulatory, socio-cultural, and economic factors shaping adoption. It is shown that while GM crops can significantly reduce pesticide usage and production costs, challenges such as public hesitancy, regulatory hurdles, limited farmer awareness, and concerns about ecological consequences continue to hinder wider uptake across the continent. Similarly, weak seed systems and the lack of regionally harmonized biosafety regulations also constrain adoption. In areas where GM crops have been successfully adopted, it was demonstrated that supportive policy frameworks, transparent biosafety regulations, effective seed certification and distribution systems, and sustained community engagement increased farmer confidence and accelerated adoption. Hence, for GM crops to be more widely adopted for sustainable crop protection in sub-Saharan Africa, governments and stakeholders must strengthen biosafety systems, invest in farmer education, promote regional regulatory coordination, and facilitate public–private partnerships. Full article

Full-duplex (FD) technology holds great promise for enhancing the spectral efficiency of Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). However, the practical performance gain of FD over Half-Duplex (HD) is highly sensitive to the dynamic nature of traffic loads and residual self-interference. Existing Optimal Dynamic Selection Strategies (ODSS) often rely on static workload assumptions within a single time window, failing to capture long-term traffic fluctuations. Consequently, applying instantaneous switching strategies in highly bursty environments necessitates excessively frequent mode switching (e.g., the switching frequency can approach the total number of time windows), incurring prohibitive signaling overhead and unignorable MAC-layer adaptation delays. To overcome these concrete bottlenecks, this paper proposes a comprehensive traffic-aware adaptive duplex mode selection framework. First, we model the multi-scale dynamic workload using Dynamic Activated Probability in Short-term (DAPS) and Long-term (DAPL), effectively characterizing both bursty traffic (via Beta distribution) and Markov-modulated stable traffic. Second, by integrating physical layer performance analysis, we define the Break-even Workload Point (BWP) to partition traffic into Oversaturated (OZ) and Unsaturated (UZ) Workload Zones (WZs). Furthermore, to handle unknown future traffic with low complexity, we propose the Pre-scheduling Duplex selection based on the Workload zone Estimation (PDWE) algorithm. PDWE leverages a Hidden Markov Model (HMM) combined with a Rollout algorithm to estimate hidden traffic states and adaptively pre-schedule duplex modes. Simulation results demonstrate that the proposed strategy achieves near-optimal throughput (approximately 91% of the ideal ODSS) while reducing the duplex switching frequency by two orders of magnitude compared to instantaneous switching strategies. This approach offers a robust cross-layer solution for next-generation self-organizing networks. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

Reliable evaluation of large language models (LLMs) for educational use requires benchmarks that reflect exam constraints, instructor grading practices, and the operational consequences of thresholded decisions. This paper introduces ExamQ-Gen, an instructor-in-the-loop benchmark that couples two tasks: (i) an LLM answering university-style exam questions and (ii) decision-support grading aligned with an instructor reference. Automatic grading is used for triage and feedback; in practice, ExamQ-Gen supports instructor-led exam authoring and provides grading recommendations, while the instructor issues the final grade and pass/fail decision. ExamQ-Gen is constructed from the course content by using an LLM to generate exam-style questions directly from the lecture materials, producing a course-derived question set suitable for controlled experimentation. The benchmark then instantiates contrasting exam conditions, including instructor-authored (HUMAN) versus pipeline-generated (PIPELINE) artifacts, to evaluate robustness under distribution shifts that can occur when exam questions and answers are produced through different generation workflows. Using two LLM “students” (Llama3-8B-Instruct and Mistral-7B-Instruct) and an LLM-based grader, we compare automatic grading against an instructor reference on a 1–10 score scale and at the decision level induced by the operational pass policy (pass if score ≥ 9). Accordingly, our conclusions are conditioned on the two evaluated student models. Score-level agreement is strong under HUMAN conditions but degrades substantially under PIPELINE conditions, indicating condition-dependent stability. At the pass threshold, decision errors are highly asymmetric, with false fails dominating false passes, meaning that conservative grading may appear safe while producing credit denial. A severity-focused analysis isolates a high-stakes failure mode—denial of instructor-perfect answers—and shows that, in the most affected PIPELINE condition, the perfect-pass miss rate reaches 0.926 (50/54), consistent with systematic conservatism rather than borderline noise. Overall, the results highlight that aggregate score agreement and accuracy are insufficient for instructor-controlled exam deployment and motivate reporting practices that combine disaggregated score agreement, threshold-based error asymmetry with uncertainty, and severity-aware diagnostics under exam-relevant condition shifts. Full article