Search Results (278)

Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To address this limitation, this paper proposes a distributed optical fiber sensing (DOFS)-driven span-level resilience assessment and hardening optimization framework for transmission networks. First, a phase-sensitive optical time domain reflectometry (Φ-OTDR)-based distributed optical fiber sensing system is employed, utilizing optical fibers embedded in existing OPGW cables as sensing media. By capturing vibration responses of the fiber induced by wind–structure interaction, real-time spatiotemporal wind speed sequences at the individual span level are reconstructed through signal processing and inversion algorithms, providing high-spatial-resolution environmental input data for resilience evaluation. Second, a span-level failure probability quantification method is established using a load–strength interference model. On this basis, a resilience evaluation framework—“span-level asset damage cost—line-level critical corridor identification—system-level load shedding assessment”—is constructed, enabling cross-scale resilience quantification from component damage to system-level performance degradation. Third, a span-level gradient hardening optimization model is developed. By adopting a scenario pre-calculation and iterative updating strategy, coordinated solving of reinforcement decisions and failure scenarios is achieved, thereby maximizing resilience enhancement benefits. The proposed framework is validated using DOFS-measured wind speed data collected from a 500 kV transmission line along the Fujian coast during three real typhoon events—Typhoon Shantuo, Typhoon Trami, and Typhoon Koinu—supporting the reliability of the acquired span-level wind speed information. Case studies conducted on a modified IEEE RTS-24 system demonstrate that the proposed span-level hardening strategy can substantially reduce reinforcement cost compared with the conventional line-level hardening strategy. In the reported benchmark case, it achieves zero load-shedding penalty with a markedly lower hardening cost, and under the same budget constraint, it further yields lower expected load shedding and lower expected asset damage. Full article

With the continuing intensification of modern agriculture, pesticides and antibiotics are extensively used to control pests and diseases, but their improper use and indirect inputs have resulted in widespread contamination of agricultural environments and food products. This review synthesizes how these contaminants enter agroecosystems, their occurrence across soils, waters and agricultural products, and the processes that redistribute residues across air–water–soil interfaces and into the soil–plant continuum. We summarize cross-media transport pathways (e.g., runoff/leaching, volatilization–deposition and irrigation-driven redistribution) and relate environmental exposure to plant uptake using a harmonized indicator set, including the bioconcentration factor (BCF), translocation factor (TF), octanol–water partition coefficient (log Kow) and soil organic carbon–water partition coefficient (Koc). We further discuss key determinants of crop accumulation, including compound-specific properties, soil characteristics and plant physiological traits, and highlight how these factors jointly shape residue profiles in edible tissues. Finally, we outline research priorities for source reduction, standardized multi-matrix surveillance, fate-to-uptake modeling, and microbiome-enabled remediation strategies to support pollution control, food safety and public health. Full article

Background/Objectives: Cardiovascular disease (CVD) in chronic kidney disease (CKD) arises from a multifaceted interplay of pathophysiological processes, including chronic inflammation, oxidative stress (OS), and accelerated vascular calcification (VC). Red blood cell distribution width (RDW) and the neutrophil-to-lymphocyte ratio (NLR) have emerged as simple, inexpensive, and readily available hematological indices that may capture these underlying disturbances. As such, they hold promise as accessible biomarkers for stratifying cardiovascular risk in patients with CKD. Methods: This cross-sectional study enrolled 497 patients, comprising 477 with CKD across all stages and 20 controls. We evaluated the associations of RDW and NLR with both traditional and non-traditional cardiovascular risk factors, as well as with serum calcification propensity (T50). Spearman’s correlation and multivariable regression analysis were used to assess these relationships. Results: Both RDW and NLR were significantly elevated in patients with established CVD (p < 0.001 for both) and demonstrated a progressive increase across advancing CKD stages (p < 0.001). RDW and NLR showed positive correlations with age, CVD duration, urea, phosphorus, parathormone, CRP, FG23, and mean carotid intima–media thickness (cIMT), while exhibiting inverse correlations with eGFR, serum albumin, hemoglobin, lipids, antioxidants such as superoxide dismutase, fetuin-A, and T50. Additionally, NLR correlated positively with the duration of hypertension and diabetes, as well as with albuminuria. Quartile analysis revealed a stepwise decline in T50 across increasing categories of RDW and NLR, supporting the link with impaired calcification defense. In multivariable analysis, T50 independently predicted NLR (β = −0.013; p = 0.018), whereas total cholesterol (β = −0.011; p = 0.019) and cIMT (β = 0.38; p = 0.018) emerged as independent determinants of RDW. Conclusions: RDW and NLR strongly reflect the burden of inflammation, metabolic disturbance, and vascular dysfunction in patients across the CKD spectrum. The consistent associations with impaired calcification defense and with established cardiovascular risk markers underscore the potential value as accessible indicators of cardiovascular vulnerability in CKD. These findings support incorporating RDW and NLR into routine risk assessment and highlight T50 as a mechanistically relevant determinant of hematologic inflammation profiles. Full article

In the last decade, as cities increasingly seek sustainable development pathways within the cultural and creative economy, cultural-creative events have gained prominence as strategic instruments for urban transformation. Among them, city design weeks have emerged as complex service systems that connect creative industries, urban governance, and tourism development. This research aims to understand how cultural-creative events (represented by design weeks) facilitate sustainable tourism development from a service design perspective. Adopting a qualitative comparative research design, the study examines 30 design weeks selected through a cross-validated process with the World Design Weeks global network and UNESCO City of Design network. Data from 2020 to 2025 is collected primarily through expert interviews, official reports, and media materials in relation to the United Nations Sustainable Development Goals (SDGs). Grounded in the service design perspective, four Service Design Levels are summarized into 17 assessment dimensions, and experts applied Likert scale to evaluate the relative service intensity of each case. Through cross-case analysis, the findings reveal four distinct models of design weeks, reflecting different configurations of service intensity and strategic orientation. The study contributes theoretically by extending service design theory to cultural-creative tourism research, and practically by providing guidance for the organizers of cultural-creative events seeking to support sustainable city tourism development. Future research may incorporate quantitative impact assessments to further refine these models. Full article

Background: Problematic Internet use (PIU) among adolescents has emerged as a significant public health concern, associated with the types of online activities and underlying psychological processes rather than screen time alone. Methods: This cross-sectional study included 750 adolescents (46.4% female) with a mean age of 15.39 years (SD = 1.76; range = 13–19) recruited from 7th and 8th grade primary school students and secondary school students in Split-Dalmatia County (Croatia). The study investigated the associations between specific online activities, psychological variables, and PIU using hierarchical regression and multiple mediation analyses. Results: Results revealed that time spent online remains the most strongly associated with PIU. While streaming and online shopping showed significant associations with problematic use, reading and browsing for information did not. Stress and loneliness were identified as variables associated with higher that significantly statistically mediate the relationships between online engagement and PIU, whereas emotional competence functioned as a protective factor, particularly in relation to social media use. These findings support theoretical models that conceptualize PIU as a maladaptive coping strategy for psychological distress. Conclusions: Based on these findings, prevention efforts should move beyond simple screen-time reduction and focus on strengthening adolescents’ emotional competence, stress management, and self-regulatory skills to promote healthier and more adaptive patterns of digital engagement. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article

Background/Objectives: Sustainable food habits are essential for reducing the environmental impacts of a food system. We investigated food sustainability knowledge, attitudes, and food waste-related behaviours among Saudi adults and assessed their associations with socio-demographic characteristics and dietary choices, which are subjects that remain under-researched. Methods: A cross-sectional study was conducted among 855 Saudi adults (≥18 years) using convenience sampling. Data were collected using a validated online questionnaire assessing sustainability knowledge, attitudes, food waste behaviours, dietary choices, and socio-demographic characteristics. Descriptive statistics, chi-square tests, and linear regression analyses were performed using SPSS version 29. Results: Overall, 32% of the study population demonstrated adequate sustainability knowledge, 61% expressed positive attitudes towards food sustainability, and 45% demonstrated favourable food waste management. Women were more knowledgeable than men. Participants who possessed a better understanding of food sustainability consumed more vegetables, fruits, and bread and less processed meat. Those with a positive attitude towards food sustainability exhibited higher consumption of red meat, white meat, eggs, milk, yogurt, cheese, vegetables, fruits, bread, and sweet or savoury snacks. Meanwhile, individuals with better food waste behaviours demonstrated significantly lower consumption of legumes, fish, pasta, red meat, white meat, processed meat, eggs, milk, yogurt, cheese, fruits, bread, and sweet or savoury snacks. Conclusions: Saudi adults possess limited knowledge of sustainability and suboptimal food waste behaviours despite having relatively positive attitudes. These findings highlight opportunities for intervention. Public education, targeted campaigns, and media communication could enhance sustainability awareness and promote healthier, environmentally sustainable dietary patterns. Full article

Social media influencers (SMIs) have become effective intermediaries that influence consumer perceptions, attitudes, and behavioral intentions through their online presence and persuasion skills; this has made it imperative to comprehend how buyer-related variables contribute to brand loyalty within contemporary marketing research. This study, therefore, examines the effect of social media influencer capability on brand loyalty in Saudi Arabia, using brand trust as a mediating variable and authentic leadership as a moderating variable. Utilizing Social Exchange Theory and Authentic Leadership Theory, the study applied a quantitative cross-sectional survey design. Data were purposively collected from 476 active social media users in three major commercial hubs in Saudi Arabia (Riyadh, Jeddah, and Dammam). The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that authenticity and communication skills have a positive and significant influence on brand trust and brand loyalty, but expertise and influence only have a significant and positive influence on brand trust, not on brand loyalty directly, which means that the two constructs are indirectly influencing brand loyalty. The study also finds that authentic leadership significantly moderates the relationship between expertise, influence, and communication skills and brand loyalty, while the interaction with authenticity is not significant. Moreover, the mediation analysis shows that brand trust plays a significant mediating role in the relationships between communication skills, expertise and influence and brand loyalty, implying that the antecedents play a leading role in fostering loyalty by first developing trust. The study contributes to theory by offering a process-based perspective on the concept of brand loyalty that positions brand trust as a fundamental mechanism and authentic leadership as a vital enabling context. The findings have practical implications for organizations that want to strengthen brand loyalty through authentic communication, trust-building strategies, and leadership practices in social media-based contexts. Full article

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article

The intensive use of social media has transformed the processes of accessing, consuming, and circulating information, positioning adolescents as one of the groups most exposed to digital misinformation. Despite their high connectivity, numerous studies show limitations in their ability to critically evaluate the content they consume and share in digital environments. In this context, this article aims to analyze, through a systematic review of the scientific literature, the role of educational institutions in the media literacy of adolescents in the face of the impact of misinformation on social media. The research was conducted following the PRISMA 2020 guidelines, and the protocol was registered in PROSPERO. An exhaustive search was conducted in the Scopus, Web of Science, and PubMed databases, considering studies published between 2019 and 2025 in English and Spanish. Following the selection process, 46 studies were included in the qualitative synthesis and 18 in the meta-analysis. Methodological quality was assessed using the PEDro and AMSTAR 2 scales. The results show that educational interventions in media literacy generate significant improvements in adolescents’ ability to identify misinformation and reduce their intention to share misleading content, especially those based on skimming, source verification, and cognitive inoculation. It is concluded that media literacy, integrated in a cross-cutting and sustained manner into the school curriculum, is a key strategy for mitigating the impact of misinformation and strengthening critical thinking in adolescents. Full article

Inflammatory bowel disease (IBD) requires sustained patient engagement in complex therapeutic and self-management processes, making health literacy (HL) a key determinant of effective care. This cross-sectional study assessed HL levels among adults with IBD attending a public day-hospital service in Lisbon, Portugal, and examined associations with sociodemographic, lifestyle, and selected clinical variables. A convenience sample of 280 participants completed a self-administered questionnaire, including the Portuguese version of the European Health Literacy Survey Questionnaire (HLS-EU-PT-Q16). Descriptive statistics, bivariate analyses, and multiple linear regression were used. HL indices were computed and categorized into proficiency levels; domain- and competency-specific indices were also analyzed. Overall, 48.3% of participants had inadequate or problematic HL, whereas 42.5% had sufficient HL. Healthcare-related HL showed the most favourable profile, whereas health promotion emerged as the weakest domain, with domain-specific mean indices ranging from 31.8 ± 8.3 to 34.4 ± 7.4 on a 0–50 scale. Competency-specific indices indicated that appraisal and, particularly in disease prevention, application were the lowest, and item-level analyses highlighted difficulties with mental health information-seeking and evaluating or acting on media-based health information. In multivariable linear regression analysis, higher educational attainment was positively associated with HL (B = 0.89; 95% CI: 0.05 to 1.73; p = 0.039), whereas female sex was independently associated with slightly lower HL scores (B = −1.72; 95% CI: −3.33 to −0.11; p = 0.036). These findings indicate that nearly half of patients with IBD in a day-hospital setting experience HL-related vulnerabilities, especially beyond clinician-mediated care. Targeted, HL-sensitive interventions focusing on critical appraisal and decision-to-action support may enhance self-management and equity in IBD care. Full article

Social media platforms have become primary communication channels for technical European universities. However, the extent to which global platform algorithms homogenize individual preferences across cultures remains underexplored. Although the current literature offers insights into the topic, none of the works consider the cross-national and multimodal nature of the phenomenon. In the current paper, we introduce the Media Information Processing System (MIPS), a privacy-preserving multimodal deep learning (DL) framework that incorporates large language models (LLMs), computer vision (CV), and knowledge graphs. We analyze data from 15,520 public videos shared by 2359 followers of six top technical universities from Romania, Germany, Italy, and Russia. The results of the study suggest that the degree of homogeneity of the followers’ interest profiles is markedly high. Statistical profiling of the data indicates that the interest profiles of the followers from different countries are positively correlated with a high degree of strength (mean Pearson r = 0.96; p > 0.90). Consensus clustering of the data reveals the existence of stable clusters of themes with high stability scores (>0.75), such as “Human Interaction Dynamics”. The results of the study contradict the traditional theory of regional cultural differentiation. Instead, the results suggest the existence of a new “digital student persona” that is characteristic of the academic lifestyle of students from different countries. Full article

Multimodal named entity recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), scalable vector graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full-Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model-generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding. Full article

Two-handed dynamic gesture recognition represents a fundamental component of sign language interpretation involving the modeling of temporal dependencies and inter-hand coordination. In this task, a major challenge is modeling asymmetric motion patterns, as well as bidirectional and long-range temporal dependencies. Most existing frameworks rely on early fusion strategies that merge joints, keypoints or landmarks from both hands in early processing stages, primarily to reduce model complexity and enforce a unified representation. In this work, a novel dual-stream BiLSTM–Transformer model architecture is proposed for two-handed dynamic sign language recognition, where parallel encoders process the trajectories of each hand independently. To capture spatial and temporal dependencies for each hand, an attention-based cross-hand fusion mechanism is employed, with hand landmarks extracted by the MediaPipe Hands framework as a preprocessing step to enable real-time CPU-based inference. Experimental evaluation conducted on custom Romanian Sign Language dynamic gesture datasets indicates that the proposed dual-stream-based system outperforms single-handed baselines, achieving improvements in high recognition accuracy for asymmetric gestures and consistent performance gains for synchronized two-handed gestures. The proposed architecture represents an efficient and lightweight solution suitable for real-time sign language recognition and interpretation. Full article

This study examines the tension between audience interests and editorial lines in the major national and regional digital media outlets in Chile. It analyzes how editors incorporate metrics and user feedback into content selection and prioritization processes. The sample included the five websites with the largest national reach according to the 2024 ComScore ranking (El Mercurio Online, BioBioChile, La Tercera, Megamedia and Chilevisión), along with digital media outlets from the country’s five most populous cities without counting the capital (La Serena, Rancagua, Antofagasta, Valparaíso, and Temuco). Semi-structured interviews were conducted with directors or editors to assess whether the use of metrics influences journalistic judgment and editorial autonomy. Data were analyzed through a thematic analysis, combining categories drawn from the literature with emergent codes. The findings indicate that audience feedback affects editorial decision-making, although to varying degrees depending on the type of outlet. In national newspapers, a fiduciary vision is more firmly sustained due to greater financial capacity, albeit with internal tensions. In contrast, regional media outlets face greater challenges in maintaining their editorial line in the face of metrics, as lower economic stability and dependence on digital traffic tend to favor dynamics closer to a market-driven model. Although the findings are based on professional discourse and do not include direct observation of production routines, the comparison between national and regional media offers a cross-cutting perspective on editorial autonomy within the Chilean digital media ecosystem, an area that remains underexplored in the country. Overall, the study shows that metrics place pressure on both editorial policy and journalistic practices by requiring a continuous balancing of professional judgment and real-time audience behavior. Full article