Search Results (24,861)

Background: Hatha yoga has gained increasing popularity worldwide and has been associated with benefits for mental health and short-term emotional functioning. Objective: The present study examined pre–post changes in mood states following a single Hatha yoga session in adult women participating in community-based exercise programs. Methods: A total of 253 adult women participated in the study. Participants completed the Profile of Mood States (POMS) questionnaire immediately before and after a single 60 min Hatha yoga session. The questionnaire assesses anxiety–tension, depression, anger, fatigue, confusion, and vigor. Repeated measures ANOVA was used to examine the changes in mood states and the potential differences between the age groups. Results: Significant improvements in mood states were observed following the session. Anxiety–tension, depression, anger, fatigue, and confusion decreased, while vigor increased. No significant time × age group interaction was observed for most mood variables. However, a significant interaction was found for vigor, indicating that women aged 41 and older showed a greater increase following the session. Conclusions: Participation in a single Hatha yoga session was associated with short-term changes in mood states among adult women, suggesting that yoga may represent a potentially beneficial community-based activity for supporting short-term mood regulation. Full article

Filipino Sign Language (FSL) serves as a vital means of communication for the Deaf and hard-of-hearing in the Philippines. However, its societal use remains limited due to the scarcity of qualified interpreters and the general lack of FSL literacy among the population. Therefore, this study aims to address the gap between FSL development and automated FSL translation by employing machine learning and computer vision techniques. A model was trained using the FSL-105 dataset, which comprises video clips of gestures related to greetings and colors, and utilized MediaPipe for real-time detection of hand, face, and body landmarks. Through iterative training with transfer learning, the model’s performance improved from an initial accuracy of 80% to a final accuracy of 98.75%. The results demonstrate that the MediaPipe-based model can reliably interpret FSL gestures, positioning it as a potentially accessible assistive tool for the Deaf and hard of hearing community. This technology holds promise for applications in education, healthcare, and public service, offering new opportunities to promote the social inclusion of Filipino Deaf communities through more inclusive communication. Full article

Background/Objectives: Sarcopenia and age-related muscle weakness pose significant global health challenges, highlighting the need for innovative and sustainable exercise interventions for older adults. This study developed and evaluated an Interactive Smart Mirror Exercise Program and investigated the factors associated with older adults’ usage intention toward the program based on the Decomposed Theory of Planned Behavior (DTPB). Methods: A cross-sectional survey was conducted with 92 community-dwelling older adults in northern Taiwan. Structural equation modeling was applied to test the proposed framework and examine the relationships among the study variables. Results: The results showed a satisfactory model fit (SRMR = 0.071). Attitude, subjective norms, and perceived behavioral control together explained 41.6% of the variance in usage intention. In addition, perceived usefulness, perceived compatibility, interpersonal influence, and self-efficacy were identified as factors significantly associated with usage intention, both directly and indirectly. Conclusions: These findings might support the applicability of the DTPB framework in explaining older adults’ usage intention toward technology-assisted exercise programs and provide insights for the design and implementation of digital exercise interventions in community settings. Full article

This study examines the evolution of the scientific literature on mining and heavy metals, with a particular focus on biosanitary risks associated with childhood exposure. The research integrates a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, combined with a bibliometric analysis of Scopus-indexed publications, international epidemiological data, and an evaluation of the socio-environmental context in Ecuadorian mining regions. The PRISMA-based screening process was applied to identify, filter, and select relevant peer-reviewed studies, enabling the delimitation of a focused corpus of literature, with particular attention given to scientific contributions produced by Latin American researchers and institutions. The results reveal a significant concentration of knowledge production among a limited number of countries and institutions, the dominance of English as the main language of scientific communication, and the centrality of journals in environmental sciences and toxicology. While notable progress has been made in identifying contaminants and exposure pathways, governance structures, territorial disparities, and policy implementation processes remain insufficiently explored. In Ecuador, the rapid growth of mining concessions in ecologically sensitive zones presents potential threats to children’s neurocognitive development, highlighting the urgent need for ongoing surveillance, biomonitoring programs, and preventive public health measures. The study emphasizes the importance of strengthening regional research capacity and fostering more equitable international scientific collaborations to ensure that knowledge production is responsive to local contexts and effectively safeguards vulnerable populations. Full article

Existing studies indicate that preventive conservation (PC) has become a key strategy in heritage risk management. For traditional villages with limited resources, community participation is necessary for the implementation of risk management. However, current research mostly focuses on case-based analyses and lacks a systematic synthesis of community participation in PC. To address this gap, this study conducts a systematic review of community participation in PC from historic rural areas and relevant heritage types. Following the PRISMA guidelines, 39 eligible studies were included in the final analysis. The findings show that effective participation depends on well-defined organizational and coordination networks. Internal organizational structures support coordinated efforts among community members, while external collaborative frameworks provide ongoing resources and support. Within this structure, capacity-building translates participatory arrangements into concrete risk-governance actions, thereby influencing how roles are assigned and adapted within the community across different risk scenarios. Based on these insights, this study presents an Organization–Capacity–Role framework that offers a way for risk governance of historical rural areas. Notably, the detailed arrangements in this framework are shaped by local institutional settings, specific risk types, and case contexts, demonstrating a strong contextual dependency. Full article

As 5G Standalone Core networks grow, Application Programming Interface (APIs) have become a key part of how network systems talk to each other. They allow different functions to share data and complete tasks quickly. However, this also makes them targets for attacks. 5G Standalone Core networks rely on Service-Based Architecture (SBA), where network functions communicate through exposed APIs. These APIs are attractive targets for cyberattacks because they are externally accessible, handle sensitive control-plane operations, and exchange structured data using Hypertext Transfer Protocol version 2 (HTTP/2) and JavaScript Object Notation (JSON) protocols. Most older security tools work using fixed rules, which cannot always detect new or changing threats. This study aimed to fix that gap by using Artificial Intelligence to make API security smarter. Two AI models were tested: Long Short-Term Memory (LSTM), which learns from past traffic and Reinforcement Learning (RL), which learns by adapting to network behavior. Both were used to assess API traffic and assign a real-time risk score. Synthetic traffic was created using Python, including both normal API calls and different types of attacks like Distributed Denial-of-Service (DDoS), brute force, and Structured Query Language (SQL) injection. The results show that both LSTM and RL models were better than traditional rule-based systems. They found more threats, gave fewer false alerts, and responded faster. RL was especially strong at handling unknown or changing attacks. Experimental results show that the proposed LSTM and RL models achieved approximately 95% detection accuracy, significantly outperforming the static rule-based baseline model, which achieved 58% accuracy. The results demonstrate the effectiveness of adaptive AI-based security mechanisms for detecting evolving API threats. This research shows that AI can help protect 5G APIs in a smarter and more flexible way. It can support telecom networks by making threat detection faster, more accurate, and ready for future challenges. Full article

High-quality genomic DNA extraction remains a major bottleneck for fungal genomics, particularly for worldwide aerobic and non-photosynthetic mushroom species that rely on their rigid cell walls, interference between metabolites, polysaccharides, etc., and complex genomes. This study systematically compares five DNA extraction protocols involving four distinct sample preparation procedures (fresh (A), filtered (B), frozen (C) and cryogenic mycelium (D)) across mycelial cultures of eight Tunisian fungal strains representing Ascomycota and Basidiomycota to identify the optimal combination for genomic DNA extraction from mycelium. The eight phylogenetically diverse fungal species were analyzed using short-read (MiSeq and NextSeq550) and/or long-read (MinION Mk1C) sequencing technologies, giving a depth coverage between 3.7× and 83×. The generation and quality of the assemblies were assessed within the Galaxy platform, which revealed a gap percentage of 0–0.509%. Taxonomic characterization and phylogenetic inference were performed with SANGER technology using the Internal Transcribed Spacer (ITS) and D1/D2 region of the 26S rRNA gene, assigning the species to our eight different strains: Clitopilus baronii (BS6), Porostereum spadiceum (BS200), Trametes versicolor (BS22-9), Schizophyllum commune (BS23-13), Gloeophyllum abietinum (BS23-14), Irpex laceratus (BS100), Trichoderma asperellum (GC9) and Trichoderma harzianum (S3). The optimized DNeasy Plant Pro Kit protocol with cryogenic biomass treatment presents a safe and cost-effective method for fungal genome sequencing and taxonomic resolution. This integrated comparative evaluation of extraction for sequencing identifies an optimal Qiagen-based extraction strategy combined with cryogenic treatment for eight diverse Tunisian fungal species, guiding method selection based on specific cell wall characteristics rather than proposing a universal protocol limited by unequal replication and strain numbers. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter data collected from domestic consumers in a medium-sized city in southern Spain. In particular, we assess Hierarchical Temporal Memory (HTM), a biologically inspired online sequence learner, against a family of Long Short-Term Memory (LSTM)-based recurrent baselines. HTM offers continual adaptation and avoids a separate training phase, whereas LSTM relies on offline supervised training and may require retraining or fine-tuning under distribution shift. For five-step-ahead forecasting, HTM achieved a test RMSE of 251 kWh (about 15% of average consumption). After hyperparameter optimization, the best tested LSTM configuration achieved a test RMSE of approximately 250 kWh under clean conditions, indicating nearly identical point accuracy between the two approaches. Under synthetic Gaussian-noise injection, however, HTM remained comparatively stable, whereas the optimized LSTM configuration degraded markedly under the tested perturbation protocol. In addition, HTM exhibited a lower runtime in the tested CPU-based implementation. These findings suggest that HTM is a viable online alternative for aggregated smart-meter forecasting, offering competitive accuracy together with a favorable operational profile under the specific evaluation setup considered here. Full article

This study assesses the elasticity between integrated media performance and corporate reputation by examining the relationship between Media Output Score (MOS) and RepScore™ in the Portuguese telecommunications sector (Altice/MEO, NOS, and Vodafone) between 2021 and 2023. Adopting a longitudinal observational design, the analysis compares inter-annual variation in communication output with corresponding changes in stakeholder-based reputation. Media performance is operationalized through MOS as a composite indicator of visibility, favorability, readership, targeting, and social amplification, while corporate reputation is measured using third-party RepScore™ data. The findings indicate directional alignment between media output and corporate reputation; however, the magnitude of reputational adjustment appears substantially lower than the amplitude of media volatility. Across heterogeneous crisis contexts, including cybersecurity incidents and governance-related events, reputational scores exhibit incremental and comparatively stable evolution despite pronounced fluctuations in media performance. These results suggest that the relationship between media output and corporate reputation is characterized by constrained responsiveness at the annual level, consistent with a stock–flow interpretation in which communication signals operate as high-variance flows and reputation evolves as a path-dependent stock. By empirically illustrating this asymmetry, the study contributes to media influence research by identifying a structural boundary condition in the translation of media exposure into stakeholder evaluation. The findings further clarify the analytical distinction between output-level communication metrics and outcome-level reputational constructs in digital media environments. Full article

Recent studies suggest that prolonged participation in formal music programs may be associated with the cultivation of resilience and locus of control (LoC) in music students. Brazilian musicians, who were attending or had attended community-based music programs, and a group of matched, untrained individuals from disadvantaged, urban communities completed the Connor–Davidson Scale of Resilience (RISC), the Craig Locus of Control Scale, and the ABEP 2022—Brazilian Criteria of Economic Classification questionnaire. Results suggested that while musical participation alone was not associated with resilience and LoC scores (model 1), a conditional restriction of the same model (model 2) showed a significant interaction between musical participation, age, and RISC and LoC scores, after controlling for SES. Among musicians, higher age was associated with higher resilience scores and internal LoC. Findings from this exploratory study are discussed in light of the multifaceted nature of community-based music programs, the building blocks of resilience and LoC. We also comment on the potential links between resilience and LoC in relation to musical participation and well-being. Limitations of this study are discussed alongside implications for future research. Full article

The current study provides a comprehensive overview of the socioeconomic and sociodemographic conditions of Vietnamese fishing crews, who form the backbone of the nation’s marine capture fisheries but remain among the most vulnerable labor groups. Based on interviews with 2037 captains and crew members across six coastal provinces, the study examines demographic characteristics, education, working conditions, legal arrangements, and income determinants. Results show that the fishing labor force is entirely male, predominantly middle-aged, and characterized by limited formal education and long occupational experience. Employment relationships are largely informal and verbal, leaving crews without labor protection, social or health insurance, or contractual stability. Statistical analysis revealed significant income disparities between captains and crew members, between inshore and offshore fleets, and among fisheries and provinces. Fishing experience and professional certification were positively correlated with income, highlighting the importance of skill development. The findings underscore the urgent need for socioeconomic policies that formalize labor contracts, expand insurance coverage, promote vocational training, and modernize fishing technologies. These measures, combined with income diversification and community welfare programs, are critical to improving the well-being, safety, and resilience of Vietnam’s fishing workforce and advancing sustainable marine economic development. This study provides valuable baseline information on an underrepresented segment of the commercial fishing industry, informing fisheries managers and policymakers in designing future development programs that account for the socioeconomic and demographic conditions of fishing crews. Full article

Processed parameters from sensors located on seismically isolated buildings (maximum acceleration, spectral acceleration and oscillation periods) are compared against free-field ground motion to evaluate the improvement in seismic response for these buildings. The study is carried out for three structures in Bucharest, the capital city of Romania. The data used in this research correspond to moderate magnitude earthquakes, 4.2 ≤ M_W ≤ 5.5 generated from the Vrancea-intermediate-depth seismic area, with focal depths greater than ~90 km. The methodology helps to evaluate amplification/reduction in the seismic motion, and confirmed that base-isolation devices reduce the seismic parameters’ amplitudes of the structure directly above the isolating layer. The effectiveness of the base-isolation technique is further assessed by comparing the amplitude of the seismic parameters recorded under and above the earthquake protection devices. The results show a clear decrease in the values right above the isolating system, supporting the efficiency of base-isolation systems. The outcomes provide necessary empirical data for refining seismic design and improving the resilience of critical structures. The work contributes to the mitigation of the seismic risk in the city area, thus targeting a more resilient urban community and sustainable city through implementation of modern base-isolation systems for the retrofitting of vulnerable buildings exposed to a high risk of seismic hazards. Full article

Green communities play a critical role in advancing sustainable development; however, evaluating their performance and identifying appropriate improvement strategies remain challenging due to uncertain, incomplete, and multidimensional information. This study formalizes three key processes essential to green community governance—sustainability evaluation, attribute reduction, and decision-rule generation—and proposes a rough set-based decision framework that integrates quantitative indicators, expert knowledge, and rule-based reasoning. Using empirical assessment data from Nantou County, the framework identifies the most influential determinants of community performance, including accessibility-related facilities, remote-area status, and socioeconomic conditions. The results reveal clear drivers of sustainable community performance. Remote villages lacking community hubs face structural barriers to participation. Communities without facilities supporting vulnerable groups tend to stall at the registration stage, while bronze-level villages require equity-focused engagement despite possessing stronger resource endowments. Notably, silver-level performance is consistently associated with moderate income levels and moderate income disparity, underscoring socioeconomic balance—rather than economic extremes—as a key precondition for stable sustainability advancement. Together, these findings provide interpretable, evidence-based guidance for policymakers and community managers to identify performance gaps and allocate resources more effectively. Full article

As image–text posts have become a dominant form of social media communication, understanding how the two modalities jointly convey meaning remains a key challenge in multimodal analysis. This study aims to examine whether image–text consistency is inherently multidimensional rather than reducible to a single similarity metric. Existing studies often reduce consistency to a single relevance score, which cannot capture semantic, emotional, and functional interactions. We construct a dataset of 28,650 multimodal posts and model image–text relationships along three dimensions: semantic consistency (CSC), emotional consistency (CEC), and informational matching consistency (IMC). Semantic and emotional alignment are measured using cross-modal representation and similarity computation, while IMC is defined through rule-based classification of informational roles. Results show that emotional consistency (CEC = 0.621) is higher than semantic consistency (CSC = 0.549, $p<0.001$), while 61.0% of posts maintain consistent informational orientation. These findings demonstrate that image–text consistency exhibits distinct cross-dimensional patterns that cannot be captured by single-metric approaches. Full article