Search Results (3,437)

Ageing is a dynamic biological process involving interconnected physiological, psychological, and social changes, making the promotion of healthy ageing a global public health priority. The World Health Organization (WHO) defines healthy ageing as the process of developing and maintaining functional ability that enables well-being in older age. The WHO’s Decade of Healthy Aging (2021–2030) outlines four key action areas: changing attitudes toward ageing, creating age-friendly environments, delivering integrated and person-centred care, and ensuring access to long-term care. This Perspective examines yoga, a holistic mind–body practice integrating physical postures, breath regulation, and mindfulness, as a potentially safe, adaptable, and scalable intervention for older adults. Evidence suggests that yoga may improve flexibility, balance, mobility, and cardiovascular function, reduce pain, and support the management of chronic conditions commonly associated with ageing. Psychological and cognitive research further indicates reductions in stress, anxiety, and depressive symptoms, alongside potential benefits for attention, memory, and executive function. Improvements in health-related quality of life (HRQoL) have been reported across physical, psychological, and social domains, with benefits sustained through regular practice. Adaptations such as chair-based practices, restorative postures, and the use of props enhance accessibility and safety, allowing participation across diverse functional levels. Mindfulness and breath-focused components of yoga may additionally support emotional regulation, resilience, and psychological well-being, particularly among older adults experiencing stress or limited mobility. Yoga interventions are generally well tolerated, demonstrate high adherence, and can be delivered through in-person and digital formats, addressing common access barriers. Despite this growing evidence base, yoga remains underintegrated within health policy and care systems in the US, UK, and India. Strengthening its role may require coordinated efforts across research, policy, and implementation to support healthy ageing outcomes. Full article

Electric mobility is increasingly recognised as a sustainable transportation solution worldwide. While the economic and environmental aspects of e-mobility have been explored extensively, social dimensions such as equity, accessibility, and inclusiveness remain underexplored. Existing literature on these social aspects is fragmented across disciplines, shaped by varying regional contexts, which complicates efforts to form a coherent understanding of the field. To address this gap, a bibliometric analysis was conducted using the R-studio software via the Biblioshiny app. Version 4.3.0. This analysis systematically maps the intellectual landscape, identifies dominant themes, and highlights critical research gaps at the intersection of e-mobility and social sustainability. A total of 490 publications were extracted from the Scopus database as of 23 March 2025. The findings reveal a sharp increase in scholarly attention since 2018, peaking at 110 publications in 2024. The top-ranked country is China, which has 130 publications. In addition, the research has clustered around four thematic areas: energy and charging infrastructure, social and economic impacts, public policy and regulations, and technological innovations. Despite this growth, persistent gaps remain, particularly concerning social equity, inclusive policy design, socio-economic disparities, and the real-world effects of emerging technologies on vulnerable populations. Future research should specifically explore how e-mobility initiatives can reduce regional access inequalities, generate quality green employment, and ensure that technologies such as vehicle-to-grid systems are equitably deployed to benefit low-income and marginalised populations. Full article

Renewable hydrogen has become a versatile technology that can play a key role in the deployment of energy communities, although technological, economic, environmental, legal, and social challenges remain to be addressed. This study conducts a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology that analyzes the current state of technologies, the different applications, challenges and limitations, and future lines of research related to the enabling role of hydrogen in energy communities. Results from the bibliometric analysis show sustained growth in the number of publications over the last five years (2020–2025), with a predominance of applications in which hydrogen is combined with other energy carriers (58%). The versatility of hydrogen has prompted the evaluation of different applications, with particular emphasis on energy storage to capitalize on energy surpluses (51%), mobility (19%), and heating (20%). The main existing barriers come from the absence of stable long-term regulation, interoperability between components and technologies, and a lack of real data. Overcoming these challenges should be based on new technologies such as artificial intelligence and the construction and operation of pilot projects. In addition, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis has been conducted building upon the SHARED-H2 SUDOE project, yielding particularly insightful results through the active involvement of stakeholders in the preparatory process. Based on all the points given above, the research concludes that it is necessary to improve long-term policies and increase training at all levels aimed at active end-user participation and a profound restructuring of the energy system. Full article

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

Walkability for non-motorized users is crucial for fostering inclusive, healthy, and sustainable communities. By prioritizing modern human-centered design principles, social equality is promoted for all categories of users, regardless of physical abilities or socio-economic status. Despite the importance of this indicator, a series of inconsistencies that produce inadequate and inaccessible urban space can still be observed in cities. The aim of this paper is to present the methodology for the calculation of the walkability index at the local level. This new methodological procedure considers walkability for pedestrians, with a special focus on people with reduced mobility. Based on specifically defined criteria, initial calculations were performed and integrated into the dynamic walkability index (DWI). One of the main advantages of this index is that it includes the dynamic component of the share of different categories of users in the total sample, which enables simple time modification without repeating the entire procedure. The developed methodology can be used as a tool for ranking existing street segments according to the urgency of reconstruction, while on the other hand promoting equality and inclusion of all categories of users in decision-making processes, thus creating more comfortable and safer environments. Full article

This study investigates cross-cultural differences in public perception of mobility electrification by applying natural language processing (NLP) techniques to social media discourse in Germany and China. Using a large language model (LLM), this study conducted sentiment analysis and zero-shot text classification on over 10,000 posts to explore how citizens in each country engage with the topic of electric mobility. Results reveal that while infrastructure readiness is a dominant concern in both contexts, German discourse places greater emphasis on environmental impact, often reflecting skepticism toward sustainability claims. On the other hand, Chinese discussions highlight technological advancement and infrastructure expansion, with comparatively limited focus on environmental concerns. These findings show the importance of culturally tailored policy and communication strategies in supporting the public acceptance of electric mobility. By demonstrating how artificial intelligence-driven large-scale social media data analysis can be used to analyze public sentiment across linguistic and cultural contexts, this study contributes methodologically to the emerging field of computational social science and offers practical insights for mobility policy in diverse national settings. Full article

Rural transport remains a critical factor of social inclusion in South Africa, particularly for people with disabilities who rely on public transport. This study explores the experiences of minibus taxi drivers in transporting passengers with disabilities in Mt Elias, a rural community in the KwaZulu-Natal province. A qualitative research design was adopted, involving semi-structured interviews with 15 drivers operating between Dalton and Mt Elias route. Thematic analysis was conducted using ATLAS.ti to identify key patterns and relationships across the dataset. The four key themes that emerged from the dataset are: infrastructure and environmental challenges, accessibility and support for passengers, operational and economic constraints, and human interactions and attitudes. Findings reveal that drivers face multiple barriers, including poor road conditions, limited vehicle space, and a lack of formal training, yet many demonstrate empathy and commitment to assisting passengers with disabilities. The study highlights the need for targeted policy interventions to improve road infrastructure, provide disability awareness training for drivers, and redesign vehicles for accessibility. Promoting inclusive rural transport requires coordinated action among government spheres, taxi associations, and disability advocacy groups. This research contributes new insights into the lived realities of rural drivers and promotes the importance of inclusive mobility as a component of social justice. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

Background/Objectives: To analyze the parent-perceived quality of life (QoL) in children with cerebral palsy (CP) and to study the relationship between sociodemographic and clinical factors and this perception, under the perspective of the International Classification of Functioning, Disability and Health (ICF). Methods: A cross-sectional study was conducted with 95 participants (ages 5–19 years) with CP. Participants’ parents were asked about sociodemographic and clinical characteristics and compiled Cerebral Palsy Quality of Life (CP-QoL) and Pediatric Disability Inventory-Computer Adaptive Test (PEDI-CAT). Participants were assessed and classified into the following functional domains: gross motor function (GMFM-88, GMFCS), manual ability (MACS), eating and drinking abilities (EDACS), and communication function (CFCS). Correlations between CP-QoL domains and variables were investigated using Spearman’s correlation coefficient and multivariate predictive models were used to investigate the variables predicting CP-QoL scores for each domain. Results: A total of 95 children with a mean age of 12.4 ± 3.5 years (range 5–19 years) were included. Participants demonstrated moderate-high GMFM-88 level (228.8 ± 44.7) and high functional performance across PEDI-CAT domains: Activity (57.2 ± 5.1), Mobility (63.1 ± 5.6), and Social/Cognitive (70.2 ± 4.3). Parent-perceived QoL was significantly higher when children did not require AFOs, botulinum toxin, or recent hospitalizations, and lower among children who attended physical therapy >2 h/week. Moderate correlations were consistently found between the ‘Feelings about Functioning’ domain and functional variables, being positive for GMFM-88 and all PEDI-CAT domains, and negative for GMFCS, MACS, EDACS and CFCS. That domain of CP-QoL was best explained by the regression model (R² = 0.619, p < 0.001), with the combination of three variables: GMFM-88, PEDI-CAT Activity and PEDI-CAT Social/Cognitive. Among them, PEDI-CAT Activity was the strongest predictor (β = 0.1436). Conclusions: In children with CP, to enhance family well-being, interventions should prioritize social participation and carefully balance the intensity and frequency of therapy against family burden and daily life demands, as QoL is primarily driven by manual ability and functional performance. Full article

Excess commuting reflects the inefficiency of urban land resource allocation, generating additional greenhouse gas emissions and social costs, and has therefore become a central concern in the pursuit of sustainable cities. While exogenous shocks inevitably alter the efficiency of land resource allocation, it remains unclear how such shocks affect overall urban efficiency. To address this gap, this paper proposes a generalized framework for measuring excess commuting that accounts for imbalances between the numbers of jobs and residences. Drawing on mobile signaling big data, we trace the daily commuting patterns of more than 900,000 residents in Beijing, comparing the pre-pandemic period (March–October 2019) with the pandemic period (March–October 2020). The results show that: (1) Excess commuting increased significantly after the outbreak of COVID-19, with the observed average commuting distance (Tact) of the full sample rising from 6267 m to 10,058 m (an increase of 59%), indicating a decline in urban land resource allocation efficiency; (2) A more pronounced center-periphery pattern emerged at the metropolitan scale: the average Jobs–Housing Ratio (JHR) increased from 1.08 to 1.11, and its standard deviation rose from 0.54 to 0.70, with the JHR of central urban areas decreasing by 3% and that of suburban areas increasing by 20%—suggesting a marked increase in commuting distances; (3) Heterogeneous impacts were observed across age groups: the Difference-in-Differences (DID) regression confirmed a significant negative interaction term (Group × COVID-19 = −0.2991 **, p < 0.05), indicating that older adults experienced a greater increase in commuting inefficiency than younger adults. These findings reveal the dynamic mechanisms linking exogenous shocks, jobs–housing mismatch, and urban land resource allocation efficiency and provide policy implications for improving spatial resource allocation in the post-pandemic era. Full article

Informal learning in institutional settings plays a vital role in lifelong education by fostering self-directed knowledge acquisition. With the increasing integration of digital media into these environments, augmented reality (AR) has emerged as a particularly promising technology due to its ability to overlay virtual content in real-time and across multiple sensory modalities. This systematic literature review investigates the use of AR in institutional informal learning places (IILPs) from 2018 to 2025, aiming to synthesize findings across the following overall research questions: (1) In which IILP contexts has AR been implemented, and what are the characteristics of the technology? (2) What learning-relevant functions and (3) outcomes are associated with AR in these settings? (4) Which learning theories underpin the design of AR interventions? Following the PRISMA guidelines, empirical studies were identified through comprehensive database searches (Scopus, Web of Science, IEEE Xplore, FIS Bildung) and cross-referencing. Forty-four studies were analyzed via qualitative content analysis. The goal is to provide a descriptive overview of findings, patterns, and relationships. Findings indicate that AR is widely adopted across diverse domains and institutional contexts, primarily through mobile-based AR applications for K–12 learning. Native app development signals growing technological maturity. AR enhances both cognitive and emotional-motivational outcomes, though its potential to support social interaction remains insufficiently investigated. The predominant function of AR is the provision of information. Most of the examined studies are grounded in constructivist or cognitivist learning theories, particularly the Cognitive Theory of Multimedia Learning. Only limited references to emotional-motivational frameworks and minimal references to behaviorist frameworks were found. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Rural areas are currently facing a deepening “social-ecological divide,” where the fragmentation of natural, economic, and cultural data—often trapped in “data silos”—hinders effective systemic governance. To bridge this gap, in this study, the Rural Landscape Information Model (RLIM), an integrative framework designed to reconfigure rural connections through data fusion, process coordination, and performance feedback, is proposed. We validate the framework’s effectiveness through a comparative analysis of two distinct rural archetypes in China: the innovation-driven Yu Village and the heritage-conservation-oriented Hani Terraces. Our results reveal that digital technologies drive distinct empowerment pathways moderated by regional contexts: (1) In the data domain, heterogeneous resources were successfully integrated into the framework in both cases (achieving a Monitoring Coverage > 80%), yet served divergent strategic ends—comprehensive territorial management in Yu Village versus precision heritage monitoring in the Hani Terraces. (2) In the process domain, digital platforms restructured social interactions differently. Yu Village achieved high individual participation (Participation Rate ≈ 0.85) via mobile governance apps, whereas the Hani Terraces relied on cooperative-mediated engagement to bridge the digital divide for elderly farmers. (3) In the performance domain, the interventions yielded contrasting but positive economic-ecological outcomes. Yu Village realized a 25% growth in tourism revenue through “industrial transformation” (Ecology+), while the Hani Terraces achieved a 12% value enhancement by stabilizing traditional agricultural ecosystems (Culture+). This study contributes a verifiable theoretical model and a set of operational tools, demonstrating that digital technologies are not merely instrumental add-ons but catalysts for fostering resilient, collaborative, and context-specific rural socio-ecological systems, ultimately offering scalable governance strategies for sustainable rural revitalization in the digital era. Full article