Search Results (88)

Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially pertinent to inquiries around the role of spirituality in mental health, with special attention to chronic pain conditions. The latter have become an open channel for novel avenues to explore the field of spirituality-based interventions within the arena of psychological inquiry. To address this, the authors developed and implemented the Church and Academia Model, a prototype for an innovative collaborative research project, with the aim of exploring the role of devotional practices, and their potential to be used as therapeutic co-adjuvants or tools to enhance the coping skills of patients with chronic pain. Keeping in mind that the church presents a rich landscape for clinical inquiry with broad relevance for clinicians and society at large, we created a unique hybrid research model. This is a new paradigm that focuses on distinct and well-defined studies where the funding, protocol writing, study design, and implementation are shared by experts from both the pastoral and clinical spaces. A team of theologians, researchers, and healthcare providers, including clinical pain psychologists, built a coalition leveraging their respective skill sets. Each expert is housed in their own environs, creating a functional network that has proven academically productive and pastorally effective. Key outputs include the creation and validation of a new psychometric measure, the Pain-related PRAYER Scale (PPRAYERS), an associated bedside prayer tool and a full-scale dissemination strategy through journal publications and specialty society conferences. This collaborative prototype is also an ideal fit for integrated knowledge translation platforms, and it is a promising paradigm for future collaborative projects focused on spirituality and mental health. Full article

The global rise in urban-related health issues poses significant challenges to public health, particularly in cities facing socio-economic crises. In Lebanon, 70% of the population is experiencing financial hardship, and healthcare costs have surged by 172%, exacerbating the strain on medical services. Given these conditions, improving the quality and accessibility of green spaces offers a promising avenue for alleviating mental health issues in urban areas. This study investigates the psychological impact of nine urban public spaces in Beirut through a comprehensive survey methodology, involving 297 participants (locals and tourists) who rated these spaces using Likert-scale measures. The findings reveal location-specific barriers, with Saanayeh Park rated highest in quality and Martyr’s Square rated lowest. The analysis identifies facility quality as the most significant factor influencing space quality, contributing 73.6% to the overall assessment, while activity factors have a lesser impact. The study further highlights a moderate positive association (Spearman’s rho = 0.30) between public space quality and mental well-being in Beirut. This study employs a hybrid methodology combining Research for Design (RfD) and Research Through Designing (RTD). Empirical data informed spatial strategies, while iterative design served as a tool for generating context-specific knowledge. Design enhancements—such as sensory plantings, shading systems, and social nodes—aim to improve well-being through better public space quality. The proposed interventions support mental health, life satisfaction, climate resilience, and urban inclusivity. The findings offer actionable insights for cities facing public health and spatial equity challenges in crisis contexts. Full article

Deep learning has advanced medical image segmentation, yet existing methods struggle with complex anatomical structures. Mainstream models, such as CNN, Transformer, and hybrid architectures, face challenges including insufficient information representation and redundant complexity, which limit their clinical deployment. Developing efficient and lightweight networks is crucial for accurate lesion localization and optimized clinical workflows. We propose the VML-UNet, a lightweight segmentation network with core innovations including the CPMamba module and the multi-scale local supervision module (MLSM). The CPMamba module integrates the visual state space (VSS) block and a channel prior attention mechanism to enable efficient modeling of spatial relationships with linear computational complexity through dynamic channel-space weight allocation, while preserving channel feature integrity. The MLSM enhances local feature perception and reduces the inference burden. Comparative experiments were conducted on three public datasets, including ISIC2017, ISIC2018, and PH2, with ablation experiments performed on ISIC2017. VML-UNet achieves 0.53 M parameters, 2.18 MB memory usage, and 1.24 GFLOPs time complexity, with its performance on the datasets outperforming comparative networks, validating its effectiveness. This study provides valuable references for developing lightweight, high-performance skin lesion segmentation networks, advancing the field of skin lesion segmentation. Full article

This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions. Full article

The rapid shift to online education during the COVID-19 pandemic exacerbated mental health risks for students, particularly those experiencing multidimensional poverty—a potential contributor to psychological distress in digital learning environments. This study examines how poverty-driven techno-distress (technology-related stress) impacts university students’ mental health, focusing on 202 Chilean learners engaged in remote classes. Using partial least squares structural equation modeling (PLS-SEM), we analyzed multidimensional poverty and its association with techno-distress, measured through validated scales. The results suggest that poverty conditions are associated with 32.5% of technostress variance (R² = 0.325), while techno-distress may indirectly relate to 18.7% of students’ dissatisfaction with academic life—a proxy for emerging mental health risks. Importance–performance map analysis (IPMA) identified housing habitability (e.g., overcrowding, inadequate study spaces) and healthcare access as priority intervention targets, surpassing purely digital factors. These findings indicate that techno-distress in online education may function as a systemic stressor, potentially amplifying pre-existing inequities linked to poverty. For educators and policymakers, this highlights the urgency of early interventions addressing students’ physical environments alongside pedagogical strategies. By framing techno-distress as a public health challenge rooted in socioeconomic disparities, this work advances preventive approaches to safeguard student well-being in increasingly hybrid educational landscapes. Full article

Composite protective coatings are critical for material durability but face challenges like fragmented knowledge and scalability issues. Existing research lacks the systematic integration of nanomaterial properties with macroscale performance and standardized evaluation protocols for hybrid systems. This study uses CiteSpace to analyze 18,363 publications (2015–2025) from Web of Science, visualizing collaborative networks, keyword clusters, and citation bursts. China leads global research output (8508 publications), with the USA and India following, while materials science, chemistry, and physics dominate disciplines. Key themes include nanocomposite coatings (e.g., graphene oxide, MXene), corrosion resistance mechanisms, and sustainable technologies, with citation bursts highlighting nanocomposites and surface functionalization. The study reveals interdisciplinary synergies in 2D nanomaterial-polymer systems, thereby improving barrier properties and enabling stimuli-responsive inhibitor release, yet it identifies gaps in lifecycle sustainability and industrial scalability. By constructing a holistic knowledge framework, this work bridges theory and application, quantifying interdisciplinary linkages and pinpointing frontiers like smart, multifunctional coatings. This study integrates data-driven insights to facilitate cross-sector collaboration. It delivers a strategic framework to tackle global challenges in material durability, sustainability, and practical application. Full article

The East–West Mountain Region (EWMR) of Wuhan is a vital natural and cultdural asset, characterized by its scenic nature landscapes and rich historical and cultural heritage. This study aims to address the problems of landscape character degradation and weakened public preferences caused by rapid urbanization and proposes a research framework integrating landscape character assessment and public preferences. Initially, we utilize K-means cluster analysis to identify landscape character types based on six landscape elements, resulting in a landscape character map with 20 types. Subsequently, we employ emotion analysis based on Natural Language Processing (NLP) techniques to analyze user-generated content (UGC) from Weibo check-in data to establish perception characteristic indicators reflecting public preferences. Finally, we quantitatively identify the environmental factors influencing public preferences through the SoIVES model and compare and integrate the landscape character map with the public emotion value map. The results show that (1) public preferences hotspots are concentrated in three types: (a) urban construction-driven types, including areas dominated by commercial service functions and those characterized by mixed-function residential areas; (b) natural terrain-dominated types with well-developed supporting facilities; and (c) hybrid transition types predominated by educational and scientific research land uses. These areas generally feature a high degree of functional diversity and good transportation accessibility. (2) Landscapes eliciting stronger emotional responses integrate moderate slopes, multifunctional spaces, and robust public services, whereas areas with weaker responses are characterized by single-function use or excessive urbanization. (3) The emotional variations within categories could be influenced by (a) functional hybridity through enhanced environmental exploration; (b) spatial usage frequency through place attachment formation; and (c) visual harmony through cognitive overload prevention. These findings provide critical insights for formulating zoning optimization plans aimed at the refined conservation and utilization of urban landscape resources, as well as offering guidance for improving landscape planning and management in the EWMR. Full article

Interdisciplinary research catalyzes innovation in mobile health (mHealth) by converging medical, technological, and social science expertise, driving critical advancements in this multifaceted field. Our longitudinal analysis evaluates how the NIH mHealth Training Institute (mHTI) program stimulates changes in research trajectories through a computational examination of 16,580 publications from 176 scholars (2015–2022 cohorts). We develop a hybrid analytical framework combining large language model (LLM) embeddings, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering to construct a semantic research landscape containing 329 micro-topics aggregated into 14 domains. GPT-4o-assisted labeling identified mHealth-related publications occupying central positions in the semantic space, functioning as conceptual bridges between disciplinary clusters such as clinical medicine, public health, and technological innovation. Kernel density estimation of research migration patterns revealed 63.8% of scholars visibly shifted their publication focus toward mHealth-dense regions within three years post-training. The reorientation demonstrates mHTI’s effectiveness in fostering interdisciplinary intellect with sustained engagement, evidenced by growth in mHealth-aligned publications from the mHTI scholars. Our methodology advances science of team science research by demonstrating how LLM-enhanced topic modeling coupled with spatial probability analysis can track knowledge evolution in interdisciplinary fields. The findings provide empirical validation for structured training programs’ capacity to stimulate convergent research, while offering a scalable framework for evaluating inter/transdisciplinary initiatives. The dual contribution bridges methodological innovation in natural language processing with practical insights for cultivating next-generation mHealth scholarship. Full article

This study proposes a hybrid blockchain system for secure and transparent data management in multinational space missions. By combining public and private blockchains, the model enables open access to non-sensitive data while protecting confidential mission records. Data integrity is ensured through cryptographic proofs without exposing the underlying content, and a cross-chain protocol enables real-time synchronization between chains without relying on centralized intermediaries. The system was implemented using Ethereum and Hyperledger Fabric and tested with real extravehicular activity data. Results show that it effectively detects data tampering, enforces access control, and synchronizes records with low latency. Compared to traditional centralized systems, this approach offers improved resilience, auditability, and trust across organizations. It provides a practical foundation for future space data infrastructures requiring both transparency and confidentiality. Full article

In aging residential neighborhoods, insufficient public spaces and a weakened sense of belonging have led to declining community vitality. Addressing the widespread idleness of boiler room facilities in cold-region contexts, this study integrates GPS tracking, Wi-Fi probe detection, questionnaire surveys, and field observations to develop a three-dimensional “space–time–behavior” evaluation model comprising five core indicators: activity type, spatial range, duration, frequency, and volatility. Unlike prior studies that rely on single data sources or unidimensional metrics, our multi-source approach enhances spatiotemporal resolution, improves the accuracy of subjective perceptions, and enables cross-validation between objective behavioral trajectories and residents’ self-reports, thereby significantly strengthening the comprehensiveness and reliability of community vitality measurement. The results show that the community service center conversion model maximizes spatial efficiency through functional integration, achieving a vitality score of 3.64—substantially higher than those for recreational renovations (3.16) and non-renovated sites (2.67). This model increases space utilization by 2.2-fold, sustains 12 h daily vitality, reduces residents’ activity radii by 38%, and boosts intergenerational interaction frequency by 43%, effectively bridging age group divides. We identify a “functional hybridization–spatial permeability–usage sustainability” mechanism underlying renovation efficacy and recommend the community service center paradigm as a priority strategy. The quantitative decision support framework established here offers empirical guidance for renewing existing spaces in severe climatic environments. Full article

Public buildings are crucial to creating healthy and sustainable cities. These buildings promote social cohesion and enrich urban life by transforming existing facilities into hybrid models that integrate medical content. Historical developments highlight shifts in residential, economic, and healthcare infrastructure. The healthcare system aims to enhance public health while ensuring financial equity. Reforms in healthcare privatization, governed by public health and insurance policies, involve liberalizing service provision and are supported by the Ministry of Health and Finance. This study examines how public buildings can adapt to enhance health and social sustainability. Through case studies, it assesses architectural adaptability in analyzing spatial, economic, and social impacts. Diagrams illustrate spatial dynamics, while surveys compare efficiency, sustainability, and user experience. Statistical analysis highlights the role of spatial adaptability in fostering sustainable urban environments. The results, which express significant differences between means for different locations and citizens’ satisfaction, suggest that the hypothesis offers substantial results in every area. Besides commercial programs in commercial buildings, healthcare also gives satisfactory results. This study advocates for adaptive architecture as a key strategy, aligning with evolving societal and health demands. Hybridizing healthcare facilities and commercial spaces transforms shopping centers into sustainable models, enhancing social cohesion and economic viability. Full article

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination of crop types. This systematic review examines the evolution of hyperspectral platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors to space-borne satellites (e.g., EnMAP, PRISMA), and explores recent scientific advances in AI methodologies for crop mapping. A review protocol was applied to identify 47 studies from databases of peer-reviewed scientific publications, focusing on hyperspectral sensors, input features, and classification architectures. The analysis highlights the significant contributions of Deep Learning (DL) models, particularly Vision Transformers (ViTs) and hybrid architectures, in improving classification accuracy. However, the review also identifies critical gaps, including the under-utilization of hyperspectral space-borne imaging, the limited integration of multi-sensor data, and the need for advanced modeling approaches such as Graph Neural Networks (GNNs)-based methods and geospatial foundation models (GFMs) for large-scale crop type mapping. Furthermore, the findings highlight the importance of developing scalable, interpretable, and transparent models to maximize the potential of hyperspectral imaging (HSI), particularly in underrepresented regions such as Africa, where research remains limited. This review provides valuable insights to guide future researchers in adopting HSI and advanced AI models for reliable large-scale crop mapping, contributing to sustainable agriculture and global food security. Full article

Refugee camps are typically designed as temporary sustainable settlements, prioritizing logistics over cultural considerations, which can lead to environments being misaligned with the lived experiences of displaced populations. This study addresses the challenge of traditional humanitarian camp designs that prioritize logistical efficiency over cultural and socio-cultural needs, leading to environments that do not align with the lived experiences of displaced populations. Focusing on the Zaatari Syrian Refugee Camp in Jordan, the research employs a structured questionnaire distributed among 102 households to investigate how refugees have reconfigured the camp’s original grid layout into more cohesive clustered patterns, informed by the principles of defensible space theory. Key findings reveal that refugees actively transform public courtyards into semi-private spaces, driven by cultural imperatives and safety needs. Statistical analyses confirm significant correlations between clustering behaviors and the attributes of defensible space, particularly the zones of influence and boundary demarcation, enhancing community resilience and accessibility. However, the study finds a limited predictive power overall, indicating that while these adaptations are significant, factors such as natural surveillance and territorial behavior do not exhibit strong influences on clustering dynamics. These findings have important implications for humanitarian planning and design. They highlight the necessity for more culturally sensitive and flexible approaches that prioritize refugee agencies and communal identity in camp layouts. This research advocates for a hybrid planning approach that integrates socio-cultural values, promoting resilience and quality of life among refugees. By aligning spatial designs with the social and cultural realities of refugee communities, humanitarian actors can enhance the effectiveness of their interventions, ultimately contributing to more sustainable and inclusive urban environments as part of broader goals related to urban planning and development. Future research is encouraged to explore these practices in diverse refugee contexts, providing further validation of these findings and enhancing the applicability of these design principles in global humanitarian efforts. Full article

The growing global demand for sustainable energy solutions has led to increased interest in kinetic energy harvesting as a viable alternative to traditional power sources. High-foot-traffic environments, such as public spaces and religious sites, generate significant mechanical energy that often remains untapped. This study explores energy-harvesting technologies applicable to public areas with heavy foot traffic, focusing on Al-Haram Mosque in Saudi Arabia—one of the most densely populated religious sites in the world. The research investigates the potential of piezoelectric, triboelectric, and hybrid systems to convert pedestrian foot traffic into electrical energy, addressing challenges such as efficiency, durability, scalability, and integration with existing infrastructure. Piezoelectric materials, including PVDF and BaTiO₃, effectively convert mechanical stress from footsteps into electricity, while triboelectric nanogenerators (TENGs) utilize contact electrification for lightweight, flexible energy capture. In addition, this study examines material innovations such as 3D-printed biomimetic structures, MXene-based composites (MXene is a two-dimensional material made from transition metal carbides, nitrides, and carbonitrides), and hybrid nanogenerators to improve the longevity and scalability of energy-harvesting systems in high-density footfall environments. Proposed applications for Al-Haram Mosque include energy-harvesting mats embedded with piezoelectric and triboelectric elements to power IoT devices, LED lighting, and environmental sensors. While challenges remain in material degradation, scalability, and cost, emerging hybrid systems and advanced composites present a promising pathway toward sustainable, self-powered infrastructure in large-scale, high-foot-traffic settings. These findings offer a transformative approach to energy sustainability, reducing reliance on traditional energy sources and contributing to Saudi Arabia’s Vision 2030 for renewable energy adoption. Full article

Accurate estimations of State-of-Charge (SOC) and State-of-Health (SOH) are crucial for ensuring the safe and efficient operation of lithium-ion batteries in Battery Management Systems (BMSs). This paper proposes a novel joint estimation method integrating an Autoregressive Equivalent Circuit Model (AR-ECM) with a data-driven model to address the strong coupling between SOC and SOH. First, a multi-strategy improved Ivy algorithm (MSIVY) is utilized to optimize the hyperparameters of a Hybrid Kernel Extreme Learning Machine (HKELM). Key voltage interval features, including split voltage, differential capacity, and current–voltage product, are extracted and filtered using a sliding window approach to enhance SOH prediction accuracy. The estimated SOH is subsequently incorporated into the AR-ECM state-space equations, where an enhanced particle swarm optimization algorithm optimizes the model parameters. Finally, the Extended Kalman Filter (EKF) is applied to achieve collaborative SOC–SOH estimation. Experimental results demonstrate that the proposed method achieves SOH errors below 1% and SOC errors under 2% on public datasets, showcasing its robust generalization capability and real-time performance. Full article