Search Results (671)

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an ϵ-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

Religion, culture, and ethnic heritage play a significant role in shaping migrant identities. This paper examines the interplay of these factors in the identity formation of African Christian migrants in Europe, with a particular focus on second-generation (2G) migrants. It analyzes how 2G individuals negotiate Western secular values alongside Pentecostal orientations in ways that facilitate upward social mobility. The study is based on a critical review of the existing literature, compared with lived realities of migrants in the Netherlands. Drawing on empirical research from various European contexts, the paper aims to provide a rigorous and multidimensional account of intergenerational identity reconstruction among 2G African Christians. By centring the Pentecostal family as a primary site of socialization, the paper explores how 2G African Christians simultaneously distance themselves from, and selectively adapt, elements of indigenous African spirit cosmologies in pursuit of secular, achievement-oriented goals. This dialectical engagement reflects a broader generational shift: while first-generation migrants tend to rely heavily on religion and religious institutions as mechanisms of integration, 2G migrants increasingly prioritize secular aspirations while navigating socioeconomic structures, negotiating belonging, and constructing hybrid forms of transnational identity. In doing so, the paper contributes to scholarship on how 2G African migrants in Europe mobilize Pentecostal spirituality as a resource for achieving secular objectives. Full article

Understanding forest dynamics requires reliable inventories that assess tree- and stand-level characteristics. Traditionally, this has relied on field measurements such as diameter at breast height (DBH), height, and crown attributes, but these methods are labor-intensive and spatially limited. Remote sensing, particularly Light Detection and Ranging (LiDAR), has expanded forest inventory capacity by generating three-dimensional structural information. Mobile laser scanning (MLS), a recent adaptation, offers flexible, high-resolution data collection, though its performance across complex forests is still being evaluated. This study assessed the effectiveness of MLS in detecting individual trees and estimating DBH in mixed-species forests of the Northeastern United States. We also evaluated the influence of tree- and plot-level characteristics on detection accuracy and DBH estimation. Results showed an 85.2% tree detection rate, a 23.5% commission rate, and a DBH root mean square error (RMSE) of 1.98 cm (9.65%). Among the variables tested, tree DBH was the only significant predictor of detection probability; tree density and relative density had minimal effect. These findings demonstrate that MLS can achieve precise DBH estimation when trees are correctly identified, but false detections remain a limitation. Further methodological improvements are needed to enhance accuracy in structurally complex forests and advance MLS for operational forest monitoring. Full article

In recent years, the generation and animation of avatars in virtual reality (VR) have undergone a definitive paradigm shift, transitioning from pre-rendered, manually rigged meshes to autonomous, AI-driven digital entities. While individual algorithms have been extensively studied, there is a critical lack of comprehensive synthesis regarding how these generative models impact the broader sociotechnical ecosystem of Spatial Computing. To address this gap, this systematic literature review, conducted in accordance with PRISMA guidelines, analyzed 48 primary studies to evaluate the intersection of Generative AI, hardware architecture, human psychology, and digital ethics. The synthesis reveals a deeply interdependent ecosystem. While advanced neural rendering and diffusion models (RQ1) successfully bypass traditional 3D authoring bottlenecks, their pursuit of absolute visual fidelity severely antagonizes the thermal and latency constraints of standalone mobile hardware (RQ2). The literature demonstrates that failing to mitigate these bottlenecks through hardware–software co-design (e.g., specialized ASICs, gaze-contingent foveation) inevitably shatters the user’s sensorimotor loop, collapsing the sense of agency and triggering the Kinematic Uncanny Valley (RQ3). Furthermore, as these hyper-realistic avatars achieve kinematic autonomy, they introduce unprecedented sociotechnical vulnerabilities regarding spatial privacy, dataset bias, and post-mortem digital identity (RQ4). Ultimately, this review concludes that realizing a compelling and inclusive AI-driven Metaverse is no longer an isolated computer graphics challenge; it demands a rigorous, interdisciplinary paradigm shift where algorithms, silicon architectures, and cognitive psychology are inextricably co-designed under a foundational framework of digital ethics. Full article

Background: Body mass index (BMI) is widely used as a proxy of nutritional status and related lifestyle risk patterns in public health, yet it does not capture body composition–related heterogeneity in cardiometabolic risk. Evidence on whether a more detailed body composition assessment improves population-level screening efficiency remains inconsistent, particularly in Central European populations. Methods: We conducted a cross-sectional analysis of 868 Hungarian adults participating in a nationwide mobile screening program. Locally weighted regression identified sex-specific BMI inflection points for cardiometabolic risk. Stratified receiver operating characteristic (ROC) analyses compared BMI with bioelectrical impedance-derived parameters across five outcomes. Cost- and time-effectiveness of scalable screening strategies were modeled at the population level. Results: Cardiometabolic risk increased at BMI levels below current WHO thresholds (females: 21.8–22.3 kg/m²; males: 23.8–24.3 kg/m²). Overall, body composition parameters did not outperform BMI in the full population. Subgroup-specific differences were observed, particularly among men with BMI 24–36 kg/m² for atherosclerosis risk, suggesting limited and outcome-specific added value rather than broad superiority over BMI. Together, non-linear risk patterns, stratified performance, and population-level modeling converged on mid-range BMI intervals (females: 22–30 kg/m²; males: 24–30 kg/m²) as likely screening windows of phenotypic heterogeneity. Within these ranges, targeted InBody assessment may help refine risk assessment for selected individuals. A mixed screening strategy covering 52% of the population would cost 178.4% of BMI-only screening, while reducing throughput by 24.3%. Conclusions: Population-specific BMI thresholds may more accurately reflect early deviations in nutritional and cardiometabolic risk than current universal cutoffs. BMI remains a useful first-line marker, and body composition assessment may add complementary information in selected BMI ranges. Overall, these findings support a potentially useful, subgroup-specific screening approach, but the modeled cost and time trade-offs should be considered hypothesis-generating and require further validation. Full article

Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

In Ghana’s political landscape, actors from both ruling and opposition parties deploy a range of linguistic and rhetorical strategies in their pursuit of political power. Prominent among these is political labeling, a discursive practice used to construct favorable self-images while delegitimizing opponents through derogatory and face-threatening expressions. This study examines how political labeling functions as a strategic tool for identity construction and power negotiation in Ghana’s electoral landscape. Situated within the fields of political discourse and communication studies, the study demonstrates how labeling operates simultaneously as a rhetorical and framing device that reflects and reinforces underlying sociopolitical power dynamics. Drawing on empirical data from major Ghanaian news portals, the study adopts an integrated analytical framework combining Framing Theory and the Theory of Impoliteness. It analyzes public labeling directed at three prominent political figures across three election cycles (2016, 2020, and 2024). The findings show that politicians, activists, and their supporters strategically deploy labels to reconstruct rivals’ identities, inflict reputational damage, and provoke ridicule, thereby undermining their perceived competence and public credibility. Focusing on derogatory labels, we argue that political labeling serves primarily to generate emotional responses, shape public perception, and mobilize collective action, ultimately influencing the trajectory of national political discourse. By examining the interplay between language, identity construction, and power, this research offers a nuanced account of how political labeling shapes individual attitudes, group dynamics, and the broader political culture in Ghana. Full article

Background/Objectives: Blood donation is a critical component of healthcare systems worldwide, yet donor recruitment remains challenging. This study evaluates the knowledge, attitudes, motivations, and practices (KAP) of blood donation among the general population in Saudi Arabia to identify key barriers and propose targeted interventions. Methods: A cross-sectional study was conducted using a structured, validated questionnaire distributed over five months (December 2022 to April 2023) via social media and in-person recruitment at the Central Blood Bank in Riyadh. A total of 1150 participants aged 18–60 years residing in Saudi Arabia were included in the final analysis. Statistical analysis was performed using SPSS version 22, with p < 0.05 considered significant. Results: Participants demonstrated moderate knowledge (mean score 5.43 ± 1.81 out of 9), with significantly higher scores among males, individuals aged 21–30 years, and those holding a bachelor’s degree. Attitudes toward donation were highly positive (mean score 15.46 ± 2.74 out of 20) and correlated with age, gender, marital status, and occupation. Despite this positive outlook, only 34.96% of participants had donated blood previously, although 95.25% expressed willingness to do so. Primary motivators included mobile donation units (89.22%) and paid leave (89.22%), whereas 51.22% of respondents considered current media campaigns ineffective. Common barriers to donation included health concerns (25.30%), time constraints (12.87%), and fear of needles (7.74%). Conclusions: This study reveals a critical disparity between positive public attitudes and actual donation practices in Saudi Arabia. To enhance donor participation, we recommend implementing convenient donation strategies such as mobile blood drives, workplace incentives, and more effective, culturally tailored educational campaigns. Addressing these factors could help Saudi Arabia improve its voluntary donation rates and ensure a sustainable, safe blood supply. Full article

Pressure injuries remain highly prevalent in hospital and home settings, particularly among elderly patients, intensive care unit patients, and individuals with impaired mobility, generating significant healthcare and economic impacts. Although low-level laser therapy has been explored as an adjunctive therapy to accelerate healing, few validated protocols exist to guide its systematic application in clinical nursing practice. This methodological study aimed to develop and validate a nursing care protocol for low-level laser therapy in stage 1 pressure injuries, conducted in three stages: integrative literature review, protocol development, and content validation using the Delphi technique with specialist nurses selected via the Lattes Platform. Judges evaluated the protocol using a five-point Likert scale, and validity was assessed by the Content Validity Index (CVI) and Cronbach’s alpha, both with minimum acceptable values of 0.80. The integrative review identified four studies supporting low-level laser therapy efficacy, informing the protocol’s technical parameters. Thirty-one specialists participated in the first Delphi round and 25 in the second, achieving a Global CVI of 0.915 and Cronbach’s alpha of 0.91, with all items reaching consensus. The validated protocol demonstrated satisfactory content validity and internal consistency, supporting its clinical applicability and potential to standardize nursing practice and reinforce patient safety. Although the protocol demonstrated satisfactory methodological validity, further clinical studies are needed to assess feasibility, implementation, and effectiveness in routine nursing care. Full article

Drawing on 964 field observations from typical community parks in Changsha, this study utilizes the System for Observing Play and Recreation in Communities (SOPARC) alongside Kernel Density Estimation (KDE) to quantitatively dissect the intrinsic associations between older adults’ outdoor activity patterns and micro-spatial characteristics. The findings reveal a pronounced demographic differentiation in spatial utilization. Specifically, female-dominated collective activities, such as square dancing, exhibit a profound reliance on central plazas with unobstructed sightlines to cultivate a sense of social security. Conversely, male users demonstrate a distinct preference for static social interactions, including playing chess or cards, within semi-enclosed and shaded spaces beneath a tree canopy. Beyond gender distinctions, age-related spatial anchoring is also evident. Intergenerational caregiving activities among the younger old cohort (aged 55 to 74) are exclusively concentrated around children’s playgrounds, whereas older old individuals and those with impaired mobility rely heavily on densely distributed micro-scale resting amenities. Driven by these empirical insights, this research contends that age-friendly park design must transcend generic accessibility standards, advocating instead for a “categorized spatial provision” strategy. By meticulously balancing open exhibition plazas, semi-private shaded units, and composite caregiving zones within a single park ecosystem, this approach can effectively accommodate the multifaceted health and social requirements of various older adult subgroups. Full article

This paper addresses the critical communication barrier experienced by deaf and hearing-impaired individuals in the Arab world through the development of an affordable, video-based Arabic Sign Language (ArSL) recognition system. Designed for broad accessibility, the system eliminates specialized hardware by leveraging standard mobile or laptop cameras. Our methodology employs Mediapipe for real-time extraction of hand, face, and pose landmarks from video streams. These anatomical features are then processed by a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN component captures spatial features, such as intricate hand shapes and body movements, within individual frames. Concurrently, BiLSTMs model long-term temporal dependencies and motion trajectories across consecutive frames. This integrated CNN-BiLSTM architecture is critical for generating a comprehensive spatiotemporal representation, enabling accurate differentiation of complex signs where meaning relies on both static gestures and dynamic transitions, thus preventing misclassification that CNN-only or RNN-only models would incur. Rigorously evaluated on the author-created JUST-SL dataset and the publicly available KArSL dataset, the system achieved 96% overall accuracy for JUST-SL and an impressive 99% for KArSL. These results demonstrate the system’s superior accuracy compared to previous research, particularly for recognizing full Arabic words, thereby significantly enhancing communication accessibility for the deaf and hearing-impaired community. Full article

Background: Unhoused individuals face disproportionately high rates of preventable chronic disease due to fragmented access to care and prolonged exposure to environmental stressors. Street medicine programs offer a mobile, low-barrier model to assess and address these unmet needs. Despite well-documented disparities, no publications in the current literature provide numerically specific screening recommendation guidelines tailored to unhoused populations. This study fills that gap using clinical data from Street Medicine Phoenix (SMP), a mobile healthcare initiative serving urban Arizona. Methods: We retrospectively reviewed 1322 clinical encounters recorded by SMP between August 2023 and October 2024. Diagnoses and treatments were manually categorized. Blood pressure (BP) and glucose values were analyzed using descriptive statistics and compared against national norms (CDC 50th percentile and ADA guidelines). Kruskal–Wallis and Dunn’s tests assessed age-based differences, while chi-square and Mann–Whitney U tests examined glucose patterns. Results: The mean patient age was 51.4 years; 34.5% identified as female. Cardiovascular issues (39.4%) and routine screenings (39.6%) were most frequently documented. Systolic and diastolic BP values were significantly elevated across all age groups except those 60+, with even the 18–39 group showing median systolic BP above CDC norms (124.0 mmHg). Among 60 patients with fasting glucose data, 41.4% met ADA criteria for diabetes, and 10.7% of those without a known diagnosis had diabetic-range values. Conclusions: Our findings suggest that cardiometabolic disease may emerge earlier and more aggressively among unhoused individuals than in the general U.S. population, reflecting patterns of accelerated biological aging. The elevation of cohort-based BP percentiles suggests that current national benchmarks may underrepresent clinical risk in this group. We propose initiating blood pressure screening at age 18 and fasting glucose screening by age 35 in unhoused individuals—adaptations of existing USPSTF recommendations based on cohort-specific trends. These screening thresholds can be feasibly implemented in street medicine settings to promote earlier detection and improve long-term health outcomes. Full article

Food waste in households means that there is a need to recognize the possibilities of balancing activities in the field of obtaining and managing food products. Activities in this area may concern giving away surplus food to others, purchasing local and organic products, limiting shopping activity. Generation Z, which was included in this research, uses new media, including mobile applications, to a greater extent than other generations. The main objective of the research is to recognize and present the food-sustainable behaviors and attitudes of Generation Z consumers. The study used the analysis of source data, which was the basis for formulating four hypotheses. They were verified in empirical studies conducted using the CAWI method. The collected material was analyzed using, among others, the proprietary index of environmentally and socially sustainable behaviors (ESRBI), the Mann-Whitney test. The studies showed that respondents assessed their food behaviors as irresponsible, with women’s assessment being higher than men’s. A positive correlation was demonstrated between the use of food saving applications and the value of the ESRBI index and individual sustainable behaviors. Respondents positively assessed the initiatives of local authorities and housing cooperatives in the area of creating places for sharing food and organizing community gardens. Full article

Background: Women with endometriosis frequently experience mobility limitations that affect daily functioning. A specific tool to assess these restrictions would help clinicians to better understand patients’ functional challenges, facilitating more effective communication and shared decision making. Addressing this gap is essential for strengthening patient–professional dialogue and improving individualized care. Objective: To develop the new instrument MobEndo and to perform initial psychometric testing of the tool. Methods: The initial domains and items were generated through semi-structured interviews with patients and based on experts’ advice. Guided by the International Classification of Functioning, Disability, and Health (ICF) framework, exploratory factor analysis was conducted on data from patients diagnosed with endometriosis. Internal consistency was assessed using Cronbach’s alpha, considering values ≥ 0.70 as acceptable. Test–retest reliability was examined using intraclass correlation coefficients (ICCs), and ICC values were judged as excellent if >0.75. Construct validity was evaluated through concurrent, discriminant, and known-groups validity. For the known-groups validity hypothesis, participants were categorized by baseline pain levels. Results: The final questionnaire included 18 items, developed from responses from 301 women (mean age 38.96 ± 6.85). Factor analysis revealed two components—transitioning between body positions and performing movements requiring stabilization and executing load-bearing tasks involving the upper limbs—with the model explaining 71.78% of the total variance. Reliability was excellent, with a Cronbach’s alpha of 0.977. The ICC for the total score was 0.976 (95% CI 0.949–0.988), with similarly high values for each component. Concurrent validity correlations were significant, while discriminant validity showed no relevant associations. Known-groups analyses showed clear differences across pain-level groups. Conclusions: The questionnaire is a valid and reliable tool for capturing women’s perceived mobility limitations in endometriosis. Full article