Search Results (1,259)

Aggregated epidemics, characterized by rapid transmission over short periods, pose severe threats to public health security, necessitating the development of precise source tracing and simulation methods to support efficient prevention and control. However, existing studies suffer from three major gaps: (1) predominant focus on national/regional scales with limited urban-scale analysis; (2) reliance on proprietary mobile data that are often inaccessible; and (3) NP-hard computational complexity in traditional source tracing methods. To bridge these gaps, this paper proposes an integrated spatiotemporal diffusion model comprising two core components: outbreak point estimation and spatial diffusion simulation. The model first uses the SEAIR infectious disease dynamics model to predict trends, combines the 3-Sigma criterion and viral incubation period to screen early epidemiological survey data, employs a road network-constrained DBSCAN algorithm for spatial clustering, and locates the outbreak point via an improved inverse distance weighting method incorporating time and POI density weights. Subsequently, using the estimated outbreak point as the initial transmission center, and based on the “cell-type” living structure hypothesis of populations, it fuses multi-source geographic data to quantify regional attractiveness and simulate viral diffusion in grid space. The model is validated using COVID-19 epidemic data from Xi’an, Shanghai, and the Hong Kong Special Administrative Region of China. Results show that the proposed outbreak point estimation method effectively estimates the initial transmission center, with distances between estimated points and officially announced points of 0.786 km, 1.676 km, and 5.441 km, respectively—shortened by 179 m, 1091 m, and 711 m compared to related studies. The spatiotemporal diffusion model effectively simulates daily incidence patterns, achieving average coverage rates of 67.32% and 72.9% and average precision rates of 60.67% and 78.84% in Shanghai and Hong Kong, respectively, with significantly better simulation accuracy than traditional methods in the early and middle stages of the epidemic. This study provides a scientifically robust, data-parsimonious framework for precise source tracing, early warning, and resource allocation in urban epidemics, with strong generalizability to other resource-limited settings. Full article

Background: Asthma during pregnancy is associated with adverse maternal and neonatal outcomes. Nursing education, self-management support, and mobile health (mHealth) technologies may support asthma management during pregnancy. Aim: This study evaluated the effect of a structured nursing intervention supported by an mHealth application on asthma control, treatment adherence, and self-monitoring, and maternal and neonatal outcomes among pregnant women with asthma. Design and Setting: A quasi-experimental pretest–posttest design with a control group was conducted at two Maternal and Child Health Centers in Shebin El-Kom, Menoufia Governorate, Egypt. Participants: A convenience sample of 140 pregnant women with physician-diagnosed asthma was recruited and allocated non-randomly according to the center attended to a study group (n = 70) and a control group (n = 70). The sample size was calculated a priori based on a previous study using a 5% significance level and 80% statistical power, with a 1:1 allocation ratio, resulting in a required sample of 70 participants per group. All recruited participants completed the study, with no withdrawals or loss to follow-up. Intervention: The study group received routine antenatal care plus an eight-week structured nursing intervention consisting of four individualized weekly educational sessions (60–90 min each) addressing asthma management, medication adherence, self-monitoring, trigger avoidance, warning signs, inhaler technique, lifestyle modification, and rhythmic breathing exercises. Participants were trained to use the Airlyn mHealth application for guided breathing exercises, practiced 2–3 times daily, and received telephone/WhatsApp follow-up during weeks 6–8. The control group received routine antenatal care according to usual MCH center procedures. Measures: Asthma control was assessed using the Asthma Control Test (ACT). Treatment adherence and self-monitoring were assessed using the Asthma Adherence and Self-Monitoring Questionnaire. Maternal outcomes included hypertensive disorders, gestational diabetes, infections, preterm labor/PPROM, hemorrhage, and mode of delivery. Neonatal outcomes included birth weight, Apgar scores, respiratory distress and respiratory support, NICU admission and length of stay, and condition at discharge. The researcher-developed instruments underwent expert assessment of content validity and assessment of internal consistency before use. Results: Baseline ACT scores were comparable between the study and control groups (14.5 ± 4.2 vs. 14.1 ± 4.3; mean difference = 0.4; p = 0.580). Following the intervention, ACT scores were higher in the study group than in the control group (19.7 ± 3.6 vs. 15.2 ± 4.2; mean difference = 4.5; 95% CI: 3.189–5.811; p < 0.001; Cohen’s d = 1.147). Well-controlled asthma was observed in 75.7% of the study group compared with 17.1% of the control group (p < 0.001). Treatment adherence and self-monitoring scores were also better in the study group (5.6 ± 2.7 vs. 9.1 ± 3.1; mean difference = −3.5; 95% CI: −4.476 to −2.524; p < 0.001; Cohen’s d = 1.199). The overall maternal outcome score was more favorable in the study group (3.5 ± 1.7 vs. 6.1 ± 2.6; mean difference = −2.6; 95% CI: −3.340 to −1.860; p < 0.001; Cohen’s d = 1.174), as was the overall neonatal outcome score (7.7 ± 2.2 vs. 5.5 ± 2.1; mean difference = 2.2; 95% CI: 1.480–2.920; p < 0.001; Cohen’s d = 1.022). Significant between-group differences were also observed in birth weight, Apgar scores, respiratory distress, and NICU admission. Conclusions: Participants who received the structured nursing intervention supported by the Airlyn mHealth application were associated with improved asthma control, treatment adherence, and self-monitoring, as well as more favorable maternal and neonatal outcomes, compared with those receiving routine antenatal care. However, the quasi-experimental design, convenience sampling, and non-randomized allocation limit causal interpretation and the generalizability of the findings. Therefore, the findings should be interpreted cautiously. Recommendations: Larger randomized controlled trials with standardized protocols, adequate sample sizes to evaluate maternal and neonatal outcomes, and longer follow-up periods are recommended to confirm these findings and further assess their clinical relevance. Full article

Background/Objectives: Chronic non-cancer pain often requires approaches beyond pharmacological treatment. This protocol describes a mixed-methods study evaluating the effectiveness of a multicomponent intervention based on non-pharmacological therapies (NPhTs) and exploring participants’ post-intervention experience. Methods: The design includes a randomized, controlled, two-arm parallel-group clinical trial with 1:1 allocation, embedded within a mixed-methods approach that includes a qualitative phenomenological strand. This study will be conducted at Hospital San Juan de Dios del Aljarafe, Spain. A total of 160 adults with chronic non-cancer pain will be randomly assigned to the intervention or control group. The intervention group will receive five weekly 3.5 h face-to-face sessions combining pain education, emotional regulation, cognitive-motivational and mind–body techniques, lifestyle recommendations, peer support, and home practice, supported by a mobile application. The control group will continue usual care, without the workshop or intervention-related app content. The primary outcome will be change in the EuroQol-5D (EQ-5D) index from baseline to 1 month. Secondary outcomes will include the EQ-5D visual analogue scale (VAS) score, pain intensity, well-being, self-esteem, resilience, anxiety and depression, pain catastrophizing, medication use, and healthcare resource use. Outcomes will also be assessed at 4 months and, in the intervention group, at 7 months. Semi-structured interviews will explore the participants’ experiences, perceived usefulness of the techniques, and barriers and facilitators to maintaining the strategies learned. Conclusions: This protocol will evaluate the effectiveness and acceptability of a structured group-based NPhT intervention for chronic non-cancer pain. The findings may inform its applicability to person-centered and self-care-oriented models of care. Trial registration: ClinicalTrials.gov NCT06440668. Full article

Environmental degradation in Colombia is often studied through separate sectoral lenses, although air pollution, emerging water contaminants, urban noise, mining-related soil contamination, artisanal-mining exposure, and material throughput arise from overlapping institutional and economic conditions. This review compares these domains to identify how environmental-health protection, social equity, resource use, and implementation capacity can inform sustainability governance. A structured integrative synthesis of 62 peer-reviewed, institutional, and conceptual sources organized evidence into cross-domain matrices and six ordinal dimensions: structural entrenchment, human-health burden, ecological propagation, persistence, financing attribution, and implementation complexity. A pattern was treated as convergent when it recurred in at least four domains and retained its meaning under a three-domain sensitivity threshold. Four convergences were identified: lock-in in mobility, production, and livelihood systems; coupled health–ecosystem effects; disproportionate exposure under informality and territorial inequality; and the need for policy portfolios rather than single instruments. Three divergences shaped feasible action: flow versus stock pollution, short versus long response horizons, and concentrated versus diffuse financing responsibility. The synthesis supports a proposed dual-track framework. Immediate measures prioritize source control, surveillance, safer work, and protection of exposed populations; longer-term measures address clean infrastructure, remediation, and the redesign of production and consumption. The profiles, horizons, and portfolio shares are interpretive planning tools, not measured effects, aggregate rankings, or optimized allocations. The contribution is the integration of established governance concepts into a Colombian comparison that connects urgent exposure reduction with equitable, sustained environmental improvement. Full article

To address charging-resource contention and task-allocation conflicts in multi-UAV operations supported by a single mobile charging vehicle (MCV), this paper develops GEMS-DQN (Global-Enhanced Multi-Agent Scheduling Deep Q-Network), a centralized discrete-action scheduling framework for coordinated task and charging decisions. The framework uses a joint individual–global state representation to characterize UAV energy, task urgency, spatial information, global task progress, and charging-resource utilization. A normalized system-level reward with a dynamic conflict penalty provides explicit feedback for task-assignment and charging-resource conflicts. Per-UAV Q-values are used for feasibility masking and top-k action ranking, while beam search constructs a bounded joint-action candidate set for Monte Carlo Tree Search (MCTS) under stochastic MCV motion. Experiments are conducted over 30 independent training runs. At 800 training iterations, GEMS-DQN achieves a total score of $883.7\pm 22.4$, a task completion rate of $92.1\pm 3.4\%$, an average energy consumption of $10.3\pm 0.5\%$, and a conflict rate of $0.091\pm 0.018$. Compared with MAPPO, the strongest modern MARL baseline evaluated, GEMS-DQN improves total score by approximately $5.6\%$ and task completion by 7.9 percentage points, while reducing average energy consumption by 0.4 percentage points and conflict rate by 0.050. Ablation, reward-sensitivity, and scalability analyses further demonstrate the complementary effects of global information, conflict-aware learning, and bounded look-ahead search, while revealing the expected computation–performance trade-off of the centralized framework. Full article

Background/Objectives: Orchestra musicians are exposed to prolonged asymmetric postures and repetitive upper-quarter loading that may influence postural-control strategies. This exploratory controlled intervention study examined whether a four-week mobilization-based exercise program performed once or twice weekly was associated with changes in static sensory-dependent balance and dynamic voluntary weight-shifting control. Methods: Twenty-three university orchestra musicians (5 males, 18 females; mean age 24.57 ± 3.41 years) were allocated to a no-intervention control group (n = 7), a once-weekly mobilization group (EG1; n = 8), or a twice-weekly mobilization group (EG2; n = 8). Because no contemporaneous documentation of the allocation-sequence generation could be retrieved, the study is reported as a non-randomized exploratory controlled intervention study, and it was registered retrospectively at ClinicalTrials.gov (NCT07632950; first posted 8 June 2026, after study completion). Postural control was assessed at baseline, Week 1, and Week 4 using CTSIB and LOS tests. Results: No between-group differences were detected for any CTSIB outcome at any time point or for change scores. Although selected CTSIB variables changed within the control and EG2 groups, the absence of between-group differences indicates that these changes were not intervention-specific. For LOS outcomes, change-score comparisons were significant for Reaction Time (H = 9.076, p = 0.011, ε² = 0.354) and Directional Control (H = 6.214, p = 0.045, ε² = 0.211). After Bonferroni adjustment, only the greater reduction in Reaction Time in EG2 versus control remained significant (p_adj = 0.027). Conclusions: In this small exploratory study, twice-weekly mobilization was associated with faster LOS movement initiation, but the absolute and clinical importance of this change remains uncertain. CTSIB outcomes did not demonstrate an intervention-specific effect; therefore, the findings should be considered hypothesis-generating. Full article

Background/Objectives: Reduced hand function significantly contributes to disability in patients with systemic sclerosis (SSc). We aimed to evaluate the effects on hand function and quality of life of a multicomponent hand rehabilitation program in patients with longstanding SSc. Methods: This prospective, controlled, interventional pilot study enrolled adults with SSc, disease duration >5 years and ≥1 upper limb joint contracture. The intervention consisted of 10 sessions over 5 weeks, including paraffin wax therapy and individualized hand rehabilitation. The allocation to the intervention or control group was based on the patients’ availability to participate in the program. Hand mobility/function and health-related quality of life were assessed through validated outcome measures at baseline and after 5 weeks. Results: Sixteen patients were included (intervention N = 10; control N = 6), with a median disease duration of 19.5 (15.3–24.3) years. Within the intervention group, significant improvements were observed in right-hand Hand Mobility in Scleroderma (HAMIS) score (p = 0.033) and in the Short Form (SF)-36 domains of “Energy/fatigue” (p = 0.008), “Emotional well-being” (p = 0.009), “Pain” (p = 0.020), and “General health” (p = 0.050). Compared with controls, significant benefit was observed in the SF-36 “Energy/fatigue” (p = 0.002; d = 1.97) and “General health” (p = 0.011; d = 1.50) domains. Between-group differences in hand function measures did not reach statistical significance, although moderate-to-large effect sizes favored rehabilitation across several outcomes. Conclusions: In this pilot study, a multicomponent hand rehabilitation program was associated with improvements in quality of life in patients with longstanding SSc, despite limited effects on hand mobility. These preliminary findings suggest a potential benefit of hand rehabilitation even in advanced disease. Full article

Agricultural mobile platforms provide the physical foundation for sensing, field operations, and material handling in smart farming, yet their design is strongly constrained by crop geometry, terrain conditions, task-specific payloads, energy demand, and operational reliability. This review examines agricultural mobile platforms from four complementary perspectives: design and operational requirements, platform classification, powertrain and mobility technologies, and agricultural applications. Wheeled, tracked, legged and wheel-legged, and rail-guided or constrained-motion platforms are compared in terms of mobility characteristics and suitable operating environments. Power sources, drive systems, steering mechanisms, mobility control, and platform–implement integration are further discussed, with particular attention to electrification, distributed drive, dynamic loads, and coordinated power allocation. Representative applications include crop monitoring and phenotyping, precision crop management, weeding and harvesting, and transportation and multi-purpose operations. Current challenges arise from the strong coupling among terrain adaptability, payload, energy capacity, autonomy, and long-term reliability, as well as limited interoperability between platforms and implements. Future development should emphasize task-oriented reconfigurable platforms, standardized mechanical and electrical interfaces, task-level energy management, and mobility control that accounts for real-time platform and terrain conditions. Full article

Timely vehicular sensing is important for traffic monitoring, cooperative driving, and road-safety management. High mobility, time-varying wireless conditions, and limited edge resources nevertheless make information freshness difficult to maintain. This paper studies age of information (AoI) minimization in a three-layer UAV-assisted edge network comprising vehicle devices (VDs), unmanned aerial vehicles (UAVs), and a cloud center (CC). VDs periodically generate sensor-data packets, UAVs provide mobile edge processing and data-relaying services, and the CC coordinates system-wide resource allocation. The joint optimization of sensor-data transmission, UAV movement, packet processing, computation offloading, and bandwidth allocation is formulated within a cooperative multi-agent framework. To solve this problem, we propose a collaborative heterogeneous federated actor–critic (CHFAC) framework. Its significance-aware federated learning mechanism evaluates local model updates according to update significance, alignment with the global learning direction, and training stability and uses the resulting contribution scores for non-uniform agent selection and contribution-weighted aggregation. In the considered simulation setting, evaluation over 1000 test episodes yields an average AoI of $7.45\pm 1.65$ and a worst-case AoI of $38.72\pm 24.06$. The average AoI is $79.0\%$, $63.9\%$, and $29.2\%$ lower than that obtained by the implemented HF-MARL, H-MAAC, and non-federated baselines, respectively. These results demonstrate the effectiveness of CHFAC for freshness-aware vehicular sensing in dynamic UAV-assisted edge environments. Full article

The integration of semantic communication with unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) offers a promising approach for next-generation wireless systems. However, jointly optimizing semantic reliability, physical-layer security, and energy efficiency remains challenging. This paper investigates an IRS-assisted UAV semantic secure communication system in the presence of a potential eavesdropper, with the objective of maximizing semantic secure energy efficiency (SSEE). We first introduce a semantic similarity-based secure energy efficiency metric to capture the trade-off among transmission reliability, physical-layer security, and UAV energy consumption. The semantic symbol number, UAV trajectory, transmit power, and IRS phase shifts are jointly considered under mobility, secrecy, and energy constraints. To efficiently solve the resulting mixed discrete-continuous optimization problem, we develop a hierarchical optimization framework: the outer layer exhaustively searches over a finite set of candidate semantic symbol numbers, while the inner layer solves the continuous resource allocation problem using a heuristic-guided soft actor–critic (HG-SAC) algorithm. Simulation results show that the proposed framework achieves noticeable SSEE gains over several benchmark schemes. Full article

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by ubiquitous connectivity of vehicles, roadside infrastructure, pedestrians, edge/cloud platforms, and smart-city services. With the IoV evolving towards highly connected, autonomous and data-driven mobility ecosystems, it needs to meet challenging requirements for low latency, scalability, interoperability, security, privacy and trust. This paper presents a comprehensive cross-layer approach for intelligent, secure and privacy-preserving IoV systems. It is built upon an analytical framework and systematically studies the perception, communication, edge/cloud computing, blockchain-enabled trust and application layers of IoV technologies. In addition, the paper presents an in-depth review of the enabling techniques such as machine learning (ML), deep learning (DL), reinforcement learning (RL), federated learning (FL), blockchain, cybersecurity mechanisms, digital twins, edge computing, 6G integration, and resource allocation. Moreover, it discusses the interplay and trade-offs between intelligence, security, privacy, computation, latency, and scalability. The survey also covers other significant challenges like intrusion detection, decentralized authentication, privacy-preserving learning, blockchain overhead, semantic interoperability, post-quantum security, and standardized datasets. This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions. Full article

Community Mobile Health Vehicles (CMHVs) represent an innovative healthcare delivery model that integrates medical services into residents’ daily community life. By extending healthcare coverage to underserved populations, CMHVs can improve access to health management services while enhancing the efficiency of healthcare resource utilization. However, existing studies have primarily focused on health outcomes or the adoption of digital health technologies, with limited attention paid to users’ willingness to utilize CMHVs and their relationship with sustainability dimensions, including social equity, economic efficiency, and environmental responsibility. Drawing on survey data collected from community residents in China, this study employs Exploratory Factor Analysis (EFA) to identify the key determinants influencing users’ willingness to use CMHVs. Furthermore, the Entropy Weight Method (EWM) is applied to prioritize both the identified factors and their corresponding design strategies according to their relative importance. The results reveal five principal determinants: Cognitive Ease, System Adaptability, Institutional Trustworthiness, Technical Reliability, and Environmental Compliance. These factors and their associated design strategies exhibit varying levels of importance in promoting user engagement, optimizing resource allocation, and facilitating community integration. Based on the findings, this study proposes a set of sustainability-oriented design prioritization strategies for CMHV service systems. The proposed framework provides both theoretical and empirical insights for urban healthcare service planning and supports the coordinated achievement of economic, social, and environmental sustainability goals. The study further offers evidence-based guidance for the implementation, continuous improvement, and long-term sustainability of CMHVs within urban healthcare and mobile health service systems. Full article

Wearable inertial sensors have become tools for objectively assessing mobility in older people. This study analyzed whether wearable inertial sensors could detect exercise-induced mobility changes and compared the effects of multicomponent training (MCT) and elastic band training (EBT) on mobility and physical function in older women. Forty-two participants were randomly allocated to either the MCT (n = 21) or EBT (n = 21) group, where 38 (EBT: n = 19; MCT: n = 19) completed the 16-week intervention. Outcomes included instrumented Timed Up-and-Go (iTUG) assessed with a wearable inertial sensor, the Senior Fitness Test, maximal isometric handgrip strength, conventional TUG, anthropometric measurements, health-related quality of life, and blood biomarkers. Data were analyzed using age-adjusted linear mixed-effects models. The iTUG showed shorter completion time (p < 0.001), reduced middle-turn duration (p = 0.004), and increased cadence (p = 0.007). Significant time effects were also observed for chair stand (p = 0.011), arm curl (p < 0.001), and conventional TUG (p = 0.009). No significant group × time interactions were detected. After adjustment for multiple comparisons, no significant changes were observed in anthropometric measures, health-related quality of life, or blood biomarkers. Wearable inertial sensors detected training-related mobility changes, and both exercise programs improved physical function and mobility without between-group differences. Full article

Sustained enhancement of energy utilization efficiency constitutes a pivotal lever for achieving dual carbon goals. To examine both the local effects and spatial spillovers of the Energy-use Rights Trading System (ERTS) on urban green total factor energy efficiency (GTFEE), this study employs the super-efficiency EBM-GML index to measure GTFEE across 270 prefecture-level cities in China from 2013 to 2022. Utilizing a Spatial-DID model, we find that the ERTS significantly drives local GTFEE improvements, with the positive effect intensifying over time, while concurrently exerting a negative spatial spillover on GTFEE in neighboring cities. Mechanism analysis suggests that the ERTS enhances local GTFEE through increased R&D investment, optimized fossil energy structures, and improved energy resource allocation in pilot cities, yet suppresses GTFEE improvement in adjacent areas via reduced R&D investment, a worsening fossil energy structure, and resource misallocation. These patterns are consistent with cross-regional flows of R&D resources and energy-intensive industries between pilot and non-pilot cities. Heterogeneity tests reveal that the policy’s effects on pilot city GTFEE are more pronounced in cities with larger populations, stronger industrial bases, more abundant human capital, greater resource endowments, and more transparent environmental information disclosure. Moreover, the population size of pilot cities intensifies the negative spatial spillover effect on neighboring GTFEE, whereas a well-established industrial base in pilot cities mitigates such adverse spillovers. It should be noted, however, that the Spatial-DID model is sensitive to the specification of spatial weights, and the mechanism analysis can only provide indirect evidence of cross-regional factor and industrial mobility. These findings offer spatial-effect empirical evidence to inform the further refinement of the ERTS under the dual carbon framework. Full article

This study characterizes the photovoltaic potential of three urban areas in Cuenca, Ecuador, located in the San Sebastián, Totoracocha, and Yanuncay parishes, through twelve technical, energy-related, socioeconomic, and environmental indicators. The analysis was complemented with grid simulations in CYME 9.2 to assess photovoltaic integration capacity in the distribution network and with a multicriteria synthesis using PROMETHEE II. The variables considered included solar irradiation, usable rooftop area, electricity consumption, population density, socioeconomic level, and the operating characteristics of distribution transformers. The results show that photovoltaic potential varies according to urban morphology, electricity demand, and the operating capacity of the grid. Totoracocha exhibited the highest theoretical potential and a self-sufficiency level of 99.1%, but it also recorded the lowest technical utilization factor, at 15.6%, because of transformer constraints. Yanuncay achieved the highest estimated technically available surplus under the evaluated operating scenarios, at 452,737 kWh/year, whereas San Sebastián exhibited the highest technical utilization factor, at 27.2%. The simulations confirmed that transformers constitute the main technical constraint on photovoltaic integration, while medium-voltage lines and voltage levels remained within their operating limits. The technically usable surplus energy could be allocated to induction cooking, electric mobility, or green hydrogen production as alternative, non-simultaneous scenarios. The energy allocated to self-sufficiency could avoid approximately 1022.5 tCO₂/year. Orientation analysis showed statistically significant but limited differences among the evaluated configurations, indicating that orientation should be considered together with other rooftop and grid-related factors rather than as a standalone feasibility criterion. Overall, the proposed integrated framework provides a comparative decision-support basis for sustainable urban photovoltaic planning by jointly considering rooftop potential, electricity demand, grid constraints, socioeconomic conditions, and environmental benefits. Full article