Search Results (985)

Background/Objectives: Physical activity (PA) interventions have the potential to delay or reverse frailty in older people, and previous meta-analyses have shown that PA reduces frailty risk. However, introduction and maintenance of PA during midlife might be more impactful as a public health intervention to reduce frailty at the population level. This study aimed to evaluate the impact of longitudinal PA patterns on future frailty risk and to examine whether it is modified by age of onset before vs. after 60 years. Methods: We conducted a systematic review and meta-analysis of prospective population-based cohort studies evaluating the effect of longitudinal PA trajectories on frailty risk of community-dwelling adults below and above 60 years at baseline. Risk ratios (RRs) from studies eligible for quantitative synthesis were pooled using random-effects meta-analysis. Results: Ten studies involving 111,714 participants from Europe, Asia, and North America were included in the systematic review and six in the meta-analysis. Relative to long-term physical inactivity, persistent PA trajectory was associated with 44% reduced risk of frailty (RR = 0.56, 95% CI:0.42–0.76) with moderate certainty, while inclining trajectories were associated with a 38% reduced risk (RR = 0.62, 95% CI: 0.57–0.68) with low certainty. Results: were similar for populations > 60 and <60 years at baseline. Conclusions: Sustained or increasing PA patterns over time are associated with lower risk of future frailty. These findings warrant further implementation studies to quantify the impact of public health policies promoting physically active lifestyles in midlife on the burden of frailty in aging populations. Full article

The decarbonisation of heavy-duty road transport is central to European climate policy, yet the economic viability of battery-electric trucks under real-world route conditions remains uncertain. This study presents a route-specific total cost of ownership (TCO) analysis comparing an 18-tonne battery-electric truck with a comparable diesel truck across four Danish distribution routes. An eight-year net present value (NPV) model at 5% discount rate captures acquisition, energy, taxation, and maintenance costs. The analysis incorporates seasonal variation and distinguishes between depot charging (0.75 DKK/kWh) and public fast charging (3.00 DKK/kWh). The electric truck achieves NPV advantages of DKK 1.01–2.14 million across all routes, with break-even periods of 1.8 to 3.3 years. Charging strategy is the most influential economic factor. The findings demonstrate that fleet-averaged TCO estimates are insufficient and route-level analysis is essential for informed electrification decisions, though results are contingent on the specific policy and operational context of this single-operator case study. Full article

This paper investigates an infinite-horizon discounted stochastic maintenance optimization problem within the framework of dynamic programming. The system degradation is modeled by a discrete-time stochastic process affected by maintenance actions and random disturbances, while the objective is to minimize the expected discounted maintenance and degradation costs. The analysis is carried out on the Banach space of continuous functions equipped with the supremum norm. It is proved that the associated Bellman operator is well defined, maps the function space into itself, and is a contraction with contraction modulus equal to the discount factor. Consequently, the existence and uniqueness of the optimal value function follow from the Banach Fixed Point Theorem, and the convergence of value iteration is established. In addition, rigorous a priori and a posteriori error estimates are derived, providing theoretical stopping criteria for numerical computation. The theoretical results are complemented by numerical experiments illustrating the optimal stationary maintenance policy, the stability of the computed solution under grid refinement, and the influence of the discount factor on both the optimal policy and the convergence rate of value iteration. Full article

Peri-urban rural public spaces are increasingly involved in territorial development, rural-regeneration management, and built-environment upgrading, yet existing research often treats rural regeneration as program implementation rather than as a spatial-development and public-space management problem. This study develops a built-environment governance framework from the 2025 Hsinchu City Community Rural Regeneration Facilitation Program in Taiwan. Using a reflective document-based case-study design, the analysis integrates administrative reports, training records, plan-review materials, spatial-planning documents, built-environment case evidence, agricultural net-zero policy materials, and Taiwanese rural-regeneration literature. Four descriptive readiness ratios are used to profile the governance process: SIE (urban–rural interface exposure), PPC (training participation), RAC (review conversion), and ATR (approval-track readiness). These indicators clarify administrative and governance readiness, but they are not treated as direct measurements of spatial quality, carbon reduction, or user wellbeing. The results identify a four-dimensional management framework for peri-urban rural public-space regeneration: wellbeing-oriented public spaces, low-carbon ecological infrastructure, cultural-landscape continuity, and community-based implementation capacity. The study further proposes standardized ordinal scoring rules, a policy-to-space translation model, and a future post-occupancy and spatial-quality validation scheme. The theoretical contribution lies in reframing rural regeneration as a built-environment governance interface for territorial development, while the practical contribution lies in providing a reviewable framework for managing public-space proposals, construction priorities, maintenance readiness, and future validation in peri-urban rural areas. Full article

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary. Full article

Urban pluvial flood resilience relies not only on engineering defenses but also on effective behavioral coordination among local governments, property management companies (PMCs), and residents. Yet even when risks are recognized, preparedness often fails because costs are immediate and certain, whereas benefits are delayed and probabilistic. This study addresses this dilemma by integrating prospect theory into a tripartite evolutionary game model. We position PMCs as pivotal intermediaries whose maintenance decisions critically influence whether physical infrastructure can be translated into operational resilience. Simulation results show that actors initially remain inactive under uncertainty. Stable collaboration emerges only when government regulation, PMC maintenance, and resident participation reinforce one another. Equal-magnitude policy comparisons reveal an asymmetric policy effect: sufficiently strong penalties induce tripartite cooperation, whereas equivalent subsidies primarily improve PMC maintenance without sustaining resident engagement. Excessive post-disaster relief further weakens ex ante incentives by creating moral hazard. These findings advance community flood governance toward psychologically informed, actor-specific policy mixes, emphasizing clear responsibility allocation, targeted incentives, and conditional relief, to strengthen community-level climate adaptation and adaptive resilience under uncertainty. Full article

Steel-cord conveyor belts are usually assessed using average damage indicators calculated for entire belt sections. Although such indicators are useful for general condition assessment, they do not adequately represent spatially non-uniform degradation across the belt width. This paper proposes a sensor-based, channel-wise framework for condition assessment, remaining useful life (RUL) estimation, and maintenance decision support for steel-cord conveyor belts. The approach uses data from consecutive non-destructive diagnostic scans, in which the belt cross-section is represented as a set of active measurement channels. For each channel, local damage density is treated as a health indicator evolving over time. A simple accelerating degradation model is then used to estimate local degradation intensity and channel-wise RUL. Three decision thresholds are introduced to represent entry into the refurbishment window, the end of economically justified refurbishment, and the critical removal condition. In addition to channel-wise RUL, the framework includes cross-sectional indicators such as the fraction of channels exceeding each threshold, the width of compact critical zones, and life-uniformity measures describing the spatial distribution of degradation. A cost-oriented interpretation is also introduced by linking non-uniform cross-sectional degradation with reduced life utilization and increased cost per unit operating time. The applicability of the framework is illustrated using consecutive diagnostic scans from an industrial conveyor belt loop. The results show that localized degradation may govern belt replacement earlier than average indicators suggest. The proposed approach provides a practical basis for condition-based maintenance, refurbishment planning, and future integration with loading-condition analysis and digital-twin-oriented diagnostics. Full article

With the increasing deployment of multi-unmanned aerial vehicle (multi-UAV) systems in dynamic environments, the problem of efficient cooperative path planning has emerged as a critical challenge requiring urgent solutions. To address this issue, this paper proposes a novel joint optimization framework, named spatio-temporal attention-based multi-agent deep deterministic policy gradient (STA-MADDPG). Rather than proposing a new reinforcement learning algorithm in the strict sense, this work integrates advanced spatial-temporal feature extraction with heuristic gradient guidance. First, a cascaded architecture combining multi-head attention and Long Short-Term Memory (LSTM) networks is utilized to extract key local and temporal features, thereby mitigating the dimensionality curse in dense multi-agent observations. Second, an improved dynamic artificial potential field (DAPF) is integrated into the reinforcement learning framework as a state augmentation mechanism, providing heuristic guidance vectors that accelerate convergence and improve obstacle avoidance. Furthermore, to balance computational complexity and adaptive behavior, a rule-based hierarchical formation strategy is designed. The framework maps predefined formations (elliptical, chain, or wedge) to specific environment categories, while the underlying MARL policy governs the dynamic trajectory planning and topology maintenance. Finally, rigorous comparative and ablation experiments are conducted to evaluate path length, search time, and relative position errors. Statistical analysis demonstrates the effectiveness of the proposed framework, achieving up to a 67.3% reduction in search time and a 91.56% search success rate compared with standard MARL baselines in complex environments. Full article

Modern power grids require safer and more reliable field operations, yet conventional robots often face limitations in unstructured environments because of rigid pre-programming and weak perception–action coupling. This review examines Embodied Intelligence (EI) as an emerging direction for enhancing power-system field operations. We first evaluate the environmental adaptability of morphological carriers, including quadrupeds, humanoids, and unmanned aerial vehicles, and then define the perception–cognition–execution closed-loop architecture used in this review. Three application domains are then examined. Intelligent inspection focuses on active perception and potential open-vocabulary object detection. Live-line maintenance emphasizes Sim-to-Real methods and shared autonomy, while disaster-response applications involve heterogeneous air–ground robotic coordination. The review also discusses the potential for EI to reduce human exposure to hazardous tasks and influence labor structures, while a regional text-based proxy illustrates differences in policy attention to digital infrastructure. Finally, we analyze major constraints, including hardware endurance under extreme climates, edge-computing latency, foundation-model uncertainty and hallucination, cybersecurity, and safety certification. Overall, EI should not be interpreted as a mature replacement for current utility practice; it is a developing technological direction whose safe deployment will require field validation, standardized evaluation, cybersecurity assurance, and continued human supervisory authority. Full article

Peri-urban rural regeneration is often assessed through completed projects or administrative indicators, while the governance mechanisms that sustain rural sustainability receive less attention. This article examines how project-based participation can be translated into governance capacity in Hsinchu City, northwestern Taiwan. Adopting an evidence-bound reflective documentary case-study design, it analyzes policy, spatial planning, training, community project, and rural regeneration records from the 2025 Hsinchu City Community Rural Regeneration Guidance Program. The findings show that regeneration operates through municipal coordination, functional clusters, community participation, and site-based interventions. Six intermediate capacities are identified: proposal-making, coordination, maintenance, interpretive, sustainability-translation, and resilience capacities. The study contributes a transferable framework linking rural sustainability, SDG localization, ESG-oriented governance, and peri-urban regeneration through explicit evidence-use boundaries. It does not claim verified long-term impacts on income, carbon reduction, biodiversity, demographic change, or resident satisfaction; rather, it clarifies how governance capacity may be generated and where future independent verification is required. Full article

This article examines heritage language transmission within Chinese migrant families in Italy, focussing on the role of Family Language Policy and the interplay between standard (Putonghua) and non-standard local varieties (fangyan). Drawing on semi-structured interviews with six families and the primary school teachers of their children (aged 6–8), the study adopts a multi-perspective design to explore how reported language practices, beliefs, and attitudes interact across family and school domains. Findings indicate a predominant shift towards Putonghua as the primary heritage language, often at the expense of fangyan, which tends to be marginalised by parents or reported as being retained only as passive competence among children. This shift reflects practical considerations and evaluative orientations related to prestige, usefulness, and standardisation. Variation across families shows that alternative orientations persist, with one family valuing fangyan for intergenerational continuity. Teachers’ perspectives further suggest a limited awareness of internal linguistic diversity within the student population. The convergence of family-driven shifts and school-based perceptions may contribute to processes of increasing standardisation in the representation and transmission of heritage varieties. This article contributes to research on heritage language maintenance by offering contextually situated insights into how the interplay between social actors and contextual factors may shape linguistic diversity within diasporic communities. Full article

Climate change and wildfire are often treated as separate policy problems, although their costs and ecological consequences converge in fire-prone landscapes. This critical integrative review examines the evidence requirements that connect Mediterranean wildfire-risk governance, restoration, biomass use, carbon accounting, ecosystem services, public procurement and professional capacity. A purposive, theme-based synthesis combines peer-reviewed research, international standards, European Union assessments and audit reports, and Greek implementation documents; Greece is used as a policy case within a wider Mediterranean evidence base. Building on the living- and green-infrastructure literature, this review treats forests as living public infrastructure: dynamic systems whose public functions depend on maintenance, ecological limits and repeated verification. It distinguishes evidence synthesis from policy recommendation and evaluates recurrent shortcuts such as unmaintained fuel continuity, tree planting without diagnosis, carbon dioxide equivalent (CO₂e) claims without credible baselines or permanence strategies, and monitoring platforms without field validation. The resulting framework links risk diagnosis, prioritised intervention, field measurement, integrity gates and public audit. Its scientific contribution is to specify a common evidence chain through which prevention, restoration and climate claims can be compared and tested rather than accepted as activity counts or communication outputs. Full article

Dual-purpose cattle systems are among the most important livestock production systems in tropical regions, contributing significantly to food production and rural livelihoods. However, their considerable diversity in management practices, resource endowments, producer-reported climatic stressors, and production intensity remains insufficiently characterized for effective policy design. Therefore, this study aimed to identify and characterize farm typologies by integrating productive, structural, environmental, and management dimensions and to describe differences in farm-system vulnerability among them. Data were collected from 132 commercial farms using structured surveys. Twenty-three variables were analyzed using Principal Component Analysis of which 22 reached the predefined absolute loading threshold for component interpretation. The resulting component scores were subsequently used in the hierarchical cluster analysis. Seven principal components with eigenvalues > 1 were identified, explaining 68.87% of the total variance. Cluster 1 comprised extensive, milk-oriented farms with the lowest milk productivity but the greatest pasture availability and diversity; nevertheless, these farms were particularly affected by drought. Cluster 2 represented the predominant dual-purpose model, with intermediate productive, environmental, and diversification characteristics. Cluster 3 included more intensive and productive farms with limited land availability, the lowest soil conservation and pasture availability scores, high maintenance expenditure, and low investment capacity. Cluster 4 showed the highest milk productivity but also the greatest exposure to excessive rainfall and northerly winds. Accordingly, appropriate strategies should prioritize drought preparedness and forage conservation in Cluster 1, gradual improvements in efficiency and environmental management in Cluster 2, soil and pasture restoration and reduced maintenance dependence in Cluster 3, and climate-resilient infrastructure and risk-management measures in Cluster 4. These findings demonstrate that dual-purpose cattle systems are highly heterogeneous systems structured by multiple productive, environmental, and organizational dimensions. The results demonstrate that typology-specific interventions are more appropriate than uniform recommendations and provide a framework for improving productivity, sustainability, and climate resilience in tropical livestock systems. Full article

The management of municipal solid waste (MSW) in a region driven by tourism is a challenge. The dynamics of the waste produced can fluctuate significantly. Islands are particularly vulnerable due to geographic constraints and limited infrastructure. Using data to forecast and evaluate MSW management strategies would help make policy decisions. This research introduces a data-driven framework that integrates tourism dynamics with a predictive baseline for MSW generation and facilitates ongoing evaluation of system performance. The framework comprises four operational zones, namely data acquisition, baseline modelling, deviation analysis, and dashboard-based communication. It also follows the waste stream from upstream waste generation, through midstream collection and transfer, to downstream treatment and disposal. Phuket Island, Thailand, was chosen as a case study. The island has strong tourism-driven waste variability, limited waste management infrastructure, and a clear upstream–midstream–downstream waste-management chain. Tourism was explicitly integrated as an exogenous factor to capture transient demand effects. The amount of MSW was modelled using monthly tourism data. Support Vector Regression was selected to establish the expected waste baseline owing to its high predictive accuracy (MAE = 1143 t, RMSE = 1352 t and MAPE = 3.3%) outperforming other tested models (i.e., SARIMAX, Mean Baseline, Seasonal Naive, Random Forest and Histogram-Based Gradient Boost). Then, it was used to establish the baseline under normal conditions. The residual was then calculated as the difference between the observed values, and the performance of the maintenance system can be further assessed using the deviation analysis. A normal range defined within 3$\sigma$ of the residual percentages from a baseline; values outside this range are flagged as anomalies. The results were integrated into a prototype dashboard to support operational monitoring, policy evaluation, and stakeholder-orientated interpretation. The proposed framework provides a practical basis for an evidence-based adaptive policy in tourism-dependent regions. Future work should incorporate waste composition and spatially explicit data to further improve the MSW management protocol. Considering that tourism demand exhibits seasonal patterns influenced by external factors, the results of this research offer an invaluable tool for waste management. Full article

Airport Economic Zones (AEZs) in China are largely guided by central and local government planning, and official planning documents provide important textual signals of industrial priorities. To identify these priorities, this study examines 17 national-level Airport Economic Demonstration Zones (AEDZs) and collects official documents on industrial development issued by central authorities and relevant local governments. Using dictionary-based named entity recognition, word-frequency analysis, clustering, and association rule mining, we quantitatively analyze stated governmental preferences in AEDZ industrial planning, focusing on emphasized industries and their combinations. The results show that local governments frequently emphasize industries prioritized by the central government, including aviation equipment manufacturing and maintenance, electronic information, aviation logistics, professional exhibitions, and e-commerce. They also tend to combine high-end intelligent manufacturing, electronic information technology services, and air cargo transportation in planning narratives. These patterns indicate policy alignment across AEDZs, while high similarity in stated industrial priorities may signal potential risks of redundant construction and resource misallocation if not matched with differentiated implementation. The findings are interpreted as textual policy signals rather than evidence of actual implementation or industrial performance. Full article