Search Results (8,295)

In the context of China’s rapid urbanization and rural land marketization reforms, the entry of rural collectively owned commercial construction land into the market (ERCCCLM) coexists with the traditional government-led land expropriation, forming a dual land supply system. China’s dual-structure land ownership system—where urban land belongs to the state and rural land to rural collectives—aims to balance land market allocation efficiency with government regulation for public interests. However, significant differences exist between the two patterns in terms of revenue distribution, risk-bearing, and institutional constraints. Consequently, stakeholders including rural collective economic organizations, farmers, local governments, and development companies face dilemmas in selecting land supply patterns, thereby limiting land resource allocation efficiency. The research employs multidimensional economic analysis to systematically compare the ERCCCLM and land expropriation patterns, establishing a land supply pattern selection mechanism with land market price and compensation for expropriation as key variables. First, the expenditure and revenue of stakeholders in both patterns were clarified based on relevant documents, and investment revenue models were constructed. Second, through comparative analysis of revenue formation mechanisms across land supply patterns and sensitivity analysis of multi-scenario calculations, the land market price and compensation for expropriation are identified as key variables determining economic revenue. The findings indicate that when the land market price exceeds compensation for expropriation, ERCCCLM generates higher economic revenue for the rural collective economic organization and farmer. Conversely, when the land market price is equal to or lower than the compensation for expropriation, land expropriation provides more stable revenue. The land expropriation and ERCCCLM examined in this research represent a unique land expropriation and utilization system exclusive to China. The proposed selection mechanism improves land market distribution efficiency and informs policy discussions on optimizing land supply patterns, ensuring a balance between market efficiency and stakeholder equity. Full article

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems. Full article

Frequent extreme heatwaves (HWs) have significantly exacerbated urban thermal risks, yet the regulatory mechanisms of urban morphology remain poorly understood. This study focuses on the core urban areas of Beijing and develops a Local Climate Zone (LCZ)-constrained spatiotemporal data fusion model (LCZ-FSDAF) to generate high-resolution Land Surface Temperature (LST) datasets from 2015 to 2024. By integrating urban–rural gradient analysis with the XGBoost-SHAP model, this study quantitatively resolves the spatiotemporal evolution of land surface temperature during heatwaves and the nonlinear threshold effects of urban morphological parameters, using a representative extreme heatwave event in July 2023 as a case study. The results indicate that the LCZ-FSDAF model achieves high precision across complex urban underlying surfaces (up to 0.946, RMSE as low as 0.762 K), effectively capturing the spatial heterogeneity of the urban thermal environment. Over the past decade, heatwave events in Beijing have exhibited a significant trend of increasing frequency, duration, and intensity. During these events, LST displays a concentric core-high, periphery-low structure; however, the peak temperature shifts toward high-density built-up areas in the sub-core, manifesting a distinct heat island core shift phenomenon. Furthermore, the impact of urban morphology on LST is characterized by significant nonlinearity, with the Normalized Difference Vegetation Index (NDVI) and Mean Building Height (MBH) identified as dominant factors. Notably, Building Coverage (BC) and Sky View Factor (SVF) exhibit pronounced threshold effects across different thermal indicators. Findings of this study are useful for guiding urban planning, optimizing spatial configurations, formulating urban heat island mitigation policies under heatwaves, and promoting the Sustainable Development Goals (SDGs) of cities and communities. Full article

This paper proposes a Transformer-based deep reinforcement learning method for adaptive controller parameter modulation. Unlike conventional approaches relying on metaheuristic optimization with fault-specific tuning or model-based gain scheduling, the proposed method learns a unified parameter modulation policy through direct environment interaction without requiring pre-computed optimal solutions. The key innovation lies in a parameter tokenization mechanism that represents each controller parameter as an independent token, enabling self-attention to capture cross-parameter dependencies for coordinated adaptation. A sequential state encoder extracts temporal fault evolution patterns, while fault-aware cross-attention integrates fault context to guide parameter adjustment according to varying fault types and severities. The policy is trained end-to-end using Proximal Policy Optimization with randomized fault injection. Experiments across three systems demonstrate consistent improvements: compared with GA-based tuning, the proposed method achieves lower ISE using a single policy without fault-specific re-optimization; against PSO-based backstepping control, the proposed method achieves tighter error bounds; compared with TD3-based PI scheduling, RMSE is reduced by 55% and recovery time by 47% under time-varying faults. These results validate that the proposed architecture enables effective fault-aware parameter modulation while preserving baseline controller structure. Full article

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) poses a substantial public health challenge in China owing to its increasing prevalence and substantial economic burden. In response, the diagnosis–intervention packet (DIP) payment reform was implemented to control healthcare costs and enhance service efficiency. Methods: To evaluate the effect of the DIP reform on medical costs, hospitalization days, and individual out-of-pocket payments for COPD inpatients in M City, a pilot city in central China, we conducted an interrupted time series (ITS) analysis using monthly reimbursement records from January 2020 to December 2023. The study included 84,410 hospitalized patients from a city-wide database of 3,241,233 inpatient records with COPD who met the inclusion criteria. The analysis focused on the total healthcare costs, length of stay, and individual out-of-pocket costs. Results: The DIP reform resulted in a 3.7% reduction (95% CI: 0.9% to 6.5%) in the total hospitalization costs in the first month post-reform, with a sustained monthly decline of 0.8% (95% CI: 0.5% to 1.1%). The length of stay decreased from 9.53 (95% CI: 9.31 to 9.75) to 8.74 days (95% CI: 8.62 to 8.86). Conversely, the proportion of out-of-pocket payments relative to total costs increased. Conclusions: While the DIP reform effectively reduced hospitalization costs and days, it led to an increase in individual out-of-pocket payments. Future research should focus on optimizing payment rules, enhancing the supervision of medical services, and refining health insurance policies to achieve the reform’s objectives better and alleviate the financial burden on patients. Full article

The global construction sector, a major consumer of virgin raw materials, is under increasing pressure to transition from a linear to a circular economy model. Marble waste, generated in large quantities during quarrying, cutting, and polishing operations, represents a promising secondary resource for sustainable construction applications. This systematic review was conducted in accordance with the PRISMA 2020 reporting guidelines to critically evaluate the utilization of marble waste in concrete and other building materials. A comprehensive literature search was performed using major scientific databases, and relevant studies published between 2000 and 2025 were analyzed. The findings consistently indicate that marble waste performs most effectively as a fine aggregate replacement at 10–20%, resulting in improved compressive strength, pore refinement, and durability. As a cement substitute, the optimum replacement level is generally 5–10%, beyond which dilution effects may adversely affect strength development. The performance is primarily attributed to improved particle packing and microstructural refinement. This review further highlights future pathways for industrial-scale implementation, mix optimization, standardisation, and policy integration to accelerate circular construction practices. These findings support the potential of marble waste as a sustainable material in advancing circular economy principles in the construction industry. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain can propagate rapidly, causing physical power outages and reducing the resilience of shipboard power systems. To address this issue, this paper investigates the cascading failure reconstruction and resilience enhancement in shipboard cyber–physical systems (SCPSs) under uncertain network attacks. First, a cascading failure propagation model is established to capture the interaction between attack paths and system vulnerabilities, revealing how cyberattacks spread through communication links and infiltrate the power topology. Then, a reinforcement learning-based load recovery strategy is developed, in which a masked proximal policy optimization (masked-PPO) algorithm is employed to optimize reconfiguration decisions under operational constraints. The proposed approach enables adaptive and efficient recovery actions in complex cross-domain environments. Case studies based on representative SCPS scenarios demonstrate that the proposed method improves cascading-failure reconfiguration capability by 13.21% and reduces the average decision time by 18.6%, validating its effectiveness, real-time performance, and scalability. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

The increase in delay-sensitive application tasks requires heterogeneous edge clusters to maintain low online latency and energy efficiency without relying on rigid scheduling policies. To address this, we propose HERO (Hybrid Energy-aware Ranking and Optimization), a lightweight collaborative scheduling framework. HERO utilizes a perturbation-based communication-aware multi-layer perceptron (MLP) predictor to quantify global time sensitivity and discover latent time slack in non-critical paths. A hybrid budget mechanism then converts this slack into customized DVFS decisions. These decisions are based on the inherent computational load and topological criticality to optimize energy consumption. A communication-aware hole-filling strategy dynamically recovers sporadic idle times fragmented by heterogeneous communication overhead. Extensive simulations were conducted across varying DAG depths, parallelism levels, and system utilizations. Compared to state-of-the-art algorithms (NSGA-II, SSA, TOM, and DPMC), HERO reduced the completion time by an average of 10.89% under high-density topologies, and achieved up to 4.04% energy savings across varying task depths. Full article

Based on panel data of Chinese A-share companies listed on the Shanghai and Shenzhen stock exchanges from 2006 to 2023, this study employs a staggered difference-in-differences (DID) approach to examine the impact of the Innovative Industrial Cluster (IIC) pilot policy on corporate carbon performance. The research findings indicate that the IIC pilot policy significantly enhances corporate carbon performance, a conclusion that remains robust after a series of reliability tests, including PSM-DID. Mechanism analysis demonstrates that the policy primarily operates through channels such as fostering corporate green technological innovation, increasing public environmental concern, and attracting the entry of green investors. Heterogeneity analysis further reveals that the policy effect is more pronounced among firms located in the eastern region, those in non-heavy-polluting industries, and state-owned enterprises (SOEs). This study provides micro-level evidence for understanding the green effects of industrial agglomeration and offers references for optimizing cluster policy design to facilitate the low-carbon transition. Full article

Shrinking and aging societies undermine the economic viability of network-based infrastructure once supported by economies of scale and network externalities. This paper develops a conceptual framing of “Denetworking” as a possible reconfiguration strategy in the contraction phase: reducing dependence on highly asset-specific dedicated networks (e.g., pipes and rail tracks) and shifting service functions to distributed systems or generic shared networks (e.g., roads) while maintaining minimum service standards. Rather than presenting a calibrated optimization model or full life-cycle cost (LCC) estimation, the paper proposes a heuristic decision condition for comparing a “keep” scenario (renew and maintain the dedicated network) with a “shift” scenario (Denetworking) and uses quantitative anchors from public sources to illustrate the associated fiscal and institutional trade-offs. Two Japanese cases are used as contrasting illustrations: physical Denetworking, referring to the reduction in or substitution of dedicated physical network assets, in wastewater services (centralized sewerage to decentralized treatment); and functional Denetworking, referring to the transfer of service functions from dedicated networks to more generic shared networks, in regional mobility (local rail to bus/BRT on the road network). The cross-case discussion suggests that Denetworking may become a rational policy option under certain conditions, particularly when demand density declines near renewal-investment peaks and asset specificity increases lock-in. The paper contributes a conceptual vocabulary and comparative policy framing for discussing infrastructure reconfiguration in shrinking societies and highlights practical issues of timing, cost sharing, phased implementation, and stakeholder engagement. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article