Search Results (412)

With the development of electrification and intelligent technologies in vehicles, ride comfort issues represented by motion sickness have become a key constraint on the performance of autonomous driving. The occurrence of motion sickness is influenced by the comprehensive movement of the vehicle in the longitudinal, lateral, and vertical directions, involving ACC, LKA, active suspension, etc. Existing motion sickness control method focuses on optimizing the longitudinal, lateral, and vertical directions separately, or coordinating the optimization control of the longitudinal and lateral directions, while there is relatively little research on the coupling effect and coupled optimization of the longitudinal and vertical directions. This study proposes a coupled framework of ACC and active suspension control system based on MPC. By adding pitch angle changes caused by longitudinal acceleration to the suspension model, a coupled state equation of half-car vertical dynamics and ACC longitudinal dynamics is constructed to achieve integrated optimization of ACC and suspension for motion suppression. The suspension active forces and vehicle acceleration are regulated coordinately to optimize vehicle vertical, longitudinal, and pitch dynamics simultaneously. Simulation experiments show that compared to decoupled control of ACC and suspension, the integrated control framework can be more effective. The research results confirm that the dynamic coordination between the suspension and ACC system can effectively suppress the motion sickness, providing a new idea for solving the comfort conflict in the human vehicle environment coupling system. Full article

Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article

PM_2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) pollution is one of the most common problems triggered by the acceleration of urbanization. The coordinated development of cities and the environment has been a topic of significant interest in recent years. Based on the spatiotemporal relationship between the evolution of urbanization levels and PM_2.5 concentrations, and starting from multiple factors characterizing urbanization, this study constructs a coupling coordination degree model between PM_2.5 and urbanization levels to explore the interaction and degree of coordination between urbanization and PM_2.5 in Guangdong Province from 2000 to 2021. The research reveals that the conflict between the urbanization process and PM_2.5 pollution in various cities of Guangdong Province is gradually easing. The year 2011 was a turning point as the PM_2.5 pollution levels in cities that were in an uncoordinated phase began to improve. The coupling coordination degree between urbanization and PM_2.5 pollution in Guangdong Province exhibits significant spatial heterogeneity. The coupling coordination degree in most coastal cities is higher than that in inland cities. Cities in economically underdeveloped regions also face relatively lower pressure from pollution emissions. These regions are characterized by lagging urbanization, and their coupling coordination degree is slowly increasing as urbanization progresses. In economically developed regions, the coupling coordination degree between urbanization levels and PM_2.5 pollution has reached a basic level of coordination, although the specific types vary. Full article

This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. On the algorithmic level, an adapted NSGA-III was implemented on the MATLAB platform (version R2022b), introducing a dynamic reference point mechanism and hybrid constraint-handling strategy to enhance convergence and solution diversity. Taking the northern residential area of the western campus of Dalian University of Technology as a case study, multiple Pareto-optimal solutions were generated. Five representative alternatives were selected and evaluated through the AHP–TOPSIS method to determine the optimal scheme. The results indicated significant improvements in energy, economic, spatial, and ecological dimensions, while also achieving quantifiable control over cultural expression. On this basis, an integrated optimization strategy targeting “form–function–environment–culture” was proposed, offering data-informed support and procedural reference for systematic campus planning. This study demonstrates the effectiveness, adaptability, and practical value of the proposed approach in addressing multi-objective conflicts in university planning. Full article

The synergistic development of ecosystems and socioeconomic systems constitutes a critical foundation for achieving Sustainable Development Goals (SDGs). Large river basins characterized by ecological and socioeconomic spatial heterogeneity frequently present contradictions and conflicts in regional sustainable development, thereby impeding the realization of SDGs. This study employed the Yellow River Basin (YRB), a typical large sediment-laden river system, as a case study. Based on the secondary water resource zones, the spatial variability and temporal evolution of ecosystem service value (ESV), population (POP), GDP, nighttime light (NTL), and Human Development Index (HDI) were analyzed at the water resource partition scale. A consistent mode was applied to quantify the spatiotemporal consistency between ESV and socioeconomic indicators across water resource partitions. The results indicated that from 1980 to 2020, the ESV of the YRB increased from 1079.83 × 10⁹ to 1139.20 × 10⁹ yuan, with no notable spatial pattern variation. From upstream to downstream, the population density, GDP per unit area, and NTL per unit area displayed increasing trends along the river course, whereas the total population, GDP, and NTL initially increased and then declined. Temporally, the population fluctuated with an overall upward tendency, while GDP and NTL experienced significant growth. The spatial distribution and temporal evolution of HDI remained comparatively stable. The coefficients of variation for population, GDP, and NTL were significantly higher than those for ecosystem services and HDI. The study highlighted an overall lack of coordination between ESV and socioeconomic development in the YRB, with relatively stable spatial patterns. These findings could offer a theoretical reference for the formulation of policies to enhance the synergistic development of ecosystems and socioeconomic systems in the YRB. Full article

Against the backdrop of ecological civilization construction and regional coordinated development strategies, functional zone (MFOZ) planning guides national spatial development through differentiated policies. However, a prominent conflict exists between the ecological protection responsibilities and regional development rights in restricted and prohibited development zones, leading to a vicious cycle of “ecological protection → restricted development → poverty exacerbation”. This paper focuses on the synergistic mechanisms between ecological compensation and targeted poverty alleviation. Based on the capability approach and sustainable development goals (SDGs), it analyzes the dialectical relationship between the two in terms of goal coupling, institutional design, and practical pathways. The study finds that ecological compensation can break the “ecological poverty trap” through the internalization of externalities and the enhancement of livelihood capabilities. Nevertheless, challenges remain, including low compensation standards, unbalanced benefit distribution, and insufficient legalization. Through case studies of the compensation reform in the water source area of Southern Shaanxi, China, and the Common Agricultural Policy (CAP) of the European Union, this paper proposes the construction of a long-term mechanism integrating differentiated compensation standards, market-based fund integration, legal guarantees, and capability enhancement. The research emphasizes the need for institutional innovation to balance ecological protection and livelihood improvement, promoting a transition from “blood transfusion” compensation to “hematopoietic” development, thereby offering a Chinese solution for global sustainable development. Full article

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

Human pose estimation is an important downstream task in computer vision, with significant applications in action recognition and virtual reality. However, data collected in a decentralized manner often exhibit non-independent and identically distributed (non-IID) characteristics, and traditional federated learning aggregation strategies can lead to gradient conflicts that impair model convergence and accuracy. To address this, we propose the Federated Gradient Harmonization aggregation strategy (FedGH), which coordinates update directions by measuring client gradient discrepancies and integrating gradient-projection correction with a parameter-reconstruction mechanism. Experiments conducted on a self-constructed single-arm robotic dataset and the public Max Planck Institute for Informatics (MPII Human Pose Dataset) dataset demonstrate that FedGH achieves average Percentage of Correct Keypoints (PCK) of 47.14% and 66.31% across all keypoints, representing improvements of 1.82 and 0.36 percentage points over the Federated Adaptive Weighting (FedAW) method. On our self-constructed dataset, FedGH attains a PCK of 86.4% for shoulder detection, surpassing other traditional federated learning methods by 20–30%. Moreover, on the self-constructed dataset, FedGH reaches over 98% accuracy in the keypoint heatmap regression model within the first 10 rounds and remains stable between 98% and 100% thereafter. This method effectively mitigates gradient conflicts in non-IID environments, providing a more robust optimization solution for distributed human pose estimation. Full article

In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

The efficacy of government subsidy programs in agriculture is frequently compromised by internal policy conflicts that arise between competing government departments. This challenge is addressed herein, with a focus on the policy environment in China, through the development of a game-theoretic model of an agricultural supply chain. This model explicitly incorporates two competing government bodies—the Agriculture and Rural Affairs Department (ARAD) and the Development and Reform Commission (DRC)—each with distinct objectives and performance indicators. Within this framework, the strategic interactions of four subsidy types are analyzed: production and cold-chain subsidies (ARAD), and platform operation and blockchain subsidies (DRC). The findings reveal that department-specific performance indicators can significantly distort the overall effectiveness of subsidies. While individual subsidies may achieve their intended departmental goals, their combined impact is shown to be complex and frequently suboptimal in the absence of higher-level coordination. Notably, a subsidy portfolio combining production and platform operation subsidies is found to consistently yield superior performance in maximizing social welfare. Ultimately, this research contributes a new framework for understanding subsidy policies and provides actionable insights for optimizing interdepartmental coordination to enhance supply chain performance. Full article

As human activities increasingly encroach on ecologically sensitive lake zones, China’s lake-ring urban agglomerations struggle to balance the intensifying human footprint (HF) and declining habitat quality (EQ). Addressing the spatiotemporal interactions between HF and EQ is essential for achieving human–environment coordination. This study examined five major freshwater lake-ring urban agglomerations in China during the period from 2000 to 2020 and developed an HF–EQ assessment framework. First, the coupling coordination degree (CCD) model quantified the spatiotemporal coupling between HF and EQ. Second, GeoDetector identified how HF and EQ interact to influence CCD. Finally, the four-quadrant static model and CCD change rate index formed a dual-dimensional management framework. The results indicate that the spatiotemporal evolution patterns of HF and EQ are highly complementary, exhibiting a significant coupling interaction. High-CCD zones expanded from lakeside urban areas and transport corridors, while low-CCD zones remained in remote, forested areas. HF factors such as GDP, land use intensity, and nighttime lights dominated CCD dynamics, while EQ-related factors showed increasing interaction effects. Five human–environment coordination zones were identified based on the static and dynamic characteristics of HF and EQ. Synergy efficiency zones had the highest coordination with diverse land use. Ecological conservation potential zones were found in low-disturbance hilly regions. Synergy restoration zones were concentrated in croplands and urban–rural fringe areas. Imbalance regulation zones were in forest areas under development pressure. Conflict alert zones were concentrated in urban cores, transport corridors, and lakeshore belts. These findings offer insights for global human–environment coordination in lake regions. Full article

This paper explores the concept of evolutionary urban resilience by framing cities as complex, open, and adaptive Social-Ecological-Technological Systems (SETS), shaped by multi-scalar dynamics, systemic uncertainty, and interdependent crises. It challenges the reductionist view of resilience as a fixed capacity or linear sequence of risk management phases, and instead proposes a process-based paradigm rooted in learning, creativity, and the ability to navigate disequilibrium. The framework defines urban resilience as a continuous and iterative transformation process, supported by: (i) a combination of tangible and intangible qualities activated according to problem typology; (ii) cross-domain processes involving infrastructures, flows, governance, networks, and community dynamics; and (iii) the engagement of diverse agents in shared decision-making and coordinated action. These dimensions unfold across three incremental and interdependent scenarios—baseline, critical, and chronic crisis—forming a ladder of resilience that guides communities through escalating challenges. Special emphasis is placed on the role of Information and Communication Technologies (ICTs) as relational and adaptive tools enabling distributed intelligence and inclusive governance. The framework also outlines concrete operational and policy implications for cities aiming to build anticipatory and transformative resilience capacities. Applied to the case of Taranto, the approach offers insights into how structurally fragile communities facing conflicting adaptive trajectories can unlock transformative potential. Ultimately, the paper calls for a shift from government to governance, from control to co-creation, and from reactive adaptation to chaos generativity, recasting urban resilience as an evolving project of collective agency, systemic reconfiguration, and co-production of emergent urban futures. Full article

Our research examines the dynamics of policy implementation in forest fire management and how local governments in Indonesia can successfully implement these policies. There are two main issues: first, the extent to which forest fire management practices are collaborative, which we assess by examining whether government implementation has focused on developing integrated forest fire management policies represented through collaborative networks. Second, we consider whether and how governments and other competing stakeholders move from conflict to collaboration to enable policy implementation. This research explores whether and how collaborative management can provide a foundation for successful forest fire management, particularly in Riau Province, Sumatra, Indonesia, an area that has experienced significant forest fires and expansion of plantations and oil palm industries. Data were collected through in-depth interviews and observations. We revealed a lack of coordination among local, central, and other stakeholders, which might result in policy “tyranny”. In order to effectively reduce the number of fires, the government needs to empower those responsible for fire prevention through law and policy. However, because forest fire management is inherently top-down and often excludes lower levels of bureaucracy, collaborative management remains challenging. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article