Search Results (661)

Cultural ecosystem services (CES) play a vital role in rural well-being, yet their spatial patterns and local perceptions remain underexplored in many regions, including Türkiye. This study aims to assess the social values of CES in rural landscapes by focusing on the Şarkikaraağaç and Yenişarbademli districts of Isparta Province. Using Participatory Geographic Information Systems (PGIS) and the Social Values for Ecosystem Services (SolVES) models, we collected and analyzed spatial data from 836 community surveys, mapping 3771 CES value points. Sentinel-2A imagery and derived indices (NDVI, NDWI, SAVI, NDBI) were used to classify landscape infrastructures into green, blue, yellow, and grey categories. The results show that aesthetic and recreational services were most highly valued, followed by biodiversity, spiritual, and therapeutic values. Chi-square and Kruskal–Wallis tests revealed significant demographic and spatial variation in CES preferences, while Principal Component Analysis highlighted two key dimensions of value perception. MaxEnt-based modeling within SolVES confirmed the spatial distribution of CES with high predictive accuracy (AUC > 0.93). Our findings underscore the importance of integrating CES into sustainable land-use planning and suggest that infrastructure type and proximity to natural features significantly influence CES valuation in rural settings. Full article

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved raccoon optimization algorithm (KYCOA) to resolve the problem of degradation of positioning accuracy due to multi-source error coupling during UAV target positioning. Firstly, a multi-coordinate system transformation model is established to analyze the nonlinear transfer characteristics of the error, and the Taylor expansion is used to linearize the error transfer process and derive the synthetic error model under the geocentric coordinate system. Secondly, the KYCOA is proposed to optimize the error allocation by combining the good point set initialization strategy to enhance the population diversity, and the golden sine algorithm to improve the position updating mechanism in response to the defect of the traditional optimization algorithm, which easily falls into the local optimum. Simulation experiments show that the positioning error distance of the KYCOA is reduced by 66.75%, 41.89%, and 62.06% when compared with that of the original Coati Optimization Algorithm (COA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), respectively. In the real flight test, the target point localization error of the KYCOA is reduced by more than 40% on average when compared with that of other algorithms, which verifies the effectiveness of the proposed method in improving the target localization accuracy and robustness of UAVs. Full article

Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. This study examined the Huaihai Economic Zone (HEZ)—a quadri-provincial border area—by constructing the evaluation systems for new urbanization and urban resilience. The development indices of the two systems were measured using the entropy weight-CRITIC method. The spatiotemporal evolution characteristics of their coupling coordination degree (CCD) were analyzed through a CCD model, while key driving factors influencing the CCD were investigated using a grey relational analysis model. The results indicated that both the new urbanization construction and urban resilience development indices in the HEZ exhibited a steady upward trend during the study period, with the urban resilience development index surpassing the new urbanization construction index. The new urbanization index increased from 0.3026 (2013) to 0.4702 (2023), and the urban resilience index increased from 0.3520 (2013) to 0.6366 (2023). The CCD between new urbanization and urban resilience reached 0.7368 by 2023, with 80% of cities in the HEZ achieving good coordination types. The variation of the CCD among cities was minimal, revealing a spatially clustered coordinated development pattern. In terms of driving factors, economic development level, public service capacity, and municipal resilience level were identified as core drivers for enhancing coupling coordination. Infrastructure construction, digital capabilities, and spatial intensification served as important supports, while ecological governance capacity remained a weakness. This study establishes a transferable framework for the coordinated development of secondary economic cooperation region, though future research should integrate diverse data sources and expand indicator coverage for higher precision. Moreover, the use of linear models to analyze the key driving factors of the CCD has limitations. The incorporation of non-linear techniques can better elucidate the complex interactions among factors. Full article

With increasing attention to sustainability and energy efficiency in transportation systems, advanced intelligent algorithms provide promising solutions for optimizing urban rail transit operations. This study addresses the challenge of optimizing train operation plans for urban rail transit systems characterized by spatiotemporal passenger flow imbalance. By exploring a combined short-turning and unpaired train operation mode, a three-objective optimization model was established, aiming to minimize operational costs, reduce passenger waiting times, and enhance load balancing. To effectively solve this complex problem, an Improved GOOSE (IGOOSE) algorithm incorporating elite opposition-based learning, probabilistic exploration based on elite solutions, and golden-sine mutation strategies were developed, significantly enhancing global search capability and solution robustness. A case study based on real operational data adjusted for confidentiality was conducted, and comparative analyses with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) demonstrated the superiority of IGOOSE. Furthermore, an ablation study validated the effectiveness of each enhancement strategy within the IGOOSE algorithm. The optimized operation planning model reduced passenger waiting times by approximately 12.72%, improved load balancing by approximately 39.30%, and decreased the overall optimization objective by approximately 10.25%, highlighting its effectiveness. These findings provide valuable insights for urban rail transit operation management and indicate directions for future research, underscoring the significant potential for energy savings and emission reductions toward sustainable urban development. Full article

This paper addresses the scientific needs for investment decision-making in distribution networks against the backdrop of new power systems, constructing a three-tier decision-making system that includes investment scale decision-making, investment structure allocation, and investment project prioritization. Initially, it systematically analyzes the new requirements imposed by the new power systems on distribution networks and establishes an investment index system encompassing four dimensions: “capacity, self-healing, interaction, and efficiency”. Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. Furthermore, distribution network projects are categorized into ten classes, and an investment direction decision-making model is constructed to determine the investment scale for each attribute. Then, for the shortcomings of the traditional project comparison method, the investment project decision-making model is established with the attribute investment amount as the constraint and the maximisation of project benefits under the attribute as the goal. Finally, the effectiveness of the decision-making system is verified by taking the Lishui distribution network as a case study. The results show that the system keeps the investment scale prediction error within 5.9%, the city’s total investment deviation within 0.3%, and the projects are synergistically optimized to provide quantitative support for distribution network investment decision-making in the context of a new type of electric power system, and to achieve precise regulation. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

Grey models have attracted considerable attention as a time series forecasting tool in recent years. Nevertheless, the linear characteristics of the differential equations on which traditional grey models rely frequently result in inadequate predictive accuracy and applicability when addressing intricate nonlinear systems. This study introduces a conformable fractional order unbiased kernel-regularized nonhomogeneous grey model (CFUKRNGM) based on statistical learning theory to address these limitations. The proposed model initially uses a conformable fractional-order accumulation operator to derive distribution information from historical data. A novel regularization problem is then formulated, thereby eliminating the bias term from the kernel-regularized nonhomogeneous grey model (KRNGM). The parameter estimation of the CFUKRNGM model requires solving a linear equation with a lower order than the KRNGM model, and is automatically calibrated through the Bayesian optimization algorithm. Experimental results show that the CFUKRNGM model achieves superior prediction accuracy and greater generalization performance compared to both the KRNGM and traditional grey models. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response. Full article

This paper addresses the challenges of broadband impedance identification in wind farms connected to the power grid, where broadband oscillations can compromise grid stability. Traditional impedance modeling approaches, including white-box and black/grey-box methods, face limitations in real-world applications, particularly when dealing with commercial new energy units with unknown control structures. To overcome these challenges, a novel real-time impedance identification method is proposed for PMSGs(Permanent Magnet Synchronous Generators). The method, called ASSA (Attention-based Shared and Specific Architecture), utilizes a multi-task neural network model combined with an attention mechanism to improve the accuracy of impedance fitting across different frequency bands. A broadband impedance dataset is constructed offline under various operating conditions, incorporating uncertainties like wind speed. The proposed approach offers an efficient solution for impedance identification, enhancing the stability and reliability of grid-connected renewable energy systems. Full article

The determination of the actual crop water requirement (CWR) today represents an important prerogative for combating climate change. A three-year trial was conducted to ad-dress the need to provide adequate support to processing tomato growers in defining the correct amounts of water to be supplied. In fact, the objective of this work was to calculate the water requirement of processing tomatoes, specifically analyzing their irrigation needs using the CROPWAT 8.0 software and through capacitive and tensiometric probes. Furthermore, for both methods, the tomato yield was evaluated both by supplying 100% of its water requirement and by supplying, through regulated deficit irrigation (RDI), 70% of its water requirement. Subsequently, for each irrigation strategy employed and for each CWR calculation method, the water footprint was calculated by analyzing the blue, green, and grey components. In the years 2022 and 2023, there was an overestimation of CWR of 13.5% for IR100 and 13.94% for IR70, and 14.53% for IR100 and 11.65% for IR70, respectively, while in 2024 there was an underestimation, with values of 9.17% and 5.22% for the IR100 and IR70 treatments compared to the values obtained with the probes. The total WF of tomatoes varied between 33.42 and 51.91 m³ t⁻¹ with the CROPWAT model and between 35.82 and 47.19 m³ t⁻¹ with the probes for IR100, while for RDI70, the values ranged between 38.72 and 59.44 m³ t⁻¹ with the CROPWAT method and between 35.81 and 53.95 m³ t⁻¹ with the probe method. In water-scarce regions, integrating the CROPWAT 8.0 model (enhanced with real-world data) and implementing smart systems can significantly improve water management, refine decision-making processes, and mitigate environmental impacts. This approach directly addresses the urgent need for water security within sustainable agriculture. Full article

Amid accelerating urbanization and intensifying climate variability, the Chengdu–Chongqing region faces acute tensions between high-quality agricultural development and water resource sustainability. This study constructs a multidimensional evaluation framework to analyze the spatiotemporal interaction between the Agricultural Quality Index (AQI) and the Water Resource Carrying Capacity Index (WCI) from 2013 to 2022 across 16 municipalities. Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. Spatial analysis reveals persistent heterogeneity: cities such as Ya’an maintain superior WCI due to natural endowments, whereas Ziyang and Zigong lag due to infrastructural and environmental limitations. From 2013–2016, disparities between AQI and WCI widened, with the spatial coefficient of variation (sCoV) peaking due to resource misallocation and industrial imbalance. However, targeted policies since 2016—e.g., integrated water infrastructure, model agricultural zones, and adaptive land-use planning—have significantly improved regional coordination and narrowed these disparities. The study forecasts AQI to reach 2.0 by 2026, with Chongqing potentially exceeding 3.0, driven by technological modernization and resource integration. Policy recommendations include: (1) cross-regional water reallocation; (2) specialty agricultural clusters anchored by core cities; and (3) climate-resilient cropping systems. This research provides a scalable governance framework for reconciling resource constraints and agricultural modernization, offering practical insights for inland economic zones globally. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article

This article examines Vienna’s public housing model as an exemplary case of institutional innovation in the public sector, defined by its regulatory stability, universalist orientation, and resistance to the commodification of urban land. Through a thematic analysis of scientific sources indexed in Scopus and official documents from the City of Vienna and the Austrian legislative framework, the study identifies both the achievements and the structural tensions within the system. The findings reveal a form of slow innovation grounded in the capacity to integrate new agendas—such as social and environmental sustainability or collaborative modes of living—into an already consolidated regulatory framework. However, grey areas persist, particularly with regard to the exclusion of vulnerable groups, community fragmentation, and the limited replicability of alternative models. The study contributes to expanding the concept of innovation in public administration beyond technocratic approaches, highlighting the value of adaptive institutionalism. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article