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Urban resilience has emerged as a crucial objective for achieving sustainable urban development. However, its practical integration into planning remains limited. This study evaluates the extent to which resilience thinking is integrated into Chengdu’s urban planning system by combining policy-theoretical analysis with empirical evidence. Drawing on a framework of nine resilience attributes, we conduct content analysis of Chengdu’s three types of statutory plan documents (Socioeconomic Development Plan, Urban and Rural Plan, and Land Use Plan) and a questionnaire survey of 70 expert planners. The results reveal that resilience is reflected implicitly in the plans through engineering-oriented attributes such as robustness, efficiency, and connectivity. In contrast, social and ecological attributes like inclusion, redundancy, and innovation are largely absent. Planners demonstrate moderate awareness of resilience, yet associate it predominantly with rapid response and infrastructure robustness rather than long-term adaptation or community capacity-building. These findings indicate the dominant top-down, growth-centric planning logic that constrains the adoption of broader socio-ecological resilience concepts. This paper concludes with policy recommendations for institutionalizing resilience in Chinese urban planning through legal mandates; multi-sectoral coordination; and participatory, adaptive planning frameworks. Full article

Coastal saline-alkali areas represent huge under-exploited land and water resources. Due to the high salinity, there exists a great discrepancy between the benefits derived from the cultivation of agricultural crops and the cost in terms of manpower and material resources. The mud crab Scylla paramamosain can survive across a wide range of salinity, making it an excellent aquaculture species in crop–fish co-cropping in coastal saline-alkali areas. However, detailed research concerning economic and ecological efficiency remains unclear. This study investigated the effect of stocking density of S. paramamosain co-cropping with salt-tolerant rice on the economic benefits, physiochemical parameters, and the microecological changes. By elaborate management of aquaculture and rice cropping, together with the comprehensive investigation of physiochemical influence on paddy water and soil, microbial community alteration, and functional gene dynamics, we found that an appropriate density of 6000 ind/ha generated the highest net profit, which is more than 9-fold higher than the rice monoculture. In addition, nutrient inflow increased the environmental burden of higher stocking densities. Microbial community composition and structure were altered, as shown by the 16S amplicon sequencing of water and soil samples. Functional gene chips confirmed that the carbon, nitrogen, sulfur, and phosphorus cycle genes in the microbial community contributed to the microecological function. This study proposes a new salt-tolerant rice–mud crab integrated culture mode, which is customized for the underdeveloped saline-alkali areas, and will be helpful in promoting aquaculture as well as sustainable development. Full article

Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce accuracy and limit transferability. To address these issues, we propose an equidistant sampling method with optimized step size and intervals that precisely quantifies nonuniform density distributions and enhances correction precision. We validate our approach by applying it to correct 480 m resolution GPP simulations generated from an eco-hydrological model, with performance evaluation against 30 m resolution benchmarks using determination coefficient (R²) and root mean square error (RMSE). The proposed method demonstrates a significant improvement over previous elevation-based correction research (baseline R² = 0.48, RMSE = 285 gCm⁻²yr⁻¹), achieving a 0.27 increase in R² and 91.22 gCm⁻²yr⁻¹ reduction in RMSE. For comparative analysis, we implement k-means clustering as an alternative geostatistical method, which shows lesser improvements (ΔR² = 0.21, ΔRMSE = −63.54 gCm⁻²yr⁻¹). Crucially, when using identical statistical interval counts, our optimized-step equidistant sampling method consistently surpasses k-means clustering in performance metrics. The optimal-step equidistant sampling method, paired with appropriate interval selection, offers an efficient solution that maintains high correction accuracy while minimizing computational costs. Controlled variable experiments further revealed that the most significant factors affecting GPP upscaling error correction are land cover, altitude, slope, and TNI, trailed by LAI, whereas slope orientation, SVF, and TWI hold equal relevance. Full article

This study analyses the operational efficiency of urban loading and unloading zones (LUZs) by applying queuing theory without waiting (Erlang B model) and incorporating weighted occupancy time as a fundamental metric. Six scenarios were evaluated in an urban block in Zaragoza, Spain: three using field data obtained through real world observation and three simulated. The system’s performance was compared under conditions of free access with a model that strictly enforces the municipal ordinance for Urban Goods Distribution, restricting access to authorized vehicles and maximum dwell times. The objective of this study is to evaluate the operational performance of different LUZ configurations, assessing how real versus regulation-compliant usage affects system capacity, estimated loss rates, and the spatial temporal productivity of the zones. The M/M/1/1 model in Kendall notation is suitable for representing this type of queuing-free urban environment, and weighted occupancy time proves to be a robust indicator for evaluating the performance of heterogeneous zones. The scenario assessment confirms that the sizing of these zones is correct if their proper use is guaranteed. The study concludes with recommendations and best practices for city governance in formulating urban policies aimed at developing more efficient and sustainable logistics to control land use in the LUZ. Full article

Driven by comparative returns, non-grain use of cultivated land (NGUCL) has intensified, posing risks to food security. This study approaches the problem by employing a risk transfer valuation framework, integrating a multi-regional input–output model with a synthetic risk index to establish China’s virtual cultivated land risk transfer network. Complex network analysis was utilized to explore its features while a temporal exponential random graph model was used to identify driving factors of its formation. Results indicate that fewer provinces took on additional pressures and risks. Despite differing motifs, transfer patterns showed little variation. Block analysis showed increasing net recipient relationships (from four to nine) and variable block divisions. Economic development and industrial structure are negatively associated with outgoing transfers, whereas population, production capacity and resource endowment are positively associated with them. The network exhibits time-dependent stability, with few new risk transfer paths forming. This study provides a theoretical basis and new ideas for optimizing land resource efficiency, re-shaping risk transfer patterns and maintaining food security. Full article

Optimizing end mill geometry is critical for improving performance and reducing costs in the high-volume manufacturing of tools, dies and molds. This study demonstrates a successful optimization framework for solid end mills machining 1.2379 cold-work tool steel, integrating Finite Element Analysis (FEA), Artificial Neural Networks (ANN), and Genetic Algorithms (GA). The optimized tool geometry, derived from four key design parameters, delivered substantial performance gains over an industrial reference (parent) tool. Our ANN-GA model achieved a remarkable predictive accuracy (R = 0.75–0.98) over the RSM model (R = 0.17–0.63) and identified an optimal design that reduced the resultant cutting force by approximately 11% (to 142.8 N) and improved surface roughness by 21% (to 0.1637 µm) compared to experimental baselines. Crucially, the new geometry halved the tool breakage rate from 50% to ~25%. Parameter analysis revealed the width of the land as the most influential geometric factor. This work provides a validated, high-performance tool design and a powerful modeling framework for advancing machining efficiency in tool, mold and die manufacturing. Full article

(1) Background: Ecuador is the fourth largest milk producer in Latin America, where ap-proximately 80% of production originates from small family farms located in the Andean region. Despite their socioeconomic importance, these farms face challenges related to low technical efficiency. While there are specific studies on efficiency in dairy systems from other regions, a knowledge gap persists regarding the temporal evolution of technical efficiency (TE) in Ecuadorian Andean dairy farms, especially during crisis periods such as the COVID-19 pandemic. The objective of this study was to evaluate the evolution of TE of family dairy farms in the Ecuadorian Andean region during the period 2018–2024 and to analyze the impact of the pandemic on said efficiency. (2) Methods: Data Envelopment Analysis (DEA) with input orientation and bootstrap simulation was employed to estimate TE, using data from a representative sample that included between 2370 and 2987 farms per year (approximately 25% of the national database of the Ministry of Agriculture and Livestock). Farms were selected based on the availability of complete information on key variables: number of milking cows, area dedicated to forage, family and hired labor (annual hours), and total annual milk production. Statistical analysis included ANOVA to compare mean TE values between years, post-hoc tests to identify specific differences between periods, and the identification of factors related to the TE. (3) Results: The mean TE of Andean dairy farms increased significantly from 0.37 in 2018 to 0.44 in 2024 (p < 0.10), evidencing sustained improvement, although the mean is still distant from the efficiency frontier. The analysis revealed a notable decrease in TE during 2020–2021, coinciding with the period of greatest impact of the COVID-19 pandemic, followed by progressive recovery in subsequent years. The TE distribution showed that between 70% and 75% of farms remained below 0.50 throughout the analyzed period, while only 8–12% achieved levels above 0.70. The main sources of technical inefficiency identified were relative excesses of labor and forage area in relation to milk production obtained. When compared with international studies, Ecuadorian farms present TE levels substantially lower than those reported in the European Union (>0.80) and similar to or slightly lower than those found in Turkey (0.61–0.71). (4) Conclusions: Family dairy farms in the Ecuadorian Andean region operate with technical efficiency levels considerably below their potential and international standards, suggesting substantial scope for improvement through the optimization of productive resource use, particularly labor and land. The COVID-19 pandemic impacted the sector’s efficiency negatively but temporarily, demonstrating resilience and recovery capacity. These findings are relevant to the design of public policies and technical assistance programs aimed at sustainable intensification of family dairy production in the Andes, with an emphasis on improving labor productivity and the efficient use of forage area. Full article

Europe is dealing with environmental problems that require cooperation beyond national and regional borders. Air pollution, water pollution, biodiversity loss, and waste management are the major issues that are not only complex but also cross borders. Therefore, it is necessary to provide collaborative responses that go beyond the capacity of individual countries. This inquiry centers on the question of what the best way is to set up and govern the transnational cooperation in Europe to confront these major environmental challenges. A systematic bibliographic review of the research conducted between 2010 and 2025 forms the basis of this work. The research combines semantic analysis and Latent Dirichlet Allocation (LDA) modeling to study 80 selected publications to find the tenets of the themes discussed. The identified topics are urban climate change adaptation and mitigation, climate policy and management, adaptation and vulnerability frameworks, land use and biodiversity impacts, and future climate projections and assessment. The findings show that there are strong synergies between biodiversity and climate adaptation, resilience, and environmental governance, as well as the great influence of climate change on the water management sector. The study has unveiled the significance of institutional policy frameworks in bringing about environmental cooperation across borders. In addition, it depicts the relationship between local urban projects and supra-regional policy strategies, in which the two can merge and function efficiently as long as they are working towards the common goal of environmental sustainability. This study is meant to shed more light on the area of environmental governance research, discovering areas that need more exploration, and providing some signposts on how to improve environmental involvement in Europe. Full article

Spatially quantifying the soil carbon sequestration potential (SCSP) is crucial for targeting climate change mitigation strategies like carbon farming. However, static mapping approaches often fail by assuming that the drivers of soil organic carbon (SOC) are stationary. We hypothesized that the hierarchy of SOC controllers is fundamentally non-stationary, shifting from intrinsic stabilization capacity (pedology) in stable ecosystems to extrinsic flux kinetics (climate) in dynamic systems. We tested this by developing a land-use-specific (LULC; Cropland, Forest land, Grassland) ensemble machine learning (ML) framework to quantify the soil carbon saturation deficit (SCSD) across Croatia’s pedologically diverse landscape on 622 soil samples. The LULC-stratified ensemble models (SVM, RF, CUB) achieved moderate to good predictive accuracy under cross-validation (R² = 0.41–0.60). Crucially, the feature importance analysis (permutation MSE loss) proved our hypothesis: in Forest land, SOC was superiorly controlled by intrinsic capacity (Soil CEC, Soil pH), defining the mineralogical C-saturation “ceiling”; in Grasslands, control shifted to extrinsic C-input kinetics (Precipitation: Bio19, Bio12), which “fuel” the microbial carbon pump (MCP) via root exudation; and in Croplands, the model revealed a hybrid control, limited by remaining intrinsic capacity (CEC, Clay) but strongly influenced by C-loss kinetics (Temperature: Bio08), which regulates microbial carbon use efficiency (CUE). This study demonstrates that LULC-specific dynamic modeling is a prerequisite for accurately mapping SCSP. By identifying soils with both high intrinsic capacity (high CEC/Clay) and high degradation (high SCSD), our data-driven assessment provides a critical tool for spatially targeting carbon farming interventions for maximum climate mitigation return on investment (ROI). Full article

Induction motors (IMs) are the largest consumers of electrical energy across most industrial sectors owing to their widespread application. The power losses in IMs significantly depend on the quality of the supply voltage. The presence of various power quality (PQ) disturbances, such as voltage deviation, voltage unbalance, and voltage harmonics, may increase the power losses by over 60%, even if the PQ fulfils the standards. To ensure the energy-efficient operation of IMs, PQ standards should be modified. One possible solution is the implementation of a coefficient of voltage energy efficiency (c_vee), which is proportional to power losses in IMs under PQ disturbances. In this paper, recommendations concerning the implementation of c_vee in the relevant standards are formulated. Additionally, results of PQ monitoring are presented and values of c_vee in land power systems are discussed. Full article

Remote sensing plays a critical role in environmental monitoring, land use analysis, and disaster response by enabling large-scale, data-driven observation of Earth’s surface. Image classification models are central to interpreting remote sensing data, yet they remain vulnerable to adversarial attacks that can mislead predictions and compromise reliability. While adversarial training improves robustness, the challenge of transferring this robustness across models and domains remains underexplored. This study investigates robustness transfer as a defense strategy, aiming to enhance the resilience of remote sensing classifiers against adversarial patch attacks. We propose a novel Multi-Teacher Feature Matching (MTFM) framework to align feature spaces between clean and adversarially robust teacher models and the student model, aiming to achieve an optimal trade-off between accuracy and robustness against adversarial patch attacks. The proposed method consistently outperforms traditional standard models and matches—or in some cases, surpasses—conventional defense strategies across diverse datasets and architectures. The MTFM approach also supersedes the self-attention module-based adversarial robustness transfer. Importantly, it achieves these gains with less training effort compared to traditional adversarial defenses. These results highlight the potential of robustness-aware knowledge transfer as a scalable and efficient solution for building resilient geospatial AI systems. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Employment promotion and employment realization are the core and fundamental problems in the resettlement of land-expropriated farmers transferred to citizens. To solve this problem, it is necessary to clarify the key factors and mechanisms that affect the employment behavior of “farmers to citizens” workers. Taking the labor force from land-expropriated “farmers to citizens” in the construction of Beijing city sub-center as the research object, this paper utilizes Logistic ISM to determine the key factors affecting the employment behavior of the labor force when changing from rural to urban, as well as the internal logical relationship and hierarchical structure among the influencing factors. The results show that only 40% of the migrant workers in the sample have achieved employment, while 69% of the unemployed population have a willingness to work but are limited by age, skills, and family factors. The logistic regression model identifies that the employment behavior of land-expropriated farmers is significantly affected by 10 factors, including gender, age, work experience, hobbies, employment demand, expenditure change, employment difficulty cognition, government training, policy satisfaction and social security. Among them, ISM further reveals that these factors form a three-level hierarchical mechanism of “structure–cognition–behavior”; gender, social security and policy satisfaction are the deep-root factors, and the intermediate factors, such as hobbies and government training, affect employment demand, employment difficulty cognition and other surface factors, and ultimately affect the employment behavior of land-expropriated “farmers to citizens”. Based on this, it is proposed to start from four aspects: differentiated employment guidance, policy transmission optimization, service efficiency improvement, and industrial driving, to systematically promote the realization of more comprehensive and stable employment for the rural-to-residential population, and provide institutional guarantees and practical paths for their sustainable livelihoods. Full article

Canopy configuration affects crop yields by optimizing radiation interception and/or use efficiency in greenhouses. Although tomato metrics have been reported, the effects of row spacing on growth, yield and radiation for different cultivars are not well documented. Here, we examined tomato growth, yield, radiation interception and use efficiency in a greenhouse with four row spacing patterns (T1: 50 cm, T2: 60 cm, T3: 70 cm and T4: 80 cm) and two tomato cultivars (Aomeila1618 and Zhefen202) over a two-year period. A constructed intermediate model was used to simulate tomato radiation interception. Although there were great differences in the genotypes between the two selected cultivars, 50 cm (T1) was the optimal row spacing to produce a larger leaf area per unit of land area, intercept more radiation and ultimately achieve higher yield than the other three row spacing patterns (T2, T3 and T4). The mean total radiation interception across years and cultivars was 559.43 MJ m⁻² in T1, 2.8–3.8% higher than in the other three row spacing patterns. Despite similar dry matter and RUE to Aomeila1618, Zhefen202 in the narrow strip used light more efficiently. These results will help to optimize canopy structures by taking cultivar-specific responses in RUE and growth traits into consideration for high-efficiency tomato production in greenhouses. Full article

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions. Full article