Search Results (1,018)

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

City networks refer to the connections of physical or virtual flows among cities at different spatial scales, including population migration networks, economic networks, information networks and innovation networks. This concept has gradually evolved into an important paradigm for understanding the regional spatial structures. Based on Baidu Index data within the Yangtze River Economic Belt (YREB) in China, this paper constructs an information network and investigates its connection patterns. Using social network analysis, the structural differentiation of the information network is further investigated at both the overall and subregional scales. The results show that the connection patterns of the information network exhibit an obvious hierarchical structure, with the complexity of the spatial pattern gradually increasing from the upstream to the downstream regions. Furthermore, the structural assessment results suggest that the information network is characterized by high agglomeration, high mobility, high hierarchy and low disassortativity. These findings indicate that the information network in the YREB is dominated by several highly developed core city clusters. However, the inherently closed structure resulting from these characteristics may not be sufficiently counterbalanced by low disassortativity. Under sudden disturbances, such a structural configuration may exhibit limited adaptability, delayed response capacity, and slow reorganization and learning processes, thereby weakening structural resilience. This study provides a deeper understanding of intercity relationships within the YREB and offers policy implications for enhancing structural resilience across the Yangtze River Basin. Full article

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Industrial robots are an important strategic resource for manufacturing firms to achieve automation and intelligent development, and their role in corporate risk management has become increasingly prominent. Using data on Chinese A-share-listed manufacturing firms from 2012 to 2023, this paper examines the impact of industrial robot adoption on firms’ risk-taking levels. The results show that for every one-unit increase in industrial robot application, the firm’s risk-taking level increases by 0.206 and 0.384 units, respectively. Mechanism analyses indicate that the use of industrial robots can reduce agency costs and enhance innovation capability, thereby promoting higher levels of corporate risk-taking. Further analysis reveals that the positive effect of industrial robot adoption on firms’ risk-taking is significant only for privately owned firms, firms facing high financing constraints, firms with a higher proportion of technical employees, and firms located in regions with high innovation network density. Meanwhile, the relationship between corporate risk-taking and firm value exhibits an inverted U-shaped pattern, indicating that firms should adhere to the principle of moderation when introducing industrial robots, so as to avoid potential damage to firm value caused by excessive or blind investment. This study extends the literature on industrial robots and corporate risk-taking and provides important implications for Chinese manufacturing firms seeking to enhance their risk-taking capacity through the adoption of intelligent technologies. Full article

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical electrical interactions among nodes. To address these limitations, this paper innovatively proposes a hierarchical coordinated control framework for distribution network fault recovery, combining dynamic zoning and coordinated load shedding. The core novelty of this research lies in integrating the node electrical correlation degree into the load grading process to assist in coordinating dynamic network dynamic zoning. By comprehensively evaluating real-time power flow, the regulation capabilities of distributed resources, and intra-region electrical correlations, the proposed framework adaptively optimizes both the zoning structure and the load shedding sequence. Simulation results demonstrate that, compared with conventional static or uncoordinated methods, the proposed approach significantly minimizes load loss while improving grid recovery efficiency and voltage stability. Ultimately, this coordinated control strategy effectively enhances the resilience and operational safety of high-penetration renewable distribution networks, providing robust support for distribution network operations under a high proportion of renewable energy integration. Full article

The management of European mountain landscapes is increasingly threatened by rural abandonment and escalating environmental risks. This study investigates an innovative Stewardship–Renewable Energy Communities model for the Central Apennines, exploring how post-seismic public reconstruction can serve as a financial engine for territorial maintenance. Utilizing Open Data Sisma administrative records and Photovoltaic Geographical Information System irradiation metrics, this research assesses the solar potential of 18 municipalities within the Sibillini seismic crater. To ensure a reliable baseline, a Building Suitability Coefficient was introduced as a conservative proxy for the public reconstruction sector. Results indicate that the implementation of a distributed network of 6.5 MWp across 325 public nodes, with a specific yield of 1390 kWh/kWp on the entire area, could generate 9 GWh/year. This translates to approximately EUR 1.08 million in annual revenue from energy incentives and sharing. This economic surplus provides a Stewardship Capacity sufficient to fund the active maintenance of 789.77 hectares per year through Nature-Based Solutions, based on a regional rate of 1200 EUR/ha. The novelty of this study lies in bridging post-disaster energy policy with landscape resilience, demonstrating that distributed rooftop solar portfolios represent a non-invasive, self-funding mechanism. By leveraging the reconstructed public stock, mountain territories can transition from passive neglect to active, energy-backed stewardship, offering a reproducible template for high-value cultural landscapes. Full article

This study investigates the spatial association network structure of Green Transportation Development (GTD) in China to support coordinated regional development. Based on panel data from 30 major Chinese cities over the period 2011–2020, an entropy weighting method is used to evaluate urban GTD levels, while social network analysis (SNA) and the Quadratic Assignment Procedure (QAP) are employed to identify the spatial network topology, clustering patterns, and driving factors of GTD. The results show that GTD exhibits significant intercity spatial associations. The overall network structure is relatively stable and exhibits a loose hierarchical pattern, with network density fluctuating between 0.232 and 0.277. Shanghai, Yinchuan, and Nanjing play prominent roles in the core–periphery structure. Block modelling further classifies the network into four functional groups: “net spillover,” “bilateral spillover,” “net benefit,” and “broker” blocks. In 2020, the network contained 214 association ties, of which 176 were inter-block ties, indicating evident cross-block spillover effects but relatively weak intra-block communication. The QAP regression results further reveal that geographical distance inhibits network formation, whereas differences in economic development and transport-related employment promote intercity GTD associations; differences in technological innovation exert a negative effect. These findings suggest that policymakers should reduce administrative barriers, formulate differentiated GTD policies, strengthen regional linkages, and promote intercity cooperation based on complementary advantages to improve the overall performance of GTD. Full article

To address the scattered protection efforts and uneven effectiveness of traditional villages in southwestern Hubei, this study focuses on 92 nationally recognized traditional villages in Enshi Prefecture. By integrating literature research, field investigation, and multi-source data fusion, we developed an innovative model that combines the Analytic Network Process (ANP), entropy weight, and fuzzy comprehensive evaluation, thereby integrating subjective and objective weighting to improve evaluation accuracy. A quantitative evaluation was conducted across 13 criteria and 32 indicators, including traffic conditions, intangible cultural heritage resources, and industrial foundation. The results reveal that traditional villages in Enshi Prefecture exhibit a significant spatial pattern of “overall dispersion with local concentration,” accompanied by a high concentration index. Traffic conditions, intangible cultural heritage, and infrastructure emerge as the core factors affecting protection effectiveness, and a spatial differentiation pattern of “two cores and one corridor” is identified within the region. Based on the quantitative evaluation, we propose targeted cluster protection strategies, including a “dual-core multi-node” transportation network, “three-industry linkage” industrial collaboration, and a living heritage approach that integrates cultural relics with intangible cultural heritage. These strategies were validated in pilot villages such as Yejiaoyuan Village, resulting in significant increases in village satisfaction and tourist volume. The findings provide methodological support and practical paradigms for the systematic protection and sustainable development of traditional villages in southwestern ethnic minority areas. Full article

Enhancing green development efficiency (GDE) is crucial for promoting ecological protection and high-quality growth in the Yellow River Basin (YRB). Using panel data from 48 prefecture-level cities in the YRB from 2010 to 2022, this study applies a Super-SBM model that accounts for undesirable outputs to measure GDE. Then, a modified gravity model and social network analysis (SNA) are used to identify the evolution of its spatial correlation. Additionally, a spatial Durbin model (SDM) is employed to examine the driving mechanisms from the dual perspectives of the innovation rebound effect and external micro-ESG (Environmental, Social, and Governance) reverse-forcing pressure. The results reveal the following: First, the spatial pattern of GDE in the YRB has changed significantly, showing an overall spatial imbalance, with efficiency improvements in the middle reaches and declines in the lower reaches. Notably, resource-based cities have improved GDE due to environmental regulations. Second, the spatial correlation network has evolved from a point-axis layout to a more complex network structure. However, spatial links among cities are mainly driven by geographic proximity, while collaborative ties between cities with similar economic features remain weak. Third, technological innovation has a significant negative effect on local GDE, likely due to the energy rebound effect. Meanwhile, the cross-regional transmission of the external supply chain ESG reverse-forcing mechanism remains weak, constrained by the carbon lock-in effect in the middle and upper reaches. These findings suggest that internal technological structures and external market constraints both influence GDE in the YRB. This research offers an empirical foundation for developing targeted, cross-regional collaborative governance policies. Full article

Background/Objectives: Despite ongoing innovation, few interventions—including Clinical Decision Support Systems (CDSS)—are successfully integrated into routine care. Understanding the process through which innovations are implemented is therefore essential for advancing practice and research. In perinatal settings, evidence on how CDSS implementation unfolds and which strategies support adoption, scale-up, and sustainment remains limited. This study aimed to understand the implementation process, key determinants and implementation strategies of a shared antenatal psychosocial CDSS (i.e., the Born in Belgium Professionals [BIB-Pro]) implemented in a real-world, cross-sectoral perinatal care setting. Methods: A qualitative exploratory case study was conducted between January and March 2025. Data included semi-structured interviews with all seven implementation agents, document analysis of the implementation plan. Directed content analysis was applied using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to categorise contextual determinants and the ERIC taxonomy to classify implementation strategies. Data were synthesised across the four EPIS phases. Results: The implementation process unfolded across all EPIS phases, showing a shift in responsibility from the policy level to the implementation team and healthcare organisations. Implementation was shaped by key determinants across multiple levels: (1) the bridging functions by the BIB-Pro implementation agents connecting policy, innovation, and organisational practice; (2) the system-level leadership and funding by the National Institute for Health and Disability Insurance that enabled initiation and sustainability; and (3) the multilevel stakeholder involvement and inter-organisational collaboration across care settings. In addition, the personal attributes of implementation agents—accessibility, active listening, adaptability, and persistent follow-up—were also identified as relevant factors in the implementation process. Across the implementation process, a broad range of implementation strategies was identified. The most prominent ERIC strategies were developing stakeholder interrelationships, evaluative and iterative strategies, engaging stakeholders, training and educating stakeholders, and providing interactive assistance. Barriers encountered during the implementation process included fragmented care networks, inconsistent regional referral structures, legal uncertainties, and variable digital readiness. In response to these challenges, implementation strategies were applied to support collaboration, clarify procedures and provide targeted support. Conclusions: This study provides insight into how a CDSS was introduced, scaled, and sustained across complex multiple Belgian perinatal care settings. Strong bridging functions, stakeholder interrelationships, iterative evaluation, and system-level support were key factors throughout the implementation process. Across all phases, stakeholder interrelationship strategies and evaluative and iterative strategies were the most prominent and consistently applied, supporting stakeholder engagement and sustained use of the platform. These findings offer actionable guidance for implementing digital tools in multi-organisational and multi-level contexts within perinatal care and other healthcare settings. Full article

Wind turbine blades are prone to multiscale and weak-feature damage in complex natural environments. Accurate and efficient detection is crucial for ensuring the safe operation of wind turbine units. However, existing models struggle to balance detection precision, robustness, and lightweight deployment requirements. In this paper, we propose a lightweight model, Wind-YOLO, for wind turbine blade defect detection based on YOLOv11, with three core innovations: (1) We design a DynamicC3k2 that adaptively adjusts the convolutional receptive field for feature extraction, enhancing fine-grained feature capture of micro-cracks and weak-texture defects. (2) We construct a Cross-Stage Partial with Focused Linear Attention (C2FLA) that precisely focuses on defect regions via a linear attention mechanism, effectively mitigating complex background and noise interference. (3) We propose a Spatially Guided Gated Feature Pyramid Network (SGG-FPN) that optimizes multiscale feature transmission and aggregation through a gated fusion mechanism, improving adaptability to cross-scale defects from millimeter-level cracks to meter-level spalling. Extensive experiments on a dedicated wind turbine defect dataset show that Wind-YOLO achieves an mAP@0.5 of 80.9% and an mAP@0.5:0.95 of 37.1%, achieving an increase of 3.9 percentage points and 2.4 percentage points, respectively, compared with the baseline YOLOv11. Meanwhile, the model has only 2.34 million parameters (2.34 M) and a computational complexity of 6.0 GFLOPs. It delivers dual improvements in precision and lightweight performance, with superior environmental adaptability for real-time wind turbine inspection. Full article

Mountainous cultural landscapes, characterized by fragmented heritage sites, present significant challenges for integrated conservation and regional planning. Taking the Mount Song Culture Circle in Dengfeng City, China—a World Heritage site embodying the core of Chinese ritual civilization—as a case study, this study proposes an adaptive minimum cumulative resistance (MCR) gravity model to optimize heritage corridor resilience against spatial fragmentation and development imbalances. Based on a spatial database of 294 cultural relic units, the adaptive model introduces a dynamic cultural value weight (CVIndex = 0.82) and a time decay function (λ = 0.05) to capture the interplay between cultural significance and ecological constraints—features absent in traditional static approaches. The model identifies three optimized heritage corridor networks—”Seeking Wisdom in the Mountains”, “Searching for Culture in the Landscape”, and “Exploring the City Along the River.” Compared with a traditional static MCR model (αindex = 0.42; core area node density = 0.74 nodes/km²), the adaptive approach improves network connectivity by 37% (α-index = 0.58, p < 0.01) and increases core area heritage node density to 1.12/km². Space syntax analysis further confirms that optimized network integration values strongly correlate with cultural dissemination efficiency (R² = 0.78, p < 0.01, n = 48). This research offers a methodological innovation for resilient conservation of complex cultural landscapes in World Heritage contexts. Full article

Under the dual-carbon goals and energy transition backdrop, the photovoltaic cell has become a crucial pillar for optimizing China’s energy structure and promoting green development. From the perspective of patent intelligence, this study systematically investigates the spatiotemporal evolution paths, coupling characteristics, and driving mechanisms of China’s photovoltaic cell industry and innovation chains, using nationwide photovoltaic cell enterprise and patent data from 2005 to 2024 and integrating spatial gravity center modeling, location quotient analysis, and spatial Durbin models. The findings reveal the following: (1) the spatiotemporal evolution of the dual chains exhibits distinct phases, with a notable developmental leap after 2015. The industry chain shows a pattern of “westward shift and eastern optimization,” while the innovation chain evolves from eastern dominance toward a nationally coordinated, multipolar network. (2) At the macro level, the dual chains demonstrate a coupling trend characterized by “coordinated gravity center migration and spatial distance convergence,” yet significant spatial heterogeneity and mismatch persist at the city scale. (3) Industrial agglomeration has an inverted U-shaped effect on innovation, with regional heterogeneity in its impact, driven synergistically by multidimensional factors such as economic foundation, the innovation environment, and openness. Based on these insights, this study proposes recommendations for optimizing the spatial layout of these dual chains, strengthening multifactor synergy, and implementing regionally differentiated policies, aiming to provide decision-making references for achieving sustainable and high-quality development in the photovoltaic cell. Full article