Search Results (4,358)

High-liquid-limit clay exhibits pronounced water sensitivity due to the strong electrostatic repulsion and weak interparticle bonding within its microstructure, which often limits its direct engineering uses and complicates the reuse of excavated clayey soils generated during the construction of transportation infrastructure. In this study, inorganic salts (KCl, CaCl₂ and FeCl₃) and carboxyl-containing polymers (PAAS, HPMA and CMC) were screened to construct organic–inorganic composite stabilization systems. Based on the screening results, an organic–inorganic composite system composed of CaCl₂ and sodium polyacrylate (PAAS) was developed to regulate interfacial interactions and induce microstructural reconstruction in clay. The synergistic mechanisms governing particle aggregation and dispersion were systematically investigated through Atterberg limit tests, zeta potential measurements, DLVO theoretical calculations, particle size analysis, scanning electron microscopy (SEM) and immersion disintegration experiments, combined with multivariate statistical modeling. Among the tested salt–polymer formulations, a composite system with 2% CaCl₂ and 0.1% PAAS showed the most favorable overall performance, achieving an optimal balance between electrostatic compression and steric stabilization, leading to enhanced structural integrity and delayed water-induced disintegration. Ca²⁺ ions compress the diffuse double layer and promote particle flocculation, whereas adsorbed PAAS chains introduce steric hindrance and interfacial modification. Their synergistic interaction reconstructs the pore–aggregate framework and regulates the interparticle potential energy landscape. DLVO analysis indicates that the optimized system attains a moderate critical interaction distance (h_c = 7.31 nm) and primary minimum depth (DPM = −2.72 × 10⁻¹⁶ J), reflecting a balanced interfacial bonding state. Multivariate statistical analyses further reveal a dual control pathway, in which consistency primarily governs disintegration duration, with additional contributions from surface electrochemical properties, while surface properties, soil structure and consistency collectively influence disintegration initiation. These findings elucidate the interfacial regulation and structural evolution mechanisms in organic–inorganic composite systems and provide insights into the design of composite modifiers for water-sensitive particulate materials, particularly for the resource reuse of high-liquid-limit clay excavated during the construction of transportation infrastructure and related geotechnical engineering applications. Full article

Cascading failures of clustered landslide dams can intensify downstream hazards not only by increasing peak flood magnitude but also by accelerating the rise of water level immediately upstream of successive dams, thereby shortening the available response time before overtopping. This study reports large-scale flume experiments on a three-dam cascade built with identical geometry and similar soil gradation, while systematically varying longitudinal spacing and inflow discharge. The principal measured variable, $C_w\left(t\right)$, is defined here as the local forebay run-up/water-level record measured at a fixed gauge position immediately upstream of each dam. The run-up hydrographs were summarized using peak run-up $C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, threshold-arrival time $t_a$ defined at $0.1 C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, time to peak $t_p$, maximum rising-stage rate $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$, and above-threshold duration $T$. Across ten tests (five spacing configurations under low/high discharge), peak run-up at both downstream dams consistently exceeded that at Dam1, with amplification factors relative to Dam1 of 1.11–1.45 at Dam2 and 1.13–1.42 at Dam3; Dam3 was not always higher than Dam2. Amplification was much stronger in the rising-stage dynamics: $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$ increased relative to Dam1 by factors of 1.56–11.0 at Dam2 and 2.27–14.0 at Dam3, demonstrating pronounced downstream wavefront steepening. Higher discharge produced earlier threshold arrivals and peaks throughout the cascade, whereas shorter spacing generally produced more impulsive downstream responses with sharper peaks and larger rate amplification. Overall, the dataset provides stage/run-up-based constraints on cascade amplification and indicates that, within the present experimental matrix, dam spacing is the dominant geometric control on flood propagation and downstream hazard escalation. Full article

In the context of global warming, the South China Sea is showing an accelerating warming trend, which highlights the impact of climate change on the marine environment of the South China Sea. The interdecadal variation of chlorophyll a concentration (Chl_a) directly reflects the long-term evolution pattern of the upper marine ecological environment in the South China Sea and has significant indicative significance for marine ecological protection. This study investigated the relationships between Chl_a concentration, sea surface temperature (SST), shortwave radiation (SSRD), mixed layer depth (MLD), wind speed (Wind), and geostrophic current (ugo) in the South China Sea over the past 27 years. Statistical methods were used to analyze the differentiated impacts of marine environmental factors under different climate backgrounds on Chl_a concentration. From 1998 to 2007 (P1), there was a decreasing trend in the early stage, from 2007 to 2015 (P2), there was an upward trend in the middle period, and from 2015 to 2024 (P3), there was an upward trend in the recent period. During these three stages, SST and MLD were the core influencing factors. The threshold of SST gradually increased over time, reaching 27.05 °C, 27.24 °C, and 27.32 °C, respectively. The average normalized information flow (NIF) of Chl_a concentration changed from positive to negative. In the early stage of P1, the SST in most areas of the South China Sea was less than 27.05 °C, and in the P2 and P3 periods, the SST in most areas reached the threshold. The thresholds of MLD were 22.33 m, 21.64 m, and 33.68 m, respectively. The average NIF of Chl_a concentration was positive in all periods. The MLD in most areas of the South China Sea did not reach the threshold in all three periods. These findings emphasize the different roles of marine environmental factors in regulating Chl_a concentration in the South China Sea, providing a scientific basis for the ecological health monitoring of marine fishery areas, disaster warning, and adaptive management in response to climate warming. Full article

Wildfires increasingly threaten the reliable operation of electric power systems due to climate-driven factors and expanding infrastructure. However, existing research remains fragmented, limiting the development of integrated resilience strategies. The objective of this study is to systematically review the literature on power system resilience under wildfire events, focusing on modeling approaches, operational strategies, and learning-based methods. This review was conducted in accordance with PRISMA 2020 guidelines. A structured search was performed in the Scopus database (May 2025; updated January 2026). Studies published between 2016 and 2025 were screened in two stages using predefined eligibility criteria. Studies addressing power system operation under wildfire disturbances with optimization or learning-based methods were included, whereas purely ecological studies were excluded. Thirty studies were included. Data extraction and qualitative thematic synthesis were conducted across four analytical layers. Risk of bias was not formally assessed, and no meta-analysis was performed. Results show increasing research activity and a shift toward stochastic and data-driven methods. Optimization remains dominant, while reinforcement learning is emerging. Hybrid approaches that integrate optimization and learning-based methods are emerging as particularly promising solutions. However, the evidence is limited by methodological heterogeneity and lack of standardized validation. Full article

Climate-related extremes such as floods and droughts have been the main causes of natural disasters in southern Africa in recent years, with noticeable trends in climate extremes being observed. The Limpopo Province in South Africa has been especially prone to these extremes. The extreme precipitation in Limpopo is mainly caused by a mix of intense tropical weather systems and La Niña conditions, both exacerbated by climate change. Climate change exacerbates current water challenges across the province by affecting precipitation patterns, distribution, timing and intensity, leading to extreme climate events such as floods and drought. The historical and future trends of precipitation and relevant extreme indices using observed data from the South African Weather Service and CORDEX ensemble model simulations under the RCP4.5 and RCP8.5 scenarios were examined. An analysis of all precipitation data suitable for the study of long-term variability and trends indicates that most areas underwent drying to various degrees over the last century, especially the central and western parts. Drier conditions over the eastern parts have become more prevalent over the last 50 years. Also, more extremes on a sub-seasonal basis were experienced. Regarding future scenarios, three projected time periods compared to the baseline period (1976–2005) were examined: Current climatology (2006–2035), near-future (2036–2065), and far-future (2066–2095). Most areas will experience a further decrease in precipitation under both emission scenarios, especially in the south-east, central and extreme northern parts. In addition, these areas are expected to experience a decrease in the frequency of heavy precipitation days for all periods under both RCP scenarios, mainly due to drying. Consecutive dry days are expected to increase significantly. Transitioning to renewable energy and enhancing natural carbon sinks can reduce emissions, while prioritizing resilience through renewable energy, water management, and climate-smart agriculture will help address climate change challenges in the province. Full article

This study applies a dynamic strategic foresight to examine how Unmanned Aerial Systems (UAS)-based Advanced Air Mobility (AAM), supported by Advanced Communication Systems (ACS), can be integrated into a coherent System of Systems (SoS) for sustainable and effective Disaster Management (DM). These three domains (AAM, ACS, and DM) form a strongly coupled Internet of Things (IoT) triad within an integrated SoS. Using lessons learned from previous or running research projects of the contributing authors, i.e., SUDEM, REGUAS, 5G!Drones, and ETHER, the foresight identifies key enablers—including resilient 5G/6G communication architectures, interoperable data fusion frameworks, and UAS-supported situational awareness. It highlights structural challenges such as fragmented standards, limited cross-agency data integration, and gaps in ACS redundancy for emergency operations. The resulting roadmap outlines development priorities for ACS-enabled AAM, from unified communication protocols and hybrid TN-NTN architectures to education and capacity-building for digital-centric DM. Practically, the findings suggest that policymakers should prioritise harmonised regulatory frameworks for AAM-ACS interoperability and invest in global data exchange standards, while system designers should incorporate redundant communication layers and modular SoS architectures to ensure operational continuity under extreme conditions. Full article

Cultural heritage sites in arid regions are often underestimated in terms of flood risk; however, the increasing frequency and intensity of extreme precipitation events under climate change have significantly amplified threats to these fragile environments. Taking the Xixia Imperial Tombs in China as a case study, this research investigates strategies for flood hazard prevention and control for cultural heritage in arid areas. By situating the study within the broader context of climate change and global heritage conservation, the paper examines the impacts of flooding on heritage sites and the historical evolution of flood control measures. It further integrates an analysis of the site’s geographical characteristics, traditional flood management structures, and contemporary conservation practices. The study systematically elucidates the compound risks of “drought–desertification–sudden flooding” faced by cultural heritage in arid landscapes. The findings suggest that heritage protection should transition from reactive, post-disaster restoration toward proactive preventive conservation. This shift requires the integration of both engineering and non-engineering measures, supported by technology-based systems such as environmental monitoring and early warning platforms, to establish a comprehensive risk management framework. The research highlights that overcoming the prevailing misconception that “arid regions are free from flood risks,” embedding heritage flood management into regional planning, and ensuring legal, financial, and interdisciplinary cooperation are essential for the long-term safeguarding of cultural heritage in arid environments. This study offers practical insights and a transferable reference for the protection of heritage sites in similar climatic and geographical contexts worldwide. Full article

This study proposes a quantitative identification method for drought–flood abrupt alternation (DFAA) events in the Yellow River Basin (YRB) based on the daily standardized precipitation evapotranspiration index (SPEI) data from 1982 to 2021 and analyzes their spatiotemporal evolution characteristics. The results show that the proposed identification method has good applicability and agrees well with historical records. Grid-scale DFAA events showed an overall slowly increasing trend in occurrence frequency. The mean occurrence frequency, mean duration, and mean intensity were 0.67 events, 30.57 d, and 1.45, respectively. The mean occurrence frequency had a pattern of being higher in the middle and lower reaches and lower in the upper reaches, whereas the mean intensity had a pattern of being higher in the west than in the east and higher in the south than in the north. A total of 16 DFAA events were identified in the YRB, with a mean annual occurrence frequency of 0.4 events per year and an increasing trend across decades. The mean total duration of these events was 31.81 d, and the intensity ranged from 0.96 to 1.79. DFAA events were generally less frequent in the upper reaches and more frequent in the middle and lower reaches and the inland-drainage area. For the level-II water resource subregions, Hekouzhen–Longmen (Subregion IV), Sanmenxia–Huayuankou (Subregion VI), the area below Huayuankou (Subregion VII), and the inland-drainage area (Subregion VIII) had higher occurrence frequencies and larger fluctuations in duration. These findings could provide a scientific reference for flood control, drought relief, and disaster risk management in the YRB. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

On the lunar surface, wrinkle ridges, grabens, and lobate scarps represent key tectonic landforms that reflect the evolution of the Moon’s stress field and its tectonic processes. However, these linear structures often exhibit weak textures, low contrast, and large scale variations, making manual interpretation inefficient and subjective. To address this issue, this study introduces an improved YOLOv8 model, termed HL-YOLOv8, for the automated detection of lunar linear features. The model incorporates a multiscale lightweight channel attention (C2f_MLCA) module into the backbone network to enhance the extraction of fine-grained and weak-texture features and integrates a multihead self-attention (C2f_MHSA) module in the feature fusion stage to improve the modelling of long-range spatial dependencies. In addition, the combination of a dual focal loss and a diversified data augmentation strategy effectively mitigates the detection difficulties caused by class imbalance and weak-feature samples. The experimental results obtained using the global LROC-WAC image dataset demonstrate that HL-YOLOv8 significantly outperforms the baseline YOLOv8 and other comparative models in terms of precision, recall, and mAP@0.5. Specifically, the proposed model achieved an average precision of 73.5%, an average recall of 73.1%, and an average mAP@0.5 of 74.6% on the evaluation dataset, showing particularly strong performance in detecting elongated grabens and boundary-blurred lobate scarps. The global distribution maps derived from the model predictions indicate that HL-YOLOv8 can be applied to comprehensively reconstruct the spatial patterns of the three types of linear structures and identify potential new features in high-latitude and geologically complex regions, demonstrating excellent generalizability and robustness. This study provides an efficient and reliable framework for the automated identification and global mapping of lunar linear features and offers a transferable methodological reference for the tectonic interpretation of terrestrial planets. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article

This study aimed to detect growth stress at the individual-tree level in chestnut (Castanea crenata Sieb. et Zucc.) plantations using UAV-based RGB orthomosaic and multispectral imagery and to determine an optimal NDVI threshold for stress classification. UAV surveys were conducted over a 21 ha chestnut orchard located in Gongju, Chungcheongnam-do, Republic of Korea. NDVI was calculated and analyzed at the individual-tree level using multispectral imagery. Based on field observations, 100 healthy trees and 23 stressed trees were selected for statistical analysis. The mean NDVI value was 0.900 ± 0.012 for healthy trees and 0.816 ± 0.013 for stressed trees, showing a highly significant difference (p < 0.001). ROC analysis showed excellent classification performance with an AUC of 1.00. The optimal NDVI threshold determined using Youden’s index was 0.855. Independent validation in another chestnut plantation approximately 1 km away achieved high classification accuracy using the same threshold. These results indicate that UAV-based multispectral imagery combined with NDVI analysis provides an effective approach for early detection of growth stress and precision monitoring at the individual-tree level in chestnut plantations. This study provides a practical and efficient approach for the early detection of growth stress at the individual-tree level, enabling early intervention against potential declines in tree vitality and proactive management in chestnut orchards. The proposed NDVI threshold-based method offers a simple yet robust tool that can be readily applied in precision forestry and smart agriculture to support large-scale monitoring and informed management decisions for maintaining orchard productivity, enabling cost-effective early intervention at the individual-tree level, which is difficult to achieve using conventional ground-based surveys in complex mountainous orchards. Full article

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article