Search Results (2,775)

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain. Full article

To address the limitations of the temperature parameter adjustment mechanism in the Soft Actor–Critic (SAC) algorithm, this paper proposes an exploration-aware SAC (EA-SAC) algorithm. First, we establish a convergence framework for the non-stationary SAC algorithm using a Prešić-type contraction to handle delayed coupling from historical feedback, and we derive the quantitative relationship between the temperature parameter and the Q-function estimation error bound. Second, we construct a policy improvement metric through reward decomposition and design a corresponding adjustment mechanism based on task performance feedback, enabling the agent to autonomously regulate its exploration intensity. Experimental results demonstrate that EA-SAC improves convergence efficiency by approximately 21.4% and 30.9% compared to two SAC variants. Furthermore, in complex environments with dynamic threats, EA-SAC achieves a 79% task completion rate and the highest overall score, significantly outperforming commonly used baseline algorithms. This research provides a novel approach to the exploration–exploitation trade-off problem in maximum entropy reinforcement learning. Full article

Coastal groundwater in monsoon-dominated regions faces compounding threats from seasonal hydrological extremes and seawater intrusion (SWI), yet the molecular-scale response of dissolved organic matter (DOM) remains poorly understood. We conducted a two-season investigation in Mannar District, Sri Lanka, integrating hydrochemistry, fluorescence spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry to characterize DOM dynamics across shallow and deep groundwater. Dry-season chloride averaged 302 mg/L (shallow—5 to 12 m) and 505 mg/L (tube wells—20 to 30 m), then declined by 60–80% during monsoon recharge. Despite this freshening, DOM dynamics were decoupled from salinity: shallow wells showed dry-season DOC peaks (6.64 mg/L) driven by soil concentration, while tube wells exhibited wet-season enrichment (5.02 mg/L). Shallow aquifers maintained consistently high humification indices (around 0.70) and aromatic-rich DOM, indicating sustained buffering by soil-derived inputs. In contrast, wet-season recharge in tube wells appeared to stimulate microbial processing, as indicated by elevated protein-like fluorescence (C2: 26% to 36%) and a higher contribution of nitrogen-bearing formulas (CHONs: 31.4% to 37.1%). Tube wells also accumulated reduced, energy-rich DOM with correspondingly high molecular lability indices. Paradoxically, correlation networks suggested that these saturated aliphatic and halogenated structures persist due to kinetic protection under low oxygen, high-salinity conditions. These findings indicate that aquifer structure and redox conditions control DOM biogeochemistry in coastal groundwater systems. At the molecular level, DOM dynamics are influenced by aquifer depth and seasonal recharge, leading to a decoupling between salinity and organic matter transformation. Full article

Machine learning helps intrusion detection systems learn new assaults quickly. These systems train on a dataset with several threats and may identify odd behavior. This research detects intrusion using Random Forest, KNN, and Gaussian Naive Bayes. We run the model on a comprehensive dataset. Dynamics Feature Selector (DFS) improves performance. This technique eliminates unnecessary inputs and improves predictions using statistical analysis and feature significance. DFS effectiveness is tested using the NSL-KDD dataset. The recommended hybrid approach, Gaussian NB, Random Forest, and KNN are compared in meta-learning. Getting excellent accuracy with fewer characteristics is the aim. In order to demonstrate how the model may function in actual cybersecurity scenarios, the final test makes use of common performance metrics such as accuracy, precision, recall, and F1-score. The proposed method outperforms previously reported results with around 96.09% accuracy, 93.21% precision, 92.53% recall, 92.79% F1-score, and 93.65% average performance. Full article

Transformer fires within indoor substations constitute severe hydrocarbon fire scenarios characterized by rapid heat release rates and extreme peak temperatures, posing a critical threat to the structural integrity of steel frameworks and power grid stability. To rigorously assess structural safety under such conditions, this study employs a sequential thermal-mechanical coupled numerical methodology combining Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). Focusing on a 110 kV indoor substation, the research simulates the transient, non-uniform temperature fields induced by transformer oil combustion and analyzes the thermo-mechanical response of key steel components. Furthermore, the protective efficacy of two non-intumescent coatings (Material A and Material B) with distinct thermal conductivities is systematically evaluated. Computational results elucidate significant thermal stratification, with upper-level structures sustaining exposure to temperatures exceeding 1500 K. Unprotected steel components subjected to direct flame impingement exhibit severe stress concentrations and plastic deformation, reaching their load-bearing limit within 4825 s. The application of fire-retardant coatings markedly enhances fire resistance; a 5 mm layer of Material A (λ = 0.20 W/(m·K)) extends the time to failure to approximately 9390 s. Notably, increasing the thickness of Material A to 20 mm, or alternatively employing a 10 mm layer of Material B (λ = 0.10 W/(m·K)), effectively mitigates thermal stress concentrations. This ensures structural deformation remains within safe limits throughout a 3 h (10,800 s) fire duration. This study provides a theoretical basis and quantitative engineering references for the optimal fire protection design of substation steel structures. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

Doping remains a major threat to athlete health and sport integrity. Although anti-doping efforts have traditionally focused on athletes, increasing attention has turned to Athlete Support Personnel (ASP) due to their influence on athletes’ decisions, behaviors and involvement in anti-doping rule violations. This narrative review aimed to synthesize the existing literature on the role of ASP (including coaches, physicians, pharmacists, sport psychologists, nutritionists, physiotherapists, parents and other family members) in anti-doping, with particular attention to their influence on athletes’ knowledge, attitudes, behaviors, education and decision-making related to doping. Coaches, physicians, and pharmacists are among the ASP groups most frequently examined in the literature, although substantial knowledge gaps remain across all groups. Coaches shape motivational climates and ethical norms but often lack adequate understanding of anti-doping regulations and supplement risks. Physicians and pharmacists play key roles in medication management and Therapeutic Use Exemptions procedures, though incomplete regulatory knowledge may contribute to inadvertent violations. Nutritionists are central in preventing supplement-related doping, while research on sport psychologists and physiotherapists remains limited despite their preventive potential. Parents significantly shape athletes’ moral development and susceptibility to doping, acting as protective or risk factors depending on family dynamics. Overall, anti-doping education for ASP remains inconsistent. In conclusion, ASP plays an essential yet heterogeneous role in influencing doping-related behaviors. Strengthening role-specific and interdisciplinary anti-doping education, particularly within university programs and professional development, appears critical for enhancing ASP competence and promoting a sustainable culture of clean sport. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

Voice cloning enables realistic fake speech in which a speaker’s identity is preserved while the spoken message is semantically altered. This paper asks whether such meaning-level manipulation leaves detectable traces in transcripts alone. To study this problem, we introduce FakeSpeech+, a paired real–fake dataset built from authentic speech clips and their matched semantically altered counterparts, re-embedded into cloned voices while preserving speaker identity. Using this dataset, we conduct a transcript-first analysis based on interpretable text-only features from two groups: (i) linguistic content organization and discourse dynamics, and (ii) compact production-related proxy cues, including hesitation and disfluency markers. We evaluate these cues under transcript-length control through residualization and compare authentic and manipulated transcripts using statistical and experimental analyses. The results show that only a limited subset of features retains strong separation after length control, with coordination-related structure and emotion anchoring emerging as the clearest cues, while several production-related and discourse-variability features show weaker but still informative differences. In contrast, a number of syntactic, lexical-diversity, and other discourse-level features show substantial overlap after residualization. These findings indicate that transcript-level structure and selected production-related cues remain informative under realistic content-manipulation threats, supporting the value of transcript-based analysis for identity-preserving fake speech. Full article

Natural resource management and area-based conservation are increasingly recognised as outcomes of complex interactions between ecological conditions and social systems, shaped by local knowledge, governance arrangements, and environmental pressures. The Social-Ecological Systems Framework (SESF), developed by Elinor Ostrom, provides a comprehensive framework to analyse these dynamics; however, most applications remain context-specific, limiting cross-site comparability. This study introduces the Conservation and Human Use Index (CHUI), a standardised diagnostic tool that operationalizes SESF principles for comparative analysis across conservation-important areas. CHUI comprises 134 qualitative questions structured across four equally weighted dimensions: (i) Natural Values and Ecosystem Services, (ii) Threats and Pressures, (iii) Governance, and (iv) Social Perceptions. Using an ordinal 0–3 scale with a “Not Applicable” option, the Index enables consistent, flexible application through both desk-based assessments and participatory processes. It generates aggregate and disaggregated outputs that help identify pressure hotspots, governance gaps, and conservation-use synergies. CHUI’s primary innovation lies in translating SESF into a pragmatic and participatory instrument that supports real-world decision-making. Rather than replacing detailed ecological or socio-economic assessments, it functions as a collaborative diagnostic compass to guide targeted investigation and intervention. Its participatory design fosters shared learning, transparency, and co-production of context-specific management pathways, supporting adaptive stewardship and community empowerment. Developed within the Horizon Europe PRO-COAST project and tested across ten European coastal case studies, CHUI advances both the operationalization of SESF and the practice of inclusive, adaptive conservation management. Full article

The power system is the backbone of the energy network, and overhead lines are its vital structures. Weather threats may jeopardise the reliability of lines and make them a weak link. In particular, heavy rainfall episodes can cause failures, especially in mountain areas. Current climate changes may exacerbate the effects on the ground, intensifying rainfall episodes and increasing the frequency of extreme events. In this context, debris flows triggered by rather intense precipitation and characterised by fast kinematics can destroy pylons and electric connections, affecting the infrastructures not only in the upper ridges but also downstream across the fan apex, where powerlines are much more distributed. This study presents an in-depth back-analysis of two debris flow events triggered in concomitance with a heavy cloudburst that occurred in Talamona (Sondrio Province, Italy) in July 2008 and in Campo Tartano (Sondrio Province, Italy) in April 2024. These events hit onsite powerlines, causing blackouts and showing the potential vulnerabilities of the local electricity system. An analysis of rainfall-induced landslide failure is carried out using the numerical model CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) and MIST-DF (Modelling Impulsive Sediment Transport—Debris Flow) with the aim of reconstructing the dynamics of the first (i.e., Talamona) geo-hydrological event. Powerline vulnerability is also investigated against debris flow dynamics, discussing possible strategies to reduce pylon exposure and to increase the resilience of the local electro-energetic network. Since, under climate change scenarios, heavy rainfall episodes are projected to intensify, an alternative approach based on rainfall-threshold curves is presented and applied to both cases of study. The latter, already implemented for civil protection purposes, could be useful in early-warning procedures against potential debris flow hazards. For both methodologies, the findings from the study confirm the strength of the approaches and foster their application in different situations (back-analysis and early warning) to reduce powerlines’ geo-hydrological risks. Full article

Cavitation damage poses a serious threat to the reliability of morning-glory spillways. This study aims to develop a reliability framework for predicting cavitation damage probability under uncertain operational conditions for the Haraz Dam spillway. Cavitation analysis in such structures exhibits inherent nonlinearity and uncertainty, complicating accurate damage prediction. This study incorporates model uncertainties to assess cavitation responses at multiple points on the Haraz Dam morning-glory spillway. Three-dimensional flow simulations were performed using Computational Fluid Dynamics (CFD) and validated against an experimental model from the Iran Water Research Institute, showing satisfactory agreement. Statistical parameters and probability density functions (PDFs) for key uncertainties were determined using the Shapiro–Wilk test. A total of 35 simulation runs, designed via the Central Composite Design (CCD) method, were conducted using Latin Hypercube Sampling (LHS). These simulations incorporated inter-uncertainty correlations and predicted cavitation damage responses at ten critical spillway locations through Response Surface Methodology (RSM). Both linear and second-order response functions were formulated based on interactions among model uncertainties. The results indicated a strong correlation (R² > 0.95) between numerical model outputs and RSM predictions, with the maximum RSM errors remaining within acceptable thresholds. Among the uncertainty factors, the inflow velocity demonstrated the highest contribution (>50%) to cavitation damage responses. These outcomes advance the understanding of cavitation mechanisms and provide a reliable methodology for evaluating damage risks in morning-glory spillways under uncertain operational conditions. Full article

The rapid evolution of malware families poses significant challenges for cybersecurity systems, particularly when newly emerging threats lack sufficient labeled data. Although image-based deep learning approaches have achieved strong performance under fully supervised conditions, their dependence on retraining limits adaptability in dynamic environments. To address this issue, we propose a Retrieval-Augmented Few-Shot Malware Detection Framework that integrates binary-to-image visualization, multimodal embedding using a frozen Vision–Language Model (Qwen2.5-VL), and similarity-based external memory retrieval. Malware binaries are converted into grayscale images and embedded into a semantic vector space without task-specific fine-tuning. During inference, query samples retrieve similar support embeddings from a vector database, and predictions are generated through similarity-weighted aggregation, enabling adaptation without parameter updates. Evaluated on the MalImg dataset with 25 malware families under 1-shot to 10-shot settings, the framework achieves 0.886 accuracy in the 10-shot configuration. Ablation results demonstrate that combining VLM embeddings with retrieval mechanisms provides consistent improvements over individual components. These findings highlight the effectiveness of decoupling representation learning from adaptation for scalable few-shot malware detection. Full article

The Zhuanghe coast in the northern part of the Yellow Sea is one of China’s important fishing and ocean engineering areas. Frequent storm surge events pose a significant threat to residents’ safety and properties. This study used the coupled Finite Volume Coastal Ocean Model (FVCOM) and the Surface Wave Model (FVCOM-SWAVE) to investigate storm surges and wave heights during Typhoons Muifa (1109) and Lekima (1909) in the northern parts of the Yellow Sea and analyze the impact of the typhoon parameters on flood inundation on the Zhuanghe coast. The wind stress comparison in the coupled wave–current model uses synthetic wind field data formed by superimposing ERA5 wind fields with a parameterized typhoon model. The results showed that the simulated and measured tide levels, wave heights, and storm surges were in good agreement, indicating that the coupled model accurately reproduced the dynamics of the storm surges and wave heights during the two typhoons. The maximum significant wave height (H_s) exhibited a right-skewed distribution in the two typhoons’ paths, with extreme values consistently located to the right of the typhoon’s center. The decrease in atmospheric pressure at the center of Typhoon Muifa was significantly, nonlinearly, and positively correlated with the severity of storm surge disasters. A significant correlation was observed between the path of Typhoon Muifa and the disaster intensity. Full article