Search Results (345)

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article

The durability of building façades is critical to sustainable construction because it affects maintenance demand, safety, and long-term service performance. As building stocks age, especially in rapidly urbanizing countries such as China, reliable prediction of façade degradation becomes increasingly important for service-life planning and maintenance decision-making. However, conventional service-life prediction methods are commonly based on ordinary least squares (OLS) regression, which mainly estimates the conditional mean and may therefore fail to represent the heterogeneity of degradation processes. Using visual inspection data from 375 painted façade samples in Ningbo, China, this study applies quantile regression (QR) to model façade degradation and predict service life. Degradation was quantified using an overall degradation level (ODL) index that integrates defects related to aesthetic deterioration, loss of integrity, and loss of adhesion. The results show that façade degradation follows heterogeneous rather than uniform trajectories, and that the effects of key variables vary across degradation levels. In particular, pollution exposure and water ingress become markedly more influential at higher quantiles, while the effect of routine maintenance weakens in severely degraded façades. After 5-fold cross-validation, the median quantile model reduced MAE by approximately 5.3% relative to the OLS benchmark (0.0537 vs. 0.0567), and the fitted quantiles showed good calibration, with empirical coverage deviations not exceeding 0.007. The QR framework predicted a service-life range of 4.3–31.8 years, substantially wider than the 8.8–20.2 years obtained from the MLR model, indicating a stronger ability to represent uncertainty and high-risk degradation paths. These results show that QR provides a more informative basis for risk-based inspection planning and façade service-life assessment in existing buildings. Full article

Rapid sterility testing of radiopharmaceuticals is essential due to their short half-lives and strict safety requirements. Conventional culture-based methods require several days and are not compatible with clinical workflows. In this work, we present a proof-of-concept droplet-based microfluidic platform for rapid optical detection of bacterial contamination through optical extinction analysis of microdroplets. Monodisperse water-in-oil microdroplets were generated and optically interrogated using a fiber-based detection system. Calibration was first performed using 500 nm polystyrene nanoparticles to establish the relationship between particle concentration and optical extinction. Subsequently, Staphylococcus aureus suspensions were analyzed under aerobic and anaerobic conditions at concentrations ranging from 0 to 230 CFU/mL. The system demonstrated reliable detection of bacterial contamination with estimated limits of detection of ~15 CFU/mL (aerobic) and ~7.5 CFU/mL (anaerobic). The platform enables real-time, high-throughput analysis with minimal sample handling and reduced analysis time compared to conventional sterility tests. This study validates the feasibility of microdroplet-based optical detection as a rapid quality control strategy specifically suited for radiopharmaceutical production, where the short half-lives of common radiotracers impose strict time constraints incompatible with conventional 14-day culture-based sterility tests. The results provide a proof-of-concept foundation for future integration into automated sterility testing workflows, with further validation on real radiopharmaceutical matrices planned as the next step. Full article

Climate change poses significant challenges to the operation and safety of dam and reservoir (D&R) systems, particularly in regions characterized by water scarcity and high climate variability. This study presents a structured methodology for climate risk assessment that integrates regional climate projections, system-specific thresholds, and a semi-quantitative risk matrix approach. A key innovation is the explicit linkage between climate indicators and system performance through physically based thresholds, combined with empirically derived exceedance probabilities from high-resolution climate projections. The methodology is applied to the Almopeos D&R system in northern Greece, using an ensemble of statistically downscaled CMIP6 simulations under two emission scenarios (SSP2-4.5 and SSP5-8.5) and two future periods (2041–2060 and 2081–2100). Three climate indicators are analyzed: TX35 (temperature extremes), CDD (consecutive dry days), and Rx1day (extreme precipitation). Results indicate that temperature increase is the dominant climate risk hazard, leading to increased irrigation demand and reduced system reliability, with risks classified as high to very high. Drought conditions represent a secondary but important risk, becoming critical during prolonged dry periods affecting reservoir storage, while extreme precipitation events exhibit low likelihood but potentially high consequences for dam safety. Adaptation measures are prioritized using a qualitative multi-criteria approach, highlighting the effectiveness of operational measures, while structural and monitoring interventions remain essential for ensuring system safety. The proposed methodology provides a transparent and transferable framework for climate-resilient planning of water infrastructure systems. Full article

Tunnels excavated in non-coal oil- and gas-bearing strata may experience the seepage and intermittent ingress of an oil–gas–water mixture during construction, creating aggressive corrosive conditions that can compromise the integrity of primary support and the safety margin of the final lining. However, the coupled degradation mechanism of primary support and its cascading effect on lining safety under such conditions remain poorly understood. Based on the Huaying Mountain Tunnel project, this study investigates the corrosion-driven damage evolution of primary support and its implications for the structural safety of the secondary lining under wet–dry cycling exposure. Accelerated wet–dry cycling tests were performed on concrete specimens using an on-site crude-oil–formation-water mixture collected during tunnelling, with exposure levels ranging from 0 to 120 cycles. Laboratory observations were then combined with inverse identification of degradation-dependent material parameters to establish a corrosion-informed mechanical description, which was implemented in numerical simulations for structural response assessment. Results show a staged evolution of mechanical properties, with an initial increase followed by progressive deterioration. After 120 cycles, compressive strength, tensile strength, and elastic modulus decreased by approximately 18.9%, 23.1%, and 17.4%, respectively. Degradation is more pronounced in the corroded zone, with tensile capacity and stiffness deteriorating earlier than compressive resistance. Numerical results indicate that corrosion leads to significant stress redistribution and damage development. The sidewall tensile stress reaches 2.80 MPa after 120 cycles, exceeding the post-corrosion capacity, while the safety factor drops below the code threshold at 90 cycles. The overall safety probability decreases from 1.0 to 0.4, accompanied by a degradation in safety grade from Level I to Level IV. These findings provide a quantitative basis for deterioration assessment, safety verification, and maintenance planning for tunnels subjected to oil–gas corrosive environments. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Water scarcity compels wastewater reuse, but lax discharge standards generate a regulatory mirage, misleading the public about safety. Here, “regulatory mirage” refers to situations where formal compliance with discharge standards creates a false perception of safety while ecological risks and degradation persist. Despite formal compliance, treated effluent severely harms Iran’s effluent-dependent Kashaf River, driving eutrophication, salinization, and the downstream transport of unregulated contaminants of emerging concern, including fluorinated substances (PFAS) and pharmaceuticals. These pressures extend beyond the river channel to adjacent natural wetlands, which act as de facto nature-based treatment systems yet are progressively transformed into sacrificial sinks for excess nutrients, salts, heavy metals, and micropollutants. By benchmarking the Iranian Wastewater Discharge Standards (IWDS) against international guidelines (WHO, EU, FAO), this study quantifies a “Permissibility Gap” frequently greater than 10 for key parameters such as BOD₅, nutrients, and trace metals, revealing how concentration-based limits ignore cumulative mass load and mixture toxicity at the basin scale. The Kashaf River case demonstrates that current end-of-pipe regulation undermines both natural wetlands and planned nature-based solutions, including constructed wetlands, in arid regions where effluent reuse is unavoidable. The study argues that aligning discharge standards with global benchmarks, adopting mass-based permits, and explicitly regulating contaminants of emerging concern are prerequisites for truly safe wastewater reuse and for protecting wetland ecosystems in effluent-dependent basins. This study shows that permissive, concentration-based discharge standards in effluent-dependent basins create a regulatory mirage that accelerates river and wetland degradation, and that stricter, mass-based limits are essential for safe wastewater reuse. Full article

In complex marine environments, the safety and efficiency of ship navigation face dual challenges from static obstacles, such as shallow waters and islands, and extreme dynamic meteorological threats, such as typhoons. Existing path-planning algorithms often struggle to achieve an optimal balance between computational efficiency and risk-avoidance effectiveness when addressing high-frequency dynamic meteorological changes. To address this limitation, this study proposes a Time-Dynamic Theta* (TDM-Theta*) approach. From an algorithmic perspective, this method extends traditional any-angle path planning by introducing a temporal dimension to the search space. For maritime application, it integrates real-time significant wave height as a spatio-temporal dynamic constraint, thereby dynamically evaluating the actual impact of marine meteorology on ship navigability. Simulation tests were conducted through nine experimental cases designed under three typical navigation scenarios: unrestricted waters, complex terrains, and typhoon transits. The results demonstrate that the TDM-Theta* algorithm not only efficiently generates the shortest paths in statically complex terrains but also achieves a 100% proactive risk avoidance rate within the boundaries of the evaluated extreme weather scenarios with multiple concurrent typhoons, incurring negligible computational overhead and low path costs. This research provides robust theoretical and methodological support for real-time safe route decision-making for intelligent ships in complex and volatile environments. Full article

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called ${\theta }^{\ast }$-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle ${\theta }^{\ast }$ global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. Full article

Climate change is reshaping global hydrological cycles, intensifying scarcity and heightening health risks in vulnerable regions. This study examines the health impacts of climate-driven water scarcity across the Middle East, South Asia, and Sub-Saharan Africa using data on water availability, climate variability, and health outcomes. The study uses a multi-regional mixed methods approach that brings together climate, hydrology, governance, and health data to explore how climate-driven water scarcity affects public health in South Asia, Sub-Saharan Africa, and the MENA region. It combines quantitative climate and health indicators with qualitative evaluations of water system vulnerability to compare exposure pathways and health outcomes across regions. Findings show that rising temperatures, altered rainfall, declining groundwater, and recurrent droughts undermine water security, leading to increased disease burdens through four pathways: (1) waterborne illnesses from unsafe or insufficient supplies; (2) reduced hygiene due to limited access; (3) food insecurity from crop failures; and (4) mental health stress, conflict, and displacement from water competition. Women, children, and low-income households face disproportionate impacts. Current adaptation measures are fragmented, highlighting the need for integrated water governance to build climate resilience. Recommended strategies include community-based water safety planning, digital water monitoring, and embedding health metrics in climate–water policies. This cross-regional analysis supports equitable, climate-resilient health systems and informs interventions to mitigate water scarcity under accelerating climate change. This study directly supports global policy agendas by providing evidence that advances the objectives of the Sustainable Development Goals and international frameworks on climate resilience, water security, and food and health protection. Full article

Panoptic segmentation of ships plays a crucial role in intelligent navigation and maritime safety, providing essential references for route planning and collision avoidance. However, the complexity of the maritime environment, including issues such as water surface reflections, weather disturbances, and the challenge of detecting small ship targets, significantly increases the difficulty of the segmentation task. To address these challenges, this paper proposes a novel panoptic ship segmentation framework, FA PEM, based on the PEM algorithm. First, we propose the Dynamic Correlation-Aware Upsampling (DCAU) module, which adopts a content-adaptive sampling point selection and grouping upsampling strategy, significantly improving boundary alignment and fine-grained feature extraction. Second, we propose the Spatial-Frequency Attention Module (SFAM). By modeling both spatial and frequency domain features, this module integrates multi-scale deep convolutions and Fourier transforms, enhancing the model’s ability to perceive both global structures and local textures. Furthermore, to address the lack of an appropriate dataset for ship panoptic segmentation, we construct and annotate a new dataset, the Ship Panoptic Segmentation Dataset (SPSD), consisting of 4360 ship images. Experimental results demonstrate that FA PEM significantly outperforms the baseline FEM on both the Cityscapes and SPSD datasets, achieving advanced performance and exhibiting strong generalization ability. Full article

Climate change is reshaping territorial safety and water-resource management, calling for digital tools that integrate heterogeneous datasets, enable advanced analyses, and enhance decision-making transparency. This article documents the three-year digital transformation (2022–2025) of the Central Apennine River Basin District Authority (AUBAC), covering > 42,000 km² and serving 8.6 million residents in central Italy. Through an incremental methodology across three releases, AUBAC developed an integrated WebGIS consolidating 613 geospatial layers and near-real-time monitoring from 1844 IoT sensors, implementing a Level 1 (Diagnostic) Digital Twin. Measured results include 141,569 platform visits, an approximately 60% reduction in administrative burden, a 70–80% reduction in plan-processing times, over 5000 users participating in public consultations, and a 40–60% increase in perceived risk understanding. The article presents the research design, platform architecture, evaluation framework, challenges encountered, and recommendations for replicability. The platform supports climate adaptation, disaster-risk reduction, and integrated water-resource management, contributing to SDGs 6, 11, and 13. The experience demonstrates that territorial Digital Twins can deliver tangible operational gains within public administration while establishing a foundation for evolution toward predictive capabilities. Full article

This study presents a comprehensive numerical simulation of reservoir dam failure based on the two-dimensional hydrodynamic model MIKE 21. To reproduce the real accident process, a detailed digital elevation model derived from LiDAR survey data was constructed, incorporating valley microtopography, river channel geometry, and hydraulic structure elements. The modeling was performed in a stepwise manner and included the simulation of breach formation using a time-varying digital elevation model, the drawdown of the reservoir, and the propagation of the dam-break flood wave in the downstream reach, as well as an assessment of the hydrodynamic impact of the flow on small dams located further downstream. The simulations produced spatiotemporal distributions of flow depths and velocities, quantified the temporal evolution of reservoir water volume, and determined overflow parameters at the small dams. Based on the analysis of bed shear stress distribution, zones of increased hydrodynamic loading were identified and compared with observed damage areas. The results confirm the applicability of the adopted modeling framework for detailed reconstruction of dam-break events. The proposed approach can be applied both to the analysis of past dam failures and for predictive purposes when assessing the potential consequences of possible accidents at other reservoirs. The methodology enables preliminary evaluation of inundation zones, erosion intensity, and impacts on downstream hydraulic structures, making it a valuable tool for safety assessment and the planning of protective measures in areas with complex terrain conditions. Full article

Hydraulic transients resulting from sudden pump shutdowns or valve closures can induce severe pressure fluctuations, known as water hammer, which compromise the safety and reliability of water distribution systems. Designing effective surge protection devices requires balancing hydraulic performance with economic feasibility, which naturally leads to a multi-objective optimization problem. This study develops an integrated framework that couples Don Wood’s Wave Plan Method for transient flow simulation with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for optimal selection and design of water hammer arrestors. The proposed model simultaneously minimizes total installation cost and a hydraulic penalty function representing deviations in pressure from allowable limits. Decision variables include geometric and operational parameters of different surge protection devices such as air vessels, relief valves, and surge tanks, all constrained by practical hydraulic and physical limits. The resulting Pareto front illustrates the inherent trade-off between cost and reliability, enabling the identification of near-optimal design solutions. This approach provides a comprehensive basis for improving the hydraulic safety of pressurized water systems while maintaining economic efficiency, offering a flexible tool for future optimization and design studies in transient flow management. Full article

We modeled water inrush propagation in mines and the optimization of evacuation routes. By constructing a water flow model, the propagation process of water flow through the tunnel network is simulated to explore branching, superposition, and water level changes. The model was constructed based on breadth-first search (BFS) and a time-stepping algorithm. Furthermore, by integrating Dijkstra’s algorithm with a spatio-temporal expanded graph, miners’ evacuation routes were planned, optimizing travel time and water level risk. In scenarios with multiple water inrush points, we developed a multi-source asynchronous model that enhances route safety and real-time performance, enabling efficient emergency response during mine water disasters. For Problem 1 defined in this study, a graph structure and BFS algorithm were used to calculate the filling time of tunnels at a single water inrush point. For Problem 2, we combined the water propagation model with dynamic evacuation route planning, realizing dynamic escape via a spatio-temporal state network and Dijkstra’s algorithm. For Problem 3, we constructed a multi-source asynchronous water inrush dynamic network model to determine the superposition and propagation of water flows from multiple inrush points. For Problem 4, we established a multi-objective evacuation route optimization model, utilizing a time-expanded graph and a dynamic Dijkstra’s algorithm to integrate travel time and water level risk for personalized evacuation decision-making. Full article