Search Results (235,545)

Urban point source pollution has been effectively controlled in recent years; however, rainfall-driven non-point source (NPS) pollution has become a major contributor to the deterioration of urban water environments. This study focuses on the plain river network region of Wuxi City in the Taihu Basin, China. By integrating field monitoring with coupled model simulations, this study upscaled results from the experimental plot to the urban-scale river network, enabling analysis of the full processes of pollutant inflow and transport and evaluation of the role of rainfall in regulating these dynamics. Field monitoring in the experimental plot demonstrated a strong correlation between the temporal dynamics of NPS pollutant inflows and rainfall characteristics. Further analysis using model simulations in the river network area revealed that rainfall, maximum 1 h rainfall, and rainfall duration were identified as the primary drivers of pollutant inflows, while early drought duration, rainfall intensity, and variance between rainfall per unit time exerted non-linear effects. Specifically, when early drought duration was approximately 6–7 days or when rainfall intensity ranged from 2.1 to 2.6 mm/h, riverine nitrogen (N) and phosphorus (P) concentrations and pollutant loadings reached their peaks. In addition, when the deviation of unit-time rainfall from the event mean was between 1.8 and 2 mm, the duration of increase in pollutants entering the river was the longest. This study provides quantitative evidence highlighting the influence of rainfall characteristics on nitrogen and phosphorus dynamics in plain river network urban rivers. The findings offer valuable insights into the remediation of urban black-odor water bodies. Full article

The integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS) has been widely adopted in railway positioning applications. However, conventional filtering-based approaches are fundamentally constrained by their dependence on instantaneous state estimates while failing to exploit valuable historical measurement information. To overcome this limitation, we develop a factor graph optimization (FGO) framework to enhance data utilization efficiency. During GNSS signal outages, existing implementations typically preserve only SINS factors while excluding GNSS observations, leading to unbounded error growth. To bridge this gap, our novel solution integrates a gated recurrent unit (GRU) with an Improved Bidirectional Long Short-Term Memory (IBiLSTM) network to generate accurate pseudo-GNSS observations through effective learning from both preceding and subsequent GNSS data sequences. Comprehensive evaluation under GNSS-denied conditions demonstrates that our approach achieves significant improvements over conventional neural network-aided methods, with horizontal root mean square error (RMSE) reductions of 49.22% (simulation) and 36.24% (onboard vehicle). Subsequent FGO processing yields additional performance gains, further reducing RMSE by 46.67% (simulation) and 35.31% (onboard vehicle). This innovative methodology effectively maintains positioning accuracy and ensures navigation continuity during GNSS outages, thereby offering a robust solution for train positioning systems in challenging environments. Full article

A dam breach is an uncommon but profoundly destructive event that transpires when a dam collapses, releasing accumulated water downstream and leading to extensive damage. This study focuses on the Jure landslide dam, located in the Sindhupalchowk district, Nepal. The region is characterized by complex river channels and steep terrains, which are significantly influenced by flood dynamics. This study aims to establish a compressive numerical simulation of a two-dimensional dam breach unsteady flow hydraulic model to simulate the dam breach process and downstream flood propagation. The study analyzes the dynamics of the Jure landslide dam outburst flood, emphasizing the flood characteristics, inundation, and velocity hazards in the mitigation of flood impacts. The results reveal that the peak discharge of the Jure landside dam was 5336.7 m³/s, while it decreased to 1181.4 m³/s when traveling 35 km. The flood depth obtained by 2D (HEC-RAS) downstream of the dam rages between 0.0334 and 55.9 m, while the corresponding estimated peak flow velocity of simulated breaches was 21.46 m/s, demonstrating extreme hydraulic force conditions, capable of catastrophe. The proposed hydraulic simulations reveal significant variations in overflow dynamics across different terrain types, with narrower sections exhibiting faster flood progression and greater water depths. The findings underscore the necessity of accounting for terrain heterogeneity in future flood risk assessments. This work offers valuable insights into the emergency management of landslide dams in similar regions. Full article

This paper presents an adaptive virtual oscillator control in coordination with an adaptive filter to mitigate subsynchronous oscillations in grid-forming converters caused by series compensation. Although series compensation enhances power transfer capability and transient stability margins, it can introduce subsynchronous resonance, leading to subsynchronous oscillations. Virtual oscillator control fed with set points is made dispatchable for grid-forming control to ensure the power-sharing, fast-synchronization, and subsynchronous oscillation damping capability of inverters. In this paper, taking advantage of power reserves in grid-forming operation, virtual oscillator control law is modified to dynamically change the set power point during low-resonance conditions to mitigate subsynchronous oscillations. Moreover, to overcome the limited damping capability of adaptive VOC during severe-resonance conditions, a coordinated adaptive adjustment of the grid-side filter inductance based on the modified power set point is designed. The IEEE’s first benchmark model is altered by integration with a 1000 MW grid-forming inverter in a MATLAB R2024b/Simulink environment. The previously proposed dispatchable virtual oscillator control and electronic-based FACT device, i.e., thyristor-controlled series capacitor, are implemented and analyzed under the same test system for the sake of comparison with the designed coordinated strategy. The simulation results are investigated in the time domain and frequency domain, and by calculating performance indices to verify the effectiveness of the proposed scheme. The overall analysis justifies the mitigated, low transient overshoot and high power quality of subsynchronous oscillations by using the designed strategy with varying compensation levels. Full article

Bio-inspired and bio-based materials are increasingly recognized as powerful enablers of climate-responsive and low-carbon architecture. By learning from natural systems, such as adaptability, self-regulation, and resource efficiency, these materials offer promising solutions to the escalating environmental pressures faced by the built environment. However, their systematic integration into building design remains limited, particularly in tropical monsoon climates. To address this gap, this study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify, prioritize, and map the interdependencies among ten bio-based and bio-inspired strategies for sustainable building design. The results highlight five dominant solutions: living building systems, bio-composite exterior cladding for weather resistance, mycelium-based insulation for humidity control, bio-based natural ventilation and passive cooling, and bio-inspired self-shading systems. The causal analysis reveals three key characteristics: (1) living building systems function as a central integrative nexus, (2) bio-composite cladding acts as a primary driver of durability and climate resilience, and (3) bio-based water filtration and local timber exhibit lower systemic leverage despite their environmental benefits. Theoretically, this study advances biomimetic design research by introducing a causal, system-level framework for understanding interactions among nature-inspired strategies. Practically, it provides architects, engineers, and policymakers with an evidence-based decision-support tool to prioritize climate-adapted, bio-inspired solutions, contributing to the development of resilient and regenerative architecture in rapidly changing climates. Full article

The outcomes of the Winter Storm URI in Houston (February 2021) and its impact on awnings highlighted how climate change has altered the load combinations considered in design codes such as ASCE 7-16, introducing new uncertainties due to freezing storm events. Previously unused load categories are now presenting significant challenges, as designers assumed sufficient safety factors would prevent failures. This research investigates the consequences of the storm and offers guidelines for conservative awning design in zero ground snow load zones, emphasizing wind load as the primary design load in regions with no active snow zone. Additionally, an attempt has been made in this research to examine the importance of anchor reliability in concrete structures, particularly under environmental stress such as winter storms. Factors like improper installation, edge distance, and embedment depth significantly affect anchor performance, potentially leading to premature failure modes like concrete breakout, pullout, or rusting from water accumulation. Through field investigations and theoretical analyses, the research evaluates the axial load capacity of anchors, taking into account edge distance, embedment depth, and environmental factors like ice accumulation. The study stresses the need for proper anchor geometry, drainage, and reinforcement to ensure structural safety. By following the proposed recommendations, engineers can mitigate adverse effects and enhance the durability and safety of concrete structures, even under extreme weather conditions. Full article

This study investigates droplet-induced corrosion, a localized corrosion phenomenon driven by oxygen depletion within electrolyte droplets, distinct from bulk volume corrosion. To evaluate the performance of corrosion inhibitors under droplet conditions, a rapid screening electrochemical test method was employed, using a two-electrode setup to monitor corrosion currents. The study examined systematically different exposure environments including dissolved oxygen, pH, electrolyte molarity, and droplet geometry as key factors influencing atmospheric corrosion. Results show that dissolved oxygen levels significantly affect corrosion mechanisms, while larger droplets amplify the Evans droplet effect. Importantly, effective corrosion inhibitors mitigate this effect by reducing the cathodic reaction rate in droplet conditions. These findings advance the understanding of droplet corrosion mechanisms and provide insights into designing sustainable protection strategies to improve the longevity of steel structures in aggressive environments. Full article

Achieving cooperative perception and decision-making among connected and autonomous vehicles (CAVs) in mixed-traffic ramp merge scenarios is crucial for building a swarm intelligence-based traffic control system. However, existing cooperative decision-making methods struggle to adequately model and represent the dynamic collaborative interactions among heterogeneous agents in mixed-traffic environments, which can lead to traffic congestion or even severe accidents in ramp merging areas. Therefore, this paper proposes an Enhanced Graph Reinforcement Learning algorithm based on a Vehicle Collaboration Graph (VCG-EGRL) to enable cooperative merging decisions for CAVs in mixed-traffic ramp merging scenarios. First, a vehicle collaboration intensity (VCI) model is designed to effectively model the intensity of collaborative interactions among vehicles. Then, based on the VCI model, the perception–communication relationships between vehicles and the vehicle-to-infrastructure (V2I) communication relationships are jointly constructed to form a local–global cooperative graph, which represents the dynamic collaborative relationships of the vehicle network from macro and micro perspectives and deeply explores the driving behavior of vehicles. Subsequently, a Graph Convolutional Network enhanced with Kolmogorov–Arnold Networks (KANs), referred to as GKAN, is employed to extract and aggregate the driving features of vehicles from the local–global graph. Finally, a graph mutual information maximization method is used to optimize the iterative process of the Graph Reinforcement Learning strategy, ensuring the generation of accurate lane-changing decisions for CAVs. Experimental results in ramp merging scenarios under varying traffic conditions demonstrate that the proposed method outperforms baseline models in terms of merging success rate, efficiency, and robustness. Full article

This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main activity classes (sitting, standing, and walking), and (2) a real-world dataset captured in an unconstrained office setting captured from 13 subjects carrying out their daily office work for six hours continuously. Three machine learning models—namely, k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF)—were trained on the model development dataset to classify the three main classes independently of sub-activity variation. The KNN, SVM, and RF models achieved accuracies of 90.94%, 92.33%, and 93.02%, respectively, on the development dataset. When deployed on the real-world dataset, the models attained mean accuracies of 69.32%, 79.43%, and 77.81%, reflecting performance degradations between 21.62% and 12.90%. Analysis of sequential predictions revealed frequent short-duration misclassifications, predominantly between sitting and standing, resulting in unstable model outputs. The findings highlight key challenges in transitioning HAR models from controlled to real-world contexts and point to future research directions involving temporal deep learning architectures or post-processing methods to enhance prediction consistency. Full article

The interruption of primary conservation procedures during food handling and preparation represents a critical operational phase for food microbiological safety, especially in environments characterized by repeated manipulation and continuous human presence. This study investigates the application of visible blue–violet light irradiation as a non-thermal process to mitigate microbial proliferation during post-processing handling of raw meat. Raw beef hamburgers, selected as the food model substrate, were subjected to irradiation using a blue–violet LED system operating in the 405–420 nm range and compared with non-irradiated controls under ambient and refrigerated conditions representative of real handling scenarios. Microbiological dynamics were evaluated through time-resolved enumeration of total aerobic mesophilic bacteria and Enterobacteriaceae, while concurrent measurements of moisture loss, texture, and color were performed to assess process-related effects on macroscopic product quality. Visible-light irradiation significantly reduced the rate of microbial growth during handling, with irradiated samples consistently exhibiting lower microbial loads than controls, particularly under ambient conditions (e.g., twofold after 24 h). Under refrigeration, irradiation contributed to stabilizing microbial levels over time, indicating a synergistic effect with low-temperature storage. From a process perspective, irradiation induced moderate and progressive changes in physicochemical attributes, primarily associated with surface dehydration and color variation, without abrupt quality degradation. These results demonstrate that visible blue–violet light irradiation can be integrated as a continuous, non-UV intervention to enhance the microbiological safety of raw meat during post-processing handling, supporting its potential role as an environmental control strategy in food-handling systems. Full article

The valorization of fruit by-products represents a sustainable strategy for developing functional foods. This study evaluated the incorporation of orange peel into kefir-based cream cheese as a value-added ingredient. Dried and ground peel was added at 1% and 5% (w/w), in non-fermented and brine-fermented forms, and its effects on physicochemical, microbiological, and functional properties were assessed. Orange peel enhanced texture and imparted a yellow–orange hue, slightly lowered pH, and increased titratable acidity, indicating improved acidification. Total phenolic content (50–300 mg GAE/100 g dw) and antioxidant activity (40–140 µmol TE/g dw) were markedly enhanced, supporting the potential of citrus by-products in functional dairy formulations. Full article

Even though wheat’s response to nitrogen (N) is well studied, practical optimization remains challenging because yield and seed quality often react inconsistently across seasons. For winter wheat, the simultaneous quantification of efficiency indicators that capture N losses and diminishing returns is important. This study evaluated nitrogen (N) fertilization in two growing seasons. This study aimed to adjust N fertilization strategy through different combinations of granular N timing and foliar applications to improve winter wheat yield and technological seed quality while maintaining high fertilization efficiency. Field experiments were conducted over two growing seasons (2021/2022 and 2022/2023) using seven fertilization treatments (Control, TSE_1, TSE_2, TSEH_1, TSEH_2, TSEH_3, and TSH, which correspond to growth stage T—tillering stage; SE—stem elongation phase; H—heading stage) in the range of 140.5 to 194.5 kg ha⁻¹ N. Seed yield and seed quality traits (moisture, hectoliter weight, starch, protein, gluten, and sedimentation value) were measured, and treatment effects were evaluated with ANOVA, correlation, and regression analyses. In 2021/2022, no significant treatment effects were detected for yield or seed quality parameters, indicating that environmental variability dominated crop response. In contrast, in 2022/2023, seed yield, hectoliter weight, gluten content, and starch yield showed significant treatment effects (p ≤ 0.05–0.01), with fertilized variants generally outperforming the Control. Across both seasons, seed quality traits displayed strong internal structure: protein, gluten, and sedimentation were strongly positively correlated, while starch was strongly negatively correlated with these traits and the yield correlated positively with hectoliter weight but negatively with protein and gluten, highlighting a yield–quality trade-off. Importantly, partial factor productivity (PFP) and nitrogen use efficiency (NUE) showed the strongest treatment sensitivity, demonstrating their value for identifying efficient N strategies even when yield and quality responses were season-dependent. Regression analyses confirmed that seasonal conditions modulated nitrogen responsiveness, with NUE and starch yield showing much stronger relationships with nitrogen input in 2021/2022 and 2022/2023, respectively. Full article

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks. Full article

Low-Speed near-space aerostats (e.g., stratospheric airships and high-altitude balloons) are low-speed unmanned aerial vehicles (UAVs) extensively utilized in communication coverage, remote sensing applications, environmental monitoring, aviation support, and other fields. A paramount challenge constraining their precise and stable operation is the leakage of buoyant gas, such as helium (He), in the harsh and unpredictable near-space environment. One of the primary causes of gas leakage is the degradation of their dedicated sealing rings. This study aims to clarify the aging mechanisms of high-performance rubber seals in near-space environments and establish a reliable service life prediction model to address the gas leakage risk of unmanned platforms. Two widely used high-performance rubber materials—ethylene propylene diene monomer (EPDM) and chloroprene rubber (CR)—were subjected to accelerated aging experiments under simulated near-space environment conditions. Their degradation was then quantified through performance degradation characterization, covering mass loss, hardness, elastic deformation, and tensile strength. A predictive model was established to estimate the mass loss rates and service life of the seals. The model revealed that EPDM exhibits superior performance to CR under near-space conditions: the aging behavior is strongly dependent on material composition, thickness, and preload, while being independent of outer diameter. Results show EPDM seals have a near-space service life of 300 days (50% longer than CR’s 200 days), with aging dependent on material composition, thickness (2 mm seals degrade 110% slower than 0.5 mm ones), and preload, but independent of outer diameter. These results provide actionable design guidelines for optimizing seal materials and geometries in aerostat pressure systems, thereby advancing the development of innovative low-speed UAV technologies and the successful application of these technologies in the emerging near-space field. These findings and the proposed methodology are directly applicable to sealing system optimization for various near-space unmanned platforms (e.g., stratospheric UAVs, high-altitude autonomous balloons), enhancing their long-duration operational reliability and mission success rate in extreme environments. Full article

Although the organic fraction of PM_2.5 has been extensively studied, there is a considerable gap in understanding the organic fraction of coarse particles with diameters between 2.5 and 10 µm. We investigate the composition of coarse organic aerosol (OA) across rural, suburban, and urban areas of Switzerland. Using Aerosol Mass Spectrometer analyses of water-soluble OA extracted from collected filter samples (one entire year, 441 samples per size fraction), we identified two distinct classes of coarse OA. The first class, which constitutes 41–81% of coarse organic carbon (OC), is associated with primary biological organic carbon (PBOC). PBOC is characterized by specific marker ions (e.g., C₂H₅O₂⁺) and exhibits pronounced seasonal variation, with peak concentrations observed in the summer. This seasonal trend correlates with that of molecular markers such as arabitol and mannitol, as well as the fraction of biological particles determined by automated scanning electron microscopy coupled to energy dispersive X-ray spectroscopy of individual particles. The second class, contributing 7.9–17.8% to OC_coarse, is denoted as sulfur-containing organic carbon (SCOC) due to the presence of sulfur-containing ions such as CH₃SO₂⁺. Elevated concentrations of SCOC in urban environments near roadways suggest a strong influence from non-exhaust traffic emissions and resuspended dust. While the overall variation in coarse OC between rural and urban areas is approximately 10%, PBOC concentrations are 1.4 times higher in rural areas, whereas SCOC concentrations are 1.5 times higher in urban settings. Overall, our study shows that although OC_coarse concentrations in Switzerland are relatively consistent across site types, major water-soluble sources, particle properties and composition vary considerably geographically and seasonally. Full article