Search Results (1,237)

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

This study systematically investigated the oxidation of chitosan using the TEMPO/NaClO/NaBr catalytic system under varying experimental conditions, namely temperature, reaction time, and pH, in order to optimize the oxidation process. Response surface methodology (RSM) was employed to determine the optimal parameters for maximizing the efficiency of the reaction. The structural modifications to the chitosan following oxidation were confirmed using Fourier-transform infrared spectroscopy (FTIR), alongside additional analytical techniques, which validated the successful introduction of carbonyl and carboxyl functional groups. Solvent-cast films were prepared from both native and oxidized chitosan in order to evaluate their functional performance. The antibacterial activity of these films was assessed against Gram-negative (Salmonella) and Gram-positive (Streptococcus faecalis) bacterial strains. The oxidized chitosan films exhibited significantly enhanced antibacterial effects, particularly at shorter incubation periods. In addition, antioxidant activity was evaluated using DPPH radical scavenging and ferrous ion chelation assays, which both revealed a marked improvement in radical scavenging ability and metal ion binding capacity in oxidized chitosan. These findings confirm that TEMPO-mediated oxidation effectively enhances the physicochemical and bioactive properties of chitosan, highlighting its potential for biomedical and environmental applications. Full article

Classical thermodynamics omits rigidity, which property distinguishes solids from gases and liquids. By accounting for rigidity (i.e., Young’s elastic modulus, ϒ), we recently amended historical formulae and moreover linked heat capacity, thermal expansivity, and ϒ. Further exploration is motivation by the importance of classical thermodynamics to various applied sciences. Based on heat performing work, we show here, theoretically, that density times sublimation enthalpy divided by the molar mass (ρΔH_sub/M, energy per volume), depends linearly on ϒ (1 GPa = 10⁹ J m⁻³). Data on diverse metals, non-metallic elements, chalcogenides, simple oxides, alkali halides, and fluorides with cubic structures validate this relationship at ambient conditions. Furthermore, data on hcp metals and molecular solids show that ρΔH_sub/M is proportional to ϒ for anisotropic materials. Proportionality constants vary only from 0.1 to 0.7 among these different material types (>100 substances), which shows that the elastic energy reservoir of solids is large. Proportionality constants depend on whether molecules or atoms are sublimated and are somewhat affected by structure. We show that ductility of refractory, high-ϒ metals affect high-temperature determinations of their ΔH_sub. Our results provide information on sublimation processes and subsequent gas phase reactions, while showing that elasticity of solids is the key parameter needed to assessing their energetics. Implications are highlighted. Full article

Introduction: Leptospirosis is an under-recognized zoonosis that affects both tropical and temperate regions. While it is often associated with exposure to contaminated water or infected animals, its presentation and epidemiology in Mediterranean countries remain incompletely understood. This retrospective cohort study investigates the clinical and epidemiological profile of leptospirosis in Crete, Greece, a region where data are scarce. Methods: All adult patients with laboratory-confirmed leptospirosis admitted to three major public hospitals in Crete, Greece, between January 2019 and December 2023 were included in the analysis. Diagnosis was made through serologic testing along with compatible clinical symptoms. Results: A total of 17 patients were included. Their median age was 48 years, with a predominance of males (70.6%). Notably, more than half of the patients had no documented exposure to classic risk factors such as rodents or standing water. Clinical presentations were varied but commonly included fever, fatigue, acute kidney injury, and jaundice. Of the patients who underwent imaging, most showed hepatomegaly. The median delay from symptom onset to diagnosis was 11 days, underscoring the diagnostic challenge in non-endemic areas. Ceftriaxone was the most frequently administered antibiotic (76.5%), often in combination with tetracyclines or quinolones. Despite treatment, three patients (17.6%) died, all presenting with severe manifestations such as ARDS, liver failure, or shock. A concerning increase in cases was noted in 2023. Conclusions: Leptospirosis can present with severe and potentially fatal outcomes even in previously healthy individuals and in regions not traditionally considered endemic. The relatively high mortality and disease frequency noted emphasize the importance of maintaining a high index of suspicion. Timely diagnosis and appropriate antimicrobial therapy are essential to improving patient outcomes. Additionally, the need for enhanced public health awareness, diagnostic capacity, and possibly environmental surveillance to control this neglected but impactful disease better, should be emphasized. Full article

The deformation characteristics of the surrounding rock in tunnel groups are considered critical for the design of support structures and the assurance of the long-term safety of deep-buried diversion tunnels. The deformation behavior of surrounding rock in tunnel groups was investigated to guide structural support design. Field tests and numerical simulations were performed to analyze the distribution of ground stress and the ground reaction curve under varying conditions, including rock type, tunnel spacing, and burial depth. A solid unit–structural unit coupled simulation approach was adopted to derive the two-liner support characteristic curve and to examine the propagation behavior of concrete cracks. The influences of surrounding rock strength, reinforcement ratio, and secondary lining thickness on the bearing capacity of the secondary lining were systematically evaluated. The following findings were obtained: (1) The tunnel group effect was found to be negligible when the spacing (D) was ≥65 m and the burial depth was 1600 m. (2) Both P_0.3 and P_max of the secondary lining increased linearly with reinforcement ratio and thickness. (3) For surrounding rock of grade III (IV), 95% u_lim and 90% u_lim were found to be optimal support timings, with secondary lining forces remaining well below the cracking stress during construction. (4) For surrounding rock of grade V in tunnels with a burial depth of 200 m, 90% u_lim is recommended as the initial support timing. Support timings for tunnels with burial depths between 400 m and 800 m are 40 cm, 50 cm, and 60 cm, respectively. Design parameters should be adjusted based on grouting effects and monitoring data. Additional reinforcement is recommended for tunnels with burial depths between 1000 m and 2000 m to improve bearing capacity, with measures to enhance impermeability and reduce external water pressure. These findings contribute to the safe and reliable design of support structures for deep-buried diversion tunnels, providing technical support for design optimization and long-term operation. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

Oxidative stress (OS) is a major concern in young poultry and livestock, prompting extensive research on OS models. This study aimed to systematically investigate the dynamic effects and temporal trends of OS induced with lipopolysaccharide (LPS) over time. Twenty-eight piglets were randomly divided into four groups and equally intraperitoneally injected with LPS at doses of 0 μg/kg (control), 50 μg/kg (L-LPS), 100 μg/kg (M-LPS) and 150 μg/kg (H-LPS) body weight, respectively. The results showed that total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD), and catalase (CAT) were decreased, while malondialdehyde (MDA), nitric oxide (NO), inducible nitric oxide synthase (iNOS), interleukin-6 (IL-6), IL-1β, tumor necrosis factor-α (TNF-α), diamine oxidase (DAO) and D-lactic acid (D-LA) were increased in the M-LPS and H-LPS group on day 1 in comparison with the control group, but no differences were found among treatments on day 7. However, LPS treatments gave rise to varying degrees of pathological injury in the intestines, livers and spleens on day 7. Metabolomics analysis indicated that compared with the control group, glycyl-valine, histamine and lepidine F were decreased in the M-LPS group. Most differentially expressed metabolites were enriched in amino acid-related metabolism pathways on both day 1 and day 7. Microbiome analysis identified that Oscillibacter_sp._CAG:241 was decreased in the M-LPS group compared with the control group on day 1, while Bacteroides_thetaiotaomicron and Lactobacillus_amylovorus were reduced in the M-LPS group on day 7. Collectively, an LPS dose of 100 μg/kg body weight is optimal for inducing acute inflammation in Wuzhishan miniature pigs. These findings highlight the importance of considering both the duration of OS induction and the specific research objectives when establishing OS models. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

This study introduces and validates a comparative experiment-based method for determining the effective thickness of composite glass, including polymeric laminated glass (with polyvinyl butyral (PVB) and SentryGlas^® (SGP) interlayers) and vacuum glazing. This method employs comparative four-point bending tests, defining effective thickness by equating the bending stress of a composite specimen to that of a reference monolithic glass specimen under identical loading and boundary conditions. Specimens with varying configurations (glass thicknesses of 5 mm, 6 mm and 8 mm) were tested using non-destructive four-point bending tests under a multi-stage loading protocol (100 N–1000 N). Strain rosettes measured maximum strains at each loading stage to calculate bending stress. Analysis of the bending stress state revealed that vacuum glazing and SGP laminated glass exhibit superior load-bearing capacity compared to PVB laminated glass. The proposed method successfully determined the effective thickness for both laminated glass and vacuum glazing. Furthermore, results demonstrate that employing a 12 mm monolithic reference glass provides the highest accuracy for effective thickness determination. Theoretical bending stress calculations using the effective thickness derived from the 12 mm reference glass showed less than 10% deviation from experimental values. Conversely, compared to established standards and empirical formulas, the proposed method offers superior accuracy, particularly for vacuum glazing. Additionally, the mechanical properties of the viscoelastic interlayers (PVB and SGP) were investigated through static tensile tests and dynamic thermomechanical analysis (DMA). Distinct tensile behaviors and differing time-dependent shear transfer capacities between the two interlayer materials are found out. Key factors influencing the reliability of the method are also discussed and analyzed. This study provides a universally practical and applicable solution for accurate and effective thickness estimation in composite glass design. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to distribution shifts that a single model cannot easily generalize over. Additionally, conflicts between temporal domain-specific patterns and limited model capacity make it difficult to learn shared parameters that work universally. To address this challenge, we propose an ensemble learning framework that leverages multiple domain-specific models to improve temporal domain generalization for sensor data forecasting. We first segment the original sensor time series into distinct temporal tasks to better handle the distribution shifts inherent in sensor measurements. A meta-learning strategy is then applied to extract shared representations across these tasks. Specifically, during meta-training, a recurrent encoder combined with variational inference captures contextual information for each task, which is used to generate task-specific model parameters. Relationships among tasks are modeled via a self-attention mechanism. For each query, the prediction results are adaptively reweighted based on all previously learned models. At inference, predictions are directly generated through the learned ensemble mechanism without additional tuning. Extensive experiments on public sensor datasets demonstrate that our method significantly enhances the generalization performance in forecasting across unseen sensor segments. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance. Full article