Search Results (3,792)

Accurate prediction of blast-induced air overpressure (AOp) is vital for environmental management and safety in mining and construction. Traditional empirical models are simple but fail to capture complex meteorological effects, while accurate black-box machine learning models lack interpretability, creating a significant dilemma for practical engineering. This study resolves this by applying explainable AI (XAI) to develop a transparent, “white-box” model that explicitly quantifies how meteorological parameters, wind speed, direction, and air temperature influence AOp. Using a dataset from an urban excavation site, the methodology involved comparing a standard USBM empirical model and a Multivariate Non-linear Regression (MNLR) model against a Symbolic Regression (SR) model implemented with the PySR tool. The SR model demonstrated superior performance on an independent test set, achieving an R² of 0.771, outperforming both the USBM (R² = 0.665) and MNLR (R² = 0.698) models, with accuracy rivaling a previous “black-box” neural network. The key innovation is SR’s ability to autonomously generate an explicit, interpretable equation, revealing complex, non-linear relationships between AOp and meteorological factors. This provides a significant engineering contribution: a trustworthy, transparent tool that enables engineers to perform reliable, meteorologically informed risk assessments for safer blasting operations in sensitive environments like urban areas. Full article

Predicting structural loads on offshore wind turbine support structures under varying environmental conditions is a complex yet critical task, particularly for large-capacity turbines such as the 15 MW offshore wind turbine. Current prediction methods often struggle with accuracy, especially for torsional moments, due to the non-linear interactions between wind parameters and structural responses. To address this challenge, present study develops a generalized load estimation framework using multivariable polynomial regression, leveraging 10,000 numerical simulations. The framework accounts for four critical variables: Extreme Wind Speed (30 to 40 m/s), Turbulence Intensity (12% to 16%), Flow Inclination Angle (−8° to +8°), and Shear Exponent (0.1 to 0.3). The proposed equations predict six key moment components at the tower base, including the bending moments about the y-axis, torsional moments about the z-axis, bending moments in the x-y, x-z, and y-z planes, and the resultant combined moment. The framework was validated using 2000 testing data points, achieving high accuracy with R² values exceeding 0.92 for all moments. Specifically, the prediction accuracy was highest for the resultant combined moment and y-z bending moment, with average absolute errors of 5.76% and 5.97%, respectively, while x-z bending moment had a slightly higher error of 13.91%, highlighting that torsional moments are inherently more challenging to predict. Heatmap and scatter plot analyses confirmed that the predicted moments align closely with the simulated values, particularly for the torsional moment about the z-axis and y-z bending moment, with standard deviation values as low as 4.85. By optimizing polynomial degrees between 2 and 4, the framework effectively balances prediction accuracy and computational efficiency. This approach provides engineers and scientists with a reliable tool for load estimation, facilitating improved design and analysis of offshore wind turbine support structures. Full article

Wildfires pose a growing threat to ecosystems, infrastructure, and public safety, particularly in the province of British Columbia (BC), Canada. In recent years, the frequency, severity, and scale of wildfires in BC have increased significantly, largely due to climate change, human activity, and changing land use patterns. This study presents a comprehensive, data-driven approach to wildfire prediction, leveraging advanced machine learning (ML) and deep learning (DL) techniques. A high-resolution dataset was constructed by integrating five years of wildfire incident records from the Canadian Wildland Fire Information System (CWFIS) with ERA5 reanalysis climate data. The final dataset comprises more than 3.6 million spatiotemporal records and 148 environmental, meteorological, and geospatial features. Six feature selection techniques were evaluated, and five predictive models—Random Forest, XGBoost, LightGBM, CatBoost, and an RNN + LSTM—were trained and compared. The CatBoost model achieved the highest predictive performance with an accuracy of 93.4%, F1-score of 92.1%, and ROC-AUC of 0.94, while Random Forest achieved an accuracy of 92.6%. The study identifies key environmental variables, including surface temperature, humidity, wind speed, and soil moisture, as the most influential predictors of wildfire occurrence. These findings highlight the potential of data-driven AI frameworks to support early warning systems and enhance operational wildfire management in British Columbia. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Bottlenose dolphins (Tursiops sp.) are opportunistic foragers with global distributions that utilize diverse feeding tactics based on environmental factors, habitat features, prey behavior, group dynamics, and genetics. We describe a unique foraging tactic regularly observed in the confluence of dredged shipping channels with high anthropogenic disturbance, and explore potential abiotic (temporal, tidal, habitat) drivers of the behavior. A shore-based digital theodolite was used from 2021 to 2022 to observe common bottlenose dolphins (T. truncatus) foraging within a current in a technique we term Orient-Against-Current (OAC). During OAC, dolphins position themselves facing into the flow of a current, swimming at a speed to maintain a stationary position within the current, and feed while prey move with the current towards them. Orient-Against-Current occurred in all seasons and throughout daylight hours, particularly during the winter and spring. Dolphins engaged in OAC during ebb tides and intermediate current speeds (1–2 knots), but not during slack tides. As OAC occurred closer to shoreline structures (i.e., seawalls, concrete blocks) than to mangroves and natural seagrass beds, it appears that hard human-engineered structures aid in prey capture during OAC. Knowledge of dolphin foraging techniques can aid in understanding behavioral plasticity shaped by anthropogenically altered environments in industrialized coastal areas. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

China’s rapid economic growth since its reform and opening-up has come at the cost of worsening atmospheric pollution. This study investigates the spatiotemporal evolution and driving mechanisms of PM_2.5 concentrations in Shandong province, a key industrial region, during 2014–2023, using comprehensive air quality monitoring, meteorological observations, and socioeconomic datasets. Through spatial analysis and geodetector methods, we identify that (1) The annual PM_2.5 concentration decreases significantly by 50.9%; spatially, heterogeneity is observed with the western urban agglomeration experiencing more severe pollution, while the eastern coastal urban agglomeration exhibits better air quality. (2) Gravity model analysis shows that the centroids of PM_2.5 pollution undergo distinct migration phases. (3) PM_2.5 levels show a distinct seasonal pattern, peaking in winter at a level 143.7% higher than the summer average. (4) The meteorological driving factors are primarily air temperature (r = 0.511) and wind speed (r = −0.487), while the socioeconomic factors are tertiary industry production (r = −0.971), particulate matter emissions (r = 0.956), and sulfur dioxide emissions (r = 0.938). Concurrently, the combined effect of tertiary industry production and PM emissions account for 99.5% of PM_2.5 variability. Notably, we validate an Environmental Kuznets Curve relationship (R² = 0.805) between economic development and air quality improvement, demonstrating that clean production policy integration can reconcile environmental and economic objectives. These findings provide empirical evidence supporting circular economy strategies for air pollution mitigation in industrializing regions. Full article

Polyvinyl chloride (PVC) is a widely used polymer in composite systems due to its versatility and processability, with growing use in advanced engineering applications. This study presents the formulation, processing optimisation, and detailed characterisation of a hot-dip PVC-based plastisol composite coating developed for electrical insulation in electric vehicle (EV) battery systems. A series of plastisol formulations with varying filler contents were prepared and applied via dip-coating at withdrawal speeds of 5, 10, and 15 mm s⁻¹. The 5 mm s⁻¹ withdrawal speed resulted in the most uniform coatings with thicknesses of 890–2100 µm. Mechanical testing showed that lower filler content significantly improved performance: Group 1 (lowest filler) exhibited the highest tensile strength (11.9 N mm⁻²), elongation at break (465%), tear strength (92 N mm⁻¹), and abrasion resistance. SEM and EDX analyses confirmed more homogeneous filler dispersion in Group 1, while FTIR spectra indicated stronger polymer–plasticiser interactions. Contact-angle measurements showed an increase of ${38}^{\circ }$ in low-filler samples, indicating enhanced surface hydrophobicity. Furthermore, Group 1 coatings demonstrated superior dielectric strength (22.1 kV mm⁻¹) and excellent corrosion resistance, maintaining integrity for over 2000 h in salt-spray testing. These findings highlight the importance of filler optimisation in balancing mechanical, electrical, and environmental performance. The proposed PVC-based composite coating offers a durable, cost-effective solution for next-generation EV battery insulation systems and has potential applicability in other high-performance engineering applications. Full article

With the advancement of industrial technology toward high speed, heavy load, precision, and automation, traditional sliding bearing materials have been unable to meet modern industrial demands. Surface coating technology, as an efficient surface modification method, has become a key means to enhance the tribological properties, wear resistance, corrosion resistance, and fatigue resistance of sliding bearings, thus extending their service life. This paper systematically reviews the research progress of coating technology for sliding bearings in the past, aiming to fill the gap in comprehensive summaries of multi-material systems and multi-process technologies in existing reviews. In terms of materials, it focuses on the performance characteristics and application scenarios of three major coating types—metal-based, ceramic-based, and polymer-based—clarifying their advantages and limitations. In terms of processes, it analyzes the technical characteristics of mainstream methods including electroplating, magnetron sputtering, and laser cladding, as well as their innovative applications in replacing traditional processes. Furthermore, this review summarizes the latest research results in coating performance evaluation, such as tribological testing via pin-on-disk testers and corrosion resistance analysis via salt spray tests. Finally, it discusses future development trends in new materials, new process applications, and environmental sustainability. This work is expected to provide a valuable reference for related research and engineering applications in the field of sliding bearing coatings. Full article

The Domain Name System (DNS) is a fundamental component of the Internet, yet its distributed and caching nature makes it susceptible to various attacks, especially cache poisoning. Although the use of random port numbers and transaction IDs has reduced the probability of cache poisoning, recent developments such as DNS Forwarder fragmentation and side-channel attacks have increased the possibility of cache poisoning. To counteract these emerging cache poisoning techniques, this paper proposes the DNS Cache Sensor (DNS-Sensor) system, which operates as a distributed sensor network for DNS security. Like environmental sensors monitoring physical parameters, DNS-Sensor continuously scans DNS cache records, comparing them with authoritative data to detect anomalies with sensor-grade precision. It involves checking whether the DNS cache is consistent with authoritative query results by continuous observation to determine whether cache poisoning has occurred. In the event of cache poisoning, the system switches to a disaster recovery resolution system. To expedite comparison and DNS query speeds and isolate the impact of cache poisoning on the disaster recovery resolution system, this paper uses a local top-level domain authoritative mirror query system. Experimental results demonstrate the accuracy of the DNS-Sensor system in detecting cache poisoning, while the local authoritative mirror query system significantly improves the efficiency of DNS-Sensor. Compared to traditional DNS, the integrated DNS query and DNS-Sensor method and local top-level domain authoritative mirror query system is faster, thus improving DNS performance and security. Full article

Accurate vessel track association is a key requirement for maritime traffic monitoring and collision-avoidance systems, yet the Automatic Identification System (AIS) records commonly contain noise, missing intervals, and overlapping trajectories in congested coastal waters. We propose a Dilated Residual Connection Temporal Convolutional Network (DRC-TCN) tailored to AIS sequences; residual dilated blocks with layer normalization enable stable training while capturing long-range temporal dependencies under imperfect data. Beyond kinematic inputs, we augment AIS with buoy-based meteorological variables (wind direction and speed, gust, pressure, air temperature, and sea surface temperature) via time-aligned nearest-station fusion, allowing the model to account for environmental effects on vessel motion. Experiments on New York coastal AIS data show that DRC-TCN outperforms CNN-LSTM and vanilla TCN baselines, improving F1 score by up to 99.3% and achieving 99.7% accuracy. The results indicate that environment-aware temporal modeling strengthens the robustness of track association and supports situational awareness for next-generation intelligent navigation and ocean engineering applications. Full article

Railway braking efficiency hinges on the thermomechanical conditions at the wheel-rail interface. Frictional heating during operation causes significant temperature fluctuations, directly impacting braking performance in rail vehicles. Evaluating these effects is important for developing infrastructure and components adapted to environmental conditions. Several studies have explored the influence of temperature on components such as the brake disc or the wheel; little attention has been paid to the thermal conditions of the rail itself. This paper examines the effect of rail temperature on the braking behavior and energy consumption of a railway vehicle. Using a 1:20 railway track, rail segments were subjected to four temperatures (28.5 °C, 40.0 °C, 49.9 °C, 71.0 °C) by heating with Nichrome wire, and tests were performed at three speeds (0.75, 1.00, and 1.30 m/s). The results show that higher rail temperatures improve wheel-rail adhesion up to an optimum point (40.0 °C), beyond which performance deteriorates. In contrast, tests at 71.0 °C showed reduced braking efficiency, despite lower electrical current peaks, indicating a non-linear thermal response. Full article

The usage of Artificial Intelligence (AI) in control loops and rule-based frameworks is a novel approach in automation and decision-making processes. Large Language Models (LLMs) are redefining conventional rule-based systems by introducing intuitive natural language interfaces, drastically changing the creation of rules, and minimizing operational complexity. Unlike static controllers, AI-enhanced systems can autonomously evolve with real-time environmental changes, achieving optimal performance without manual intervention. By allowing non-experts to modify rules through natural language commands, LLM can change the control system management. These advancements not only improve adaptability and operational efficiency but also reduce downtime through proactive error detection and self-correction mechanisms. AI-powered systems allow refining operations, thus accelerating response speeds and increasing reliability. The synergy between rule-based logic and AI-driven intelligence provides a new approach for autonomous systems, improving their capability of context-specific decision-making. In this paper, an approach is presented to control a storage system by natural language commands. The comparison of the Hungarian and English language interpretations is discussed. Full article

Urban logistics in smart cities are increasingly challenged by congestion, sustainability pressures, and the growing demand for resilient delivery systems. To address these challenges, this study introduces the Multi-Visit Time-Dependent Truck–Drone Routing Problem with simultaneous Pickup and Delivery (MTTRP-PD), a novel framework that integrates three realistic features: (i) drones serving multiple customers per sortie, (ii) time-dependent truck speeds reflecting dynamic traffic conditions, and (iii) synchronized pickup and delivery between trucks and drones. By incorporating these elements, the proposed model provides a more realistic and comprehensive representation of urban air-ground collaborative logistics in the last mile. An optimization framework and an efficient solution approach are developed and validated through computational experiments. The results demonstrate that enabling multi-visit sortie and simultaneous pickup–delivery operations can significantly reduce logistics costs compared with conventional single-visit or delivery-only strategies. Sensitivity analyses further reveal the critical influence of dynamic traffic conditions on fleet configuration and operational decision making. The findings offer actionable insights for logistics operators and policymakers, illustrating how coordinated UAV–truck collaboration can enhance efficiency, resilience, and environmental sustainability in next-generation urban logistics systems. Full article

The increasing prominence of blade icing in wind power generation within cold regions has positioned anti-icing coating technology as a key research focus. This study synthesised phase-change microcapsules using bio-wax as the core material and isophorone diisocyanate as the shell material via interfacial polymerisation. These microcapsules were then compounded with polyurethane to form an anti-icing coating, whose properties and anti-icing performance were systematically investigated. Key findings indicate that a 1% emulsifier concentration yielded microcapsules with a concentrated particle size distribution (≈20 μm). Microcapsules with a core-to-shell ratio of 7:3 exhibited optimal thermal storage performance, characterised by a melting enthalpy of 49.73 J/g and an encapsulation efficiency of 78%, establishing this as the optimal formulation. Icing wind tunnel tests demonstrated enhanced anti-icing efficacy with increasing microcapsule concentration. At 36% concentration, the coating achieved an anti-icing efficiency of 65.80% under conditions of −15 °C and 3 m/s wind speed, and 64.05% at −10 °C and 6 m/s. The coating maintained its effectiveness under high wind speeds, though its performance diminished with increased water spray flux. The coating functioned by delaying ice formation through phase-change heat release. It consistently demonstrated an anti-icing efficiency exceeding 60% across operational conditions −15 °C to −5 °C and wind speeds of 3–9 m/s. This work provides an efficient and environmentally friendly anti-icing solution for wind turbine blades in cold regions. Full article