Search Results (56,242)

In the context of increasingly stringent vehicle emission regulations, computer-aided engineering has been indispensable for optimizing the design and the operational strategies of emission control systems. This paper proposes a new filtration model for particulate filters that enables the accurate estimation of solid particle number emissions above 10 and 23 nm in diameter (SPN10 and SPN23, respectively). The model incorporates a persistent slip factor and a linear filtration efficiency of cake layers into the unit collector model proposed by Konstandopoulos and Johnson. This enhancement captures PM escape phenomena, such as a passage through interconnected large pores in filter walls. Simulations using a 1D + 1D two-channel framework with the proposed model successfully reproduced experimental results of SPN10 and SPN23 emissions downstream of a miniature gasoline particulate filter (GPF) tested with a synthetic particle generator. The model was also able to represent the observed continuous emissions during a cake filtration mode. Additional simulations using the same model parameters showed good agreement with experimental data of SPN10 and SPN23 emissions downstream of a full-size GPF tested with a gasoline direct injection (G-DI) engine under 5 steady-state operating conditions. The simulations revealed that particles in the 10–100 nm size range dominated the downstream SPN emissions despite their high filtration efficiency, whereas particles in the 100–200 nm size range were less significant. The proposed model is expected to contribute to the GPF developments to comply with the stringent emission regulations of the upcoming Euro 7. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

This paper introduces a lightweight cryptographic hash algorithm, LWH-128, developed using a sponge-based construction and specifically adapted for operation under constrained computational and energy conditions typical of embedded systems and Internet of Things devices. The algorithm employs a two-layer processing structure based on simple logical operations (XOR, cyclic shifts, and S-boxes) and incorporates a preliminary diffusion transformation function G, along with the Davis–Meyer compression scheme, to enhance irreversibility and improve cryptographic robustness. A comparative analysis of hardware implementation demonstrates that LWH-128 exhibits balanced characteristics in terms of circuit complexity, memory usage, and processing speed, making it competitive with existing lightweight hash algorithms. As part of the cryptanalytic evaluation, a Boolean SATisfiability (SAT) Problem-based model of the compression function is constructed in the form of a conjunctive normal form of Boolean variables. Experimental results using the Parkissat SAT solver show an exponential increase in computational time as the number of unknown input bits increased. These findings support the conclusion that the LWH-128 algorithm exhibits strong resistance to preimage attacks based on SAT-solving techniques. Full article

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

The substation grounding grid, as the primary path for fault current dissipation, is crucial for ensuring the safe operation of the power system and requires regular inspection. The pulsed eddy current method, known for its non-destructive and efficient features, is widely used in grounding grid detection. However, during the parameter identification process, it is prone to local minima or no solution. To address this issue, this paper first develops a pulsed eddy current forward response model for the substation grounding grid based on the magnetic dipole superposition principle, with accuracy validation. Then, a variable dimensional Bayesian parameter identification method is introduced, utilizing the Reversible-Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using nonlinear optimization results as the initial model and introducing a dual-factor control strategy to dynamically adjust the sampling step size, the model enhances coverage of high-probability regions, enabling effective estimation of grounding grid parameter uncertainties. Finally, the proposed method is validated by comparing the forward response model with field test results, showing that the error is within 10%, demonstrating both the accuracy and practical applicability of the proposed parameter identification method. Full article

Abandoned coal mine lands, particularly waste coal piles that predate the Surface Mining Control and Reclamation Act (SMCRA) of 1977, pose significant environmental and safety risks. Unlike sites mined after SMCRA—where operators are legally mandated to conduct reclamation—there is no legal obligation for companies or individuals to restore lands disturbed before the law’s enactment. As a result, these historical sites remain largely unmanaged and understudied. This study develops a satellite imagery-based analytical workflow to identify and monitor such historical waste coal piles. Using supervised classification of Sentinel-2 imagery with four machine learning models, we identified waste coal piles in both active mining areas and regions disturbed prior to SMCRA. Among the models tested, Random Forest achieved the highest accuracy for classifying waste coal, with a precision of 86% and a recall of 77%. A subsequent time-series analysis revealed that historical waste coal piles have undergone gradual but consistent vegetation recovery since 1986, indicating a natural reclamation process. These areas showed minimal changes in disturbance magnitude, suggesting the absence of significant disturbing events. In contrast, active mining regions showed substantial disturbance consistent with ongoing operations. The combined classification and change detection approach successfully distinguished historical waste coal piles from those in active mining regions, with a precision of 78% and recall of 100%. These findings highlight the potential of remote sensing and temporal analysis to support the identification and assessment of historical waste coal piles. The proposed approach can help prioritize reclamation efforts and inform policy decisions addressing the long-term environmental impacts of historical coal mining. Full article

The self-starting capability of straight-bladed H-type Darrieus Vertical Axis Wind Turbines (VAWTs) remains a major constraint for deployment, particularly in urban, low speed, and turbulent environments. We conducted a systematic review of technological strategies to improve self-starting, grouped into five categories: (1) aerodynamic airfoil design, (2) rotor configuration, (3) passive flow control, (4) active flow control, and (5) incident flow augmentation. Searches in Scopus and IEEE Xplore (last search 20 August 2025) covered the period from 2019 to 2026 and included peer-reviewed journal articles in English reporting experimental or numerical interventions on H-type Darrieus VAWTs with at least one start-up metric. From 1212 records, 53 studies met the eligibility after title/abstract screening and full-text assessment. Data were synthesized qualitatively using a comparative thematic approach, highlighting design parameters, operating conditions, and performance metrics (torque and power coefficients) during start-up. Quantitatively, studies reported typical start-up torque gains of 20–30% for airfoil optimization and passive devices, about 25% for incident-flow augmentation, and larger but less certain improvements (around 30%) for active control. Among the strategies, airfoil optimization and passive devices consistently improved start-up torque at low TSR with minimal added systems; rotor-configuration tuning and incident-flow devices further reduced start-up time where structural or siting constraints allowed; and active control showed the largest laboratory gains but with uncertain regarding energy and durability. However, limitations included heterogeneity in designs and metrics, predominance of 2D-Computational Fluid Dynamics (CFDs), and limited 3D/field validation restricted quantitative pooling. Risk of bias was assessed using an ad hoc matrix; overall certainty was rated as low to moderate due to limited validation and inconsistent uncertainty reporting. In conclusions, no single solution is universally optimal; hybrid strategies, combining optimized airfoils with targeted passive or active control, appear most promising. Future work should standardize start-up metrics, adopt validated 3D Fluid–Structure Interaction (FSI) models, and expand wind-tunnel/field trials. Full article

To broaden the informational scope of existing fuzzy frameworks and enhance their flexibility in representing and processing uncertainty, we propose a novel p,q-rung triangular orthopair fuzzy number (p,q-RTOFN). To enhance the aggregation capability of fuzzy data, we develop a p,q-rung triangular orthopair fuzzy weighted power Bonferroni mean (p,q-RTOFWPBM) operator that integrates the strengths of the Bonferroni mean and power average operators. We formally establish its theorems, proofs, and key properties, including symmetry and idempotency. Furthermore, we extend the complex proportional assessment (COPRAS) method to the p,q-RTOF environment, resulting in a p,q-RTOF-PBM-COPRAS model. This model effectively incorporates both positive and negative evaluation information under uncertainty, thereby reducing information loss and improving decision accuracy. A case study on urban smart farm selection confirms the feasibility and superiority of the proposed approach. This study introduces the p,q-RTOFN framework with extended informational scope, develops a hybrid p,q-RTOFWPBM operator, and incorporates these advances into an extended COPRAS method to achieve more accurate multi-criteria decision-making under uncertainty. Full article

This work presents the synthesis and application of magnetic Fe₃O₄ nanoparticles supported on baobab seed-derived biochar (Fe₃O₄/BSB) for removing Congo red (CR) dye from aqueous solutions through an oxidative process. The biochar support offered a porous structure with a surface area of 85.6 m²/g, facilitating uniform dispersion of Fe₃O₄ nanoparticles and efficient oxidative activity. Fourier-transform infrared (FT–IR) spectroscopy analysis confirmed surface fictionalization after Fe₃O₄ incorporation, while scanning electron microscopy (SEM) images revealed a rough, porous morphology with well-dispersed nanoparticles. Thermogravimetric analysis (TGA) demonstrated enhanced thermal stability, with Fe₃O₄/BSB retaining ~40% of its mass at 600 °C compared to ~15–20% for raw baobab seeds. Batch experiments indicated that operational factors such as pH, nanoparticles dosage, and initial dye concentration significantly affected removal efficiency. Optimal CR removal (94.2%) was achieved at pH 4, attributed to stronger electrostatic interactions, whereas efficiency declined from 94.1% to 82.8% as the initial dye concentration increased from 10 to 80 mg/L. Kinetic studies showed that the pseudo-second-order model accurately described the oxidative degradation process. Reusability tests confirmed good stability, with removal efficiency decreasing only from 92.6% to 80.7% after four consecutive cycles. Overall, Fe₃O₄/BSB proves to be a thermally stable, magnetically recoverable, and sustainable catalyst system for treating dye-contaminated wastewater. Full article

Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating role of quality of information sharing in these relationships. Methods: Data were collected from 80 readymade garment companies across five different geographical locations, with companies of varying sizes (large, medium, and small), involving 365 respondents with a response rate of 65%. A self-administered questionnaire survey was conducted, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for the analysis. Results: The results indicate that all four practices significantly enhance operational performance, while customer relationship management and use of information technology also improve performance indirectly through quality of information sharing, unlike lean practices. Conclusions: The findings suggest that supply chain managers and stakeholders can improve operational performance by implementing supply chain management practices and understanding the complexities of their interrelationships. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

The harsh environment in Arctic regions presents significant challenges to ship stability, particularly when ice accumulation and hull damage occur simultaneously, potentially increasing the risk of instability. This study addresses this critical issue by proposing a comprehensive stability assessment framework for ships operating in Arctic regions. Utilizing the DTMB-5415 ship model, the evaluation integrates both static and dynamic stability under combined ice accumulation and damage conditions. Firstly, an ice accumulation prediction model was developed to estimate ice accumulation over various durations. Subsequently, the static stability of damaged ships with ice accumulation was evaluated. Computational Fluid Dynamics (CFD) simulations were then conducted to calculate roll damping coefficients and analyze the effects of damage location and ice accumulation on free roll decay behavior. A single-degree-of-freedom (SDOF) roll motion model was constructed, incorporating roll damping coefficients and wave excitation moments to simulate roll responses in random wave environments. Extreme value prediction was employed to estimate the short-term extreme response distribution of roll motions. The results indicate that ship stability decreases significantly when ice accumulation and hull damage occur simultaneously. This integrated framework provides a systematic foundation for evaluating ship stability in the Arctic environment, specifically accounting for the combined effects of ice accretion and hull damage. Full article

This study introduces a novel methodology for estimating loading and fatigue damage in the blades of wind turbines, emphasizing non-Gaussian wind conditions’ impact. By calculating blade loading and fatigue using higher statistical moments of the irregular winds, the study demonstrates the significance of non-Gaussian effects on loading and fatigue predictions. A two-step methodology is developed to synthesize non-Gaussian wind processes, integrating the TurbSim(version 1.5) and Hermite moment model transformation methods. These wind time histories are then utilized in a fully coupled simulation of a floating wind turbine, integrating with a blade beam model. Preliminary analysis of wind thrust and the blade root bending moment indicates non-Gaussian effects on aerodynamic loading. Further analysis of fatigue reveals that fatigue hot spots vary along the blade surface, depending on short-term wind conditions and long-term wind distribution, with total fatigue life estimated by summing the fatigue damage at each potential hot spot. The probability density function of long-term wind process is estimated by fitting the Weibull distribution to measured buoy data. The results show that variations in long-term wind speed distributions lead to an average fatigue life difference of about 1.3 years (16%). The Gaussian wind model overestimates fatigue life by roughly 1.5 years (18%) compared to the non-Gaussian model. This highlights the importance of considering both long-term wind distributions and short-term wind characteristics for accurate fatigue assessment. The findings provide valuable insights for the design and operation of floating offshore wind turbines. Full article

As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions. Full article

During operation, failures in a centrifugal pump’s rotary shaft seal—such as wear, deformation, or thermal cracking—can adversely affect system performance. This study utilizes both theoretical and experimental methods to investigate the vibration characteristics of centrifugal pumps under different rotary-shaft-seal conditions. Vibration signals are collected and processed using empirical mode decomposition (EMD) and autoregressive (AR) modeling to generate an EMD-AR spectrum. The results show that rotary-shaft-seal failure leads to decreases in both the head and efficiency of the centrifugal pump. For improved operation stability, centrifugal pumps should operate at or slightly above their design flow rates (Q_d), while avoiding low-flow conditions. Furthermore, the amplitude of the EMD-AR spectrum increases progressively as rotary-shaft-seal degradation worsens. Therefore, the EMD-AR spectrum provides a reliable diagnostic indicator for detecting rotary-shaft-seal damage. Full article