Search Results (18,375)

Battery electric vehicles (BEVs) are gaining market share, yet range anxiety and sparse charging still create demand for hybrids with combustion-engine range extenders. Range-extender vehicles face high customer expectations for noise, vibration, and harshness (NVH) due to their direct comparability with fully electric vehicles. Key challenges include the vibrations of the internal combustion engine, especially from vehicle-induced starts, and the discontinuous operating principle. A technological concept to reduce vibrations in the drivetrain and on the engine mounts, called “FEVcom,” relies on rolling moment compensation. In this concept, a counter-rotating electric machine is coupled to the internal combustion engine via a gear stage to minimize external mount forces. However, due to high speed fluctuations of the crankshaft, the gear drive tends to rattle, which is perceived as disturbing and must be avoided. As part of this work, the rolling moment compensation system was examined regarding its vibration excitation, and an extension to prevent gear rattling was simulated and optimized. For the simulation, the extension, based on a chain or belt drive, was set up as a multi-body simulation model in combination with the range extender and examined dynamically at different speeds. Variations of the extended system were simulated, and recommendations for an optimized layout were derived. This work demonstrates the feasibility of successful rattling avoidance in a range-extender drivetrain with full utilization of the rolling moment compensation. It also provides a solid foundation for further detailed investigations and for developing a prototype for experimental validation based on the understanding gained of the system. Full article

Fatigue in air traffic controllers (ATCOs), particularly within remote tower operations, poses a substantial risk to aviation safety due to its detrimental effects on vigilance, decision-making, and situational awareness. While electroencephalography (EEG) provides a promising avenue for objective fatigue monitoring, existing models often fail to adequately capture both the spatial dependencies across brain regions and the temporal dynamics of cognitive states. To address this challenge, we propose a novel EEG-based fatigue detection framework, Spatio-Temporal Graph with Center Loss Network (STG-CLNet), which jointly models topological brain connectivity and temporal EEG evolution. The model leverages a multi-stage graph convolutional network to encode spatial dependencies and a triple-layer LSTM module to capture temporal progression, while incorporating center loss to enhance feature discriminability in the embedding space. We constructed a domain-specific EEG dataset involving 34 ATCO participants operating in high- and low-traffic remote tower simulations, with fatigue labels derived from three validated subjective metrics. Experimental results demonstrate that STG-CLNet achieves superior classification performance (accuracy = 96.73%, recall = 92.01%, F1-score = 87.15%), outperforming several strong baselines, including LSTM and EEGNet. These findings underscore the potential of STG-CLNet for integration into real-time cognitive monitoring systems in air traffic control, contributing to both theoretical advancement and operational safety enhancement. Full article

Traditional methods often fail when confronted with data characterised by uncertainty, incompleteness, and dynamically evolving threats within the Industrial Internet of Things (IIoT) environment. This paper presents the role of fuzzy set methods as a response to these challenges in ensuring IIoT security. A systematic literature review reveals how fuzzy set methods contribute to supporting and enabling actions ranging from anomaly detection to risk analysis. The work focuses on fuzzy systems such as the Fuzzy Inference System (FIS) and the Adaptive Neuro-Fuzzy Inference System (ANFIS), highlighting their strengths, including their resilience to imperfect data and the intuitiveness of their rules. It also identifies challenges related to optimisation and scalability. The article outlines directions for further research, indicating the potential of fuzzy methods as a cornerstone of future, intelligent IIoT cyber defence systems, capable of effectively responding to complex and changing attack scenarios. Full article

In the global shift toward sustainable energy, enhancing the efficiency of renewable energy systems plays a pivotal role in advancing the Sustainable Development Goals. This study focuses on optimizing the design of a corrugated-flange diffuser integrated with a wind turbine to enhance its performance, particularly in low-wind conditions. While most previous research has examined wind farm performance at high wind speeds, the challenge of effective power extraction at low wind speeds remains largely unresolved. The potential of diffusers to enhance wind turbine efficiency under low-wind conditions has received limited investigation, with most prior studies focusing solely on empty diffuser configurations without turbine integration. In addition, the influence of flange geometry on diffuser performance remains largely unexplored. In this study, parametric analyses were conducted to identify the optimal diffuser design, followed by comparative performance evaluations of configurations with and without turbine integration, using computational fluid dynamics (CFD) simulations. The results show that integrating a turbine with the optimized corrugated-flange diffuser increased flow velocity by 67.85%, achieving an average of approximately 14 m/s around the blade region. In comparison, the optimized corrugated-flange diffuser alone increased flow velocity by 44%, from 4.5 m/s to 8.036 m/s. These findings highlight the potential of optimized diffuser designs to enhance small-scale wind turbine performance in low-wind conditions. Full article

Even though clinoptilolite mineral is the most important natural zeolite for technical applications, the molecular-level insights and detailed knowledge of their true local structures and adsorption behavior are largely lacking. An experimental determination of their surface structures, in particular, could be very challenging due to the sensitivity of some facets to temperature and impurities. In this study, we present a robust multiscale modeling framework to investigate the adsorption of 5-fluorouracil, an anticancer drug, on dispersion-corrected density functional theory (DFT-D3)-optimized Na-clinoptilolite surfaces. Using a combination of interface force field and polymer consistent force field-based molecular dynamics with simulated annealing and parallel replica sampling, followed by DFT-D3 optimizations, we explore a wide configurational space of surface–molecule interactions. Our results show that Na-clinoptilolite surfaces support very strong adsorption, with adsorption energies ranging from −430.0 to −174.4 kJ/mol. Surface models with exposed Na cations consistently exhibit stronger binding, in contrast to their known steric hindrance effects in bulk environments. Furthermore, cation-free surfaces displayed relatively weaker interactions, yet configurations exposing the 8-membered rings (8 MR) demonstrated more favorable adsorption than those exposing 10 MR channels due to enhanced hydrogen bonding and spatial and entropic confinement effects. These findings reveal the importance of surface composition, local geometry, and configurational sampling in determining adsorption performance and lay the groundwork for future studies on cation-specific and multicationic clinoptilolite systems. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

Accurate prediction of slab temperature during the continuous casting and rolling process is essential for optimizing reheating furnace scheduling and achieving energy savings and emission reductions in steel production. However, because of the dynamic boundary conditions caused by the complex transport processes, obtaining precise temperature data for slabs remains challenging. These difficulties lead to issues such as low hot charging rates, mixing of hot and cold slabs in reheating furnaces, and excessive heat loss from slabs after cutting. To address these challenges, this study develops a mathematical model to calculate slab temperatures during the continuous casting and rolling process, providing a foundation for production scheduling optimization. The model accounts for the coupled heat transfer effects induced by dynamic slab stacking and the stacking heat transfer effects resulting from slabs with varying cross-sectional dimensions. Validation against experimental data demonstrated the model’s accuracy and reliability. Key findings highlighted that neglecting dynamic stacking effects or simplifying slab dimensions introduces errors. These results enhance slab temperature tracking in complex processes and advance related theoretical understanding. Full article

Rolling bearings, as essential parts of rotating machinery, face significant challenges in fault diagnosis due to limited fault samples and high noise interference, both of which reduce the effectiveness of traditional methods. To tackle this, this study proposes a fault diagnosis approach that combines Digital Twin (DT) and deep learning. First, actual bearing vibration data were collected using an experimental platform. After denoising the data, a high-fidelity digital twin system was built by integrating the bearing dynamics model with a Generative Adversarial Network (GAN), thereby effectively increasing the fault data. Next, the Wavelet Synchro-Extracting Transform (WSET) is used for high-resolution time-frequency analysis, and convolutional neural networks (CNNs) are employed to extract deep fault features adaptively. The fully connected layer of the CNN is then combined with a Least Squares Support Vector Machine (LSSVM), with key parameters optimized through an Improved Pelican Optimization Algorithm (IPOA) to improve classification accuracy significantly. Experimental results based on both simulated and publicly available datasets show that the proposed model has excellent generalizability and operational flexibility, surpassing existing deep learning-based diagnostic methods in complex industrial settings. Full article

Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to provide reliable forecasts of future quality changes. To overcome these challenges, an interpretable prediction framework for wheat storage quality based on stacked ensemble learning is proposed. Three key features, Effective Accumulated Temperature (EAT), Cumulative High Temperature Deviation (CHTD), and Cumulative Temperature Gradient (CTG), were derived from grain temperature data to capture the spatiotemporal dynamics of the internal temperature field. These features were then input into the stacked ensemble learning model to accurately predict historical quality changes. In addition, future grain temperatures were predicted with high precision using a Graph Convolutional Network-Temporal Fusion Transformer (GCN-TFT) model. The temperature prediction results were then employed to construct features and were fed into the stacked ensemble learning model to enable future quality change prediction. Baseline experiments indicated that the stacked model significantly outperformed individual models, achieving R² = 0.94, MAE = 0.44 mg KOH/100 g, and RMSE = 0.59 mg KOH/100 g. SHAP interpretability analysis revealed that EAT constituted the primary driver of wheat quality deterioration, followed by CHTD and CTG. Moreover, in future quality prediction experiments, the GCN-TFT model demonstrated high accuracy in 60-day grain temperature forecasts, and although the prediction accuracy of fatty acid value changes based on features derived from predicted temperatures slightly declined compared to features based on actual temperature data, it remained within an acceptable precision range, achieving an MAE of 0.28 mg KOH/100 g and an RMSE of 0.33 mg KOH/100 g. The experiments validated that the overall technical route from grain temperature prediction to quality prediction exhibited good accuracy and feasibility, providing an efficient, stable, and interpretable quality monitoring and early warning tool for grain storage management, which assists managers in making scientific decisions and interventions to ensure storage safety. Full article

Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in accuracy, robustness, and practicality persist, especially for cross-subject detection. Multimodal data fusion can enhance the effective estimation of driver fatigue. In this work, we leverage the advantages of multimodal signals to propose a novel Multimodal Attention Network (TMU-Net) for driver fatigue detection, achieving precise fatigue assessment by integrating electroencephalogram (EEG) and electrooculogram (EOG) signals. The core innovation of TMU-Net lies in its unimodal feature extraction module, which combines causal convolution, ConvSparseAttention, and Transformer encoders to effectively capture spatiotemporal features, and a multimodal fusion module that employs cross-modal attention and uncertainty-weighted gating to dynamically integrate complementary information. By incorporating uncertainty quantification, TMU-Net significantly enhances robustness to noise and individual variability. Experimental validation on the SEED-VIG dataset demonstrates TMU-Net’s superior performance stability across 23 subjects in cross-subject testing, effectively leveraging the complementary strengths of EEG (2 Hz full-band and five-band features) and EOG signals for high-precision fatigue detection. Furthermore, attention heatmap visualization reveals the dynamic interaction mechanisms between EEG and EOG signals, confirming the physiological rationality of TMU-Net’s feature fusion strategy. Practical challenges and future research directions for fatigue detection methods are also discussed. Full article

Atmospheric pollution poses varying degrees of health risks to the public health of local residents and ecological sustainability. As a typical basin-edge city, Xiangyang, one of the industrial center cities in Hubei Province, China, is facing challenges regarding air quality. However, previous research on the regional correlation, temporal potential sources, and dynamic changes in health risks related to air pollution in Xiangyang has been reported infrequently. The purpose is to investigate the spatiotemporal characteristics of PM_2.5 and O₃ in Xiangyang, their potential source regions, associated health risks, and spatial correlations. The health risks associated with air pollution in Xiangyang City from 2019 to 2023 have showed downward trend from 2.252 in 2019 to 1.032 in 2023. The potential source regions of O₃ in summer were concentrated in northeastern Hubei, southeastern Henan, western and northern Anhui, and central Shaanxi province. The weight of potential sources of O₃ in summer demonstrated an increasing trend in central Shaanxi. The obvious potential sources regions of PM_2.5 in winter extended to Hubei, northern Hunan, Henan, northern Anhui, western Shandong, and central Shaanxi. The weight of potential sources of PM_2.5 in winter demonstrated an increasing trend in central Shaanxi. Full article

Wireless body area networks (WBANs) are a pivotal solution for continuous health monitoring, but their energy constraints pose a significant challenge for long-term operation. This paper provides a comprehensive review of state-of-the-art energy-efficient mechanisms, critically evaluating solutions across various network layers. We focus on three key approaches: energy-aware MAC protocols that reduce idle listening and optimize duty cycling; energy-efficient routing protocols that enhance data transmission and network longevity; and emerging energy harvesting techniques that offer a path toward energy-autonomous WBANs. Furthermore, the paper provides a detailed analysis of the inherent trade-offs between energy efficiency and other critical performance metrics, such as latency, reliability, and security. It also explores the transformative potential of emerging technologies, such as AI and blockchain, for dynamic energy management and secure data handling. By synthesizing these findings, this work contributes to the development of sustainable WBAN solutions and outlines clear directions for future research. Full article

Asymmetric nanomotors are a class of self-propelled nanoparticles that exhibit asymmetries in shape, composition, or surface properties. Their unique asymmetry, combined with nanoscale dimensions, endows them with significant potential in environmental and biomedical fields. For instance, glutathione (GSH) induced chemotactic nanomotors can respond to the overexpressed glutathione gradient in the tumor microenvironment to achieve autonomous chemotactic movement, thereby enhancing deep tumor penetration and drug delivery for efficient induction of ferroptosis in cancer cells. Moreover, self-assembled spearhead-like silica nanomotors reduce fluidic resistance owing to their streamlined architecture, enabling ultra-efficient catalytic degradation of lipid substrates via high loading of lipase. This review focuses on three core areas of asymmetric nanomotors: scalable fabrication (covering synthetic methods such as template-assisted synthesis, physical vapor deposition, and Pickering emulsion self-assembly), propulsion mechanisms (chemical/photo/biocatalytic, ultrasound propelled, and multimodal driving), and functional applications (environmental remediation, targeted biomedicine, and microelectronic repair). Representative nanomotors were reviewed through the framework of structure–activity relationship. By systematically analyzing the intrinsic correlations between structural asymmetry, energy conversion efficiency, and ultimate functional efficacy, this framework provides critical guidance for understanding and designing high-performance asymmetric nanomotors. Despite notable progress, the prevailing challenges primarily reside in the biocompatibility limitations of metallic catalysts, insufficient navigation stability within dynamic physiological environments, and the inherent trade-off between propulsion efficiency and biocompatibility. Future efforts will address these issues through interdisciplinary synthesis strategies. Full article

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

With the advancement of intelligent human–computer interaction (IHCI) technology, the accurate recognition of an operator’s intent has become essential for improving the collaborative efficiency in complex tasks. To address the challenges posed by stringent safety requirements and limited data availability in pilot intent recognition within the aviation domain, this paper presents a human intent recognition model based on operational sequence comparison. The model is built based on standard operational sequences and employs multi-dimensional scoring metrics, including operation matching degree, sequence matching degree, and coverage rate, to enable real-time dynamic analysis and intent recognition of flight operations. To evaluate the effectiveness of the model, an experimental platform was developed using Python 3.8 (64-bit) to simulate 46 key buttons in a flight cockpit. Additionally, five categories of typical flight tasks along with three operational test conditions were designed. Data were collected from 10 participants with flight simulation experience to assess the model’s performance in terms of recognition accuracy and robustness under various operational scenarios, including segmented operations, abnormal operations, and special sequence operations. The experimental results demonstrated that both the linear weighting model and the feature hierarchical recognition model enabled all three feature scoring metrics to achieve high intent recognition accuracy. This approach effectively overcomes the limitations of traditional methods in capturing complex temporal relationships while also addressing the challenge of limited availability of annotated data. This paper proposes a novel technical approach for intelligent human–computer interaction systems within the aviation domain, demonstrating substantial theoretical significance and promising application potential. Full article