Search Results (34)

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events. Full article

Metal-composite joints, leveraging the high specific strength/stiffness and superior fatigue resistance of carbon fiber reinforced polymers (CFRP) alongside metallic materials’ excellent toughness and formability, have become prevalent in aerospace structures. Fastener flexibility serves as a critical parameter governing load distribution prediction and fatigue life assessment, where accurate quantification directly impacts structural reliability. Current approaches face limitations: experimental methods require extended testing cycles, numerical simulations exhibit computational inefficiency, and conventional machine learning (ML) models suffer from “black-box” characteristics that obscure mechanical principle alignment, hindering aerospace implementation. This study proposes an integrated framework combining numerical simulation with explainable ML for fastener flexibility analysis. Initially, finite element modeling (FEM) constructs a dataset encompassing geometric features, material properties, and flexibility values. Subsequently, a random forest (RF) prediction model is developed with five-fold cross-validation and residual analysis ensuring accuracy. SHapley Additive exPlanations (SHAP) methodology then quantifies input features’ marginal contributions to flexibility predictions, with results interpreted in conjunction with theoretical flexibility formulas to elucidate key parameter influence mechanisms. The approach achieves 0.99 R2 accuracy and 0.11 s computation time while resolving explainability challenges, identifying fastener diameter-to-plate thickness ratio as the dominant driver with negligible temperature/preload effects, thereby providing a validated efficient solution for aerospace joint optimization. Full article

Objective: Predicting driver fatigue degree is crucial for traffic safety. This study proposes a deep learning model utilizing electroencephalography (EEG) signals and multi-step temporal data to predict the next time-step fatigue degree indicator percentage of eyelid closure (PERCLOS) while exploring the impact of different EEG features on prediction performance. Approach: A CTL-ResFNet model integrating CNN, Transformer Encoder, LSTM, and residual connections is proposed. Its effectiveness is validated through two experimental paradigms, Leave-One-Out Cross-Validation (LOOCV) and pretraining–finetuning, with comparisons against baseline models. Additionally, the performance of four EEG features—differential entropy, $\alpha /\beta$ band power ratio, wavelet entropy, and Hurst exponent—is evaluated, using RMSE and MAE as metrics. Main Results: The combined input of EEG and PERCLOS significantly outperforms using PERCLOS alone validated by LSTM, and CTL-ResFNet surpasses baseline models under both experimental paradigms. In LOOCV experiments, the $\alpha /\beta$ band power ratio performs best, whereas differential entropy excels in pretraining–finetuning. Significance: This study presents a high-performance hybrid deep learning framework for predicting driver fatigue degree and reveals the applicability differences in EEG features across experimental paradigms, offering guidance for feature selection and model deployment in practical applications. Full article

A new and transformative era in semiconductor packaging is underway, wherein, there is a shift from transistor scaling to system scaling and integration through advanced packaging. For advanced packaging, interconnect scaling is a key driver, with interconnect density requirements for chip-to-substrate microbump pitch below 5 μm and half-line pitch below 1 μm for Cu redistribution layer (RDL). Here, we present a comprehensive theoretical comparison of thermal cycling behavior in accordance with JESD22-A104D standard, intermetallic thickness evolution, and steady-state thermal analysis of Cu-microbump assembly for different bonding materials and substrates. Bonding materials studied include solder caps such as SAC105 (Sn98.5Ag1.0Cu0.5), eutectic Sn-Pb (Sn63Pb37), eutectic Sn-Bi (Sn42Bi58), Pb95Sn5, Indium, and Cu-Cu TCB structure. Effect of substrates including Si, glass and FR-4 is evaluated for various microbump structures with varying pitches (85 µm, 40 µm, 10 µm, and 5 µm) on their fatigue life. Results indicate that for Cu-microbump assemblies at an 85 µm pitch. The Pb95Sn5 exhibits the longest predicted fatigue life (3267 cycles by Engelmaier and 452 cycles by Darveaux), while SAC105 shows the shortest (320 and 103 cycles). Additionally, the Cu-Cu TCB structure achieves an estimated lifetime of approximately 7800 cycles, which is significantly higher than all solder-based Cu-microbump assemblies. The findings contribute to advanced packaging applications by providing valuable theoretical references for optimizing solder materials and structural configurations. Full article

Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R² = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 10⁴ to >2 × 10⁶ cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet. Full article

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics relevant to their region. These methods should consider key factors such as speed limits, compliance with traffic signs and signals, pedestrian crossings, right-of-way rules, weather conditions, driver negligence, fatigue, and the impact of excessive speed on RTC occurrences. Raising awareness of these factors enables individuals to exercise greater caution, thereby contributing to accident prevention. A promising approach to improving road traffic accident severity classification is the stacking ensemble method, which leverages multiple machine learning models. This technique addresses challenges such as imbalanced datasets and high-dimensional features by combining predictions from various base models into a meta-model, ultimately enhancing classification accuracy. The ensemble approach exploits the diverse strengths of different models, capturing multiple aspects of the data to improve predictive performance. The effectiveness of stacking depends on the careful selection of base models with complementary strengths, ensuring robust and reliable predictions. Additionally, advanced feature engineering and selection techniques can further optimize the model’s performance. Within the field of artificial intelligence, various machine learning (ML) techniques have been explored to support decision making in tackling RTC-related issues. These methods aim to generate precise reports and insights. However, the stacking method has demonstrated significantly superior performance compared to existing approaches, making it a valuable tool for improving road safety. Full article

This study examines the evolution of willingness to accept the monkeypox (Mpox) vaccine in Romania between 2022 and 2025. It explores key sociodemographic and behavioral predictors of vaccine acceptance and investigates how public perceptions—particularly concerning disease severity and conspiracy beliefs—have shifted across two independent cross-sectional samples. Two nationally distributed surveys were conducted in July 2022 (n = 820) and January–February 2025 (n = 1029), targeting Romanian residents aged 18 and above. Data were analyzed using descriptive statistics, Chi-square tests, Kolmogorov–Smirnov tests, and a Random Forest classification model to assess the relative importance of predictors of vaccine acceptance. Between 2022 and 2025, vaccine acceptance increased modestly, particularly among individuals aged 36–65 and those with prior experience of voluntary or COVID-19 vaccination. Random Forest analysis identified behavioral factors as the strongest predictors of acceptance in both years, while the influence of education and gender varied over time. Belief in conspiracy theories slightly declined and lost predictive relevance by 2025. Perceptions of pandemic potential and fear of infection also decreased, suggesting reduced risk salience and possible pandemic fatigue. Despite a slight upward trend, overall Mpox vaccine acceptance in Romania remains among the lowest in Europe. These findings highlight the need for targeted public health communication, particularly toward skeptical or demographically vulnerable groups. Prior vaccination behavior emerged as a key driver of acceptance, indicating that trust-building strategies should capitalize on existing pro-vaccination habits. Future research should adopt qualitative and longitudinal approaches to better capture the evolving psychosocial dynamics of vaccine hesitancy. Full article

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. Full article

Background/Objectives: COVID-19 pandemic had a significant impact on endemic respiratory illnesses. Through behavioral changes in populations and government policy, mainly through non-pharmaceutical interventions (NPIs), Canada saw historic lows in the number of influenza A cases from 2020 through 2022. In this study, we use historical influenza A data for Canada and three provincial jurisdictions within Canada—Ontario, Quebec, and Alberta—to quantify the effects of these NPIs on influenza A. Methods: We aim to see which base parameters and derived parameters of an SIR model are most affected by NPIs. We fit a simple SIR model to historical influenza data to get average paramters for seasonal influenza. We then compare these parameters to those predicted by fitting influenza cases during the COVID-19 pandemic. Results: We find substantial differences in the effective population size and basic reproduction number during the COVID-19 pandemic. We also see the effects of fatigue and relaxation of NPIs when comparing the years 2020, 2021, and 2022. Conclusions: We find that the effective population size is the main driver of change to disease spread and discuss how these retrospective estimates can be used for future forecasting. Full article

The International Maritime Organization (IMO) mandates a reduction in carbon dioxide emissions from 2008 levels by at least 40% by 2030, prompting the widespread adoption of slow steaming and engine de-rating strategies. This study investigates the fatigue life of marine propulsion shafts under slow steaming conditions, focusing on the interplay between torsional and bending vibrations. A finite element (FE) model of a low-speed two-stroke propulsion system is developed, incorporating torsional and lateral excitation sources from both the engine and propeller. Vibrational stresses are computed for multiple operating conditions, and fatigue life is assessed using both the conventional Det Norske Veritas (DNV) methodology and a proposed biaxial stress approach. Results indicate that while torsional vibrations remain the primary fatigue driver, bending-induced stresses contribute marginally to the overall fatigue life. The proposed methodology refines high-cycle fatigue (HCF) assessment by incorporating a corrected S-N curve and equivalent von Mises stress criteria. Comparisons with classification society standards demonstrate that existing guidelines remain valid for most cases, though further studies on extreme alignment deviations and dynamic bending effects are recommended. This study enhances understanding of fatigue mechanisms in marine shafting and proposes a refined methodology for improved fatigue life prediction. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

This study examines the factors influencing the behavioral intention and compliance behavior of Transportation Network Vehicle Service (TNVS) drivers during the COVID-19 pandemic. Grounded in the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM), the study integrates psychological, environmental, and organizational factors to explain TNVS drivers’ adherence to safety protocols. Data were collected from 342 TNVS drivers in the National Capital Region (NCR) and CALABARZON through a structured survey. Structural Equation Modeling (SEM) was employed to analyze the relationships among variables and assess the determinants of compliance behavior. The results indicate that attitude toward compliance (β = 0.453, p < 0.001), risk perception (β = 0.289, p = 0.001), availability of personal protective equipment (PPE) (β = 0.341, p < 0.001), passenger compliance (β = 0.293, p = 0.002), company policies (β = 0.336, p = 0.001), and organizational support systems (β = 0.433, p < 0.001) significantly influence behavioral intention. In turn, behavioral intention strongly predicts compliance behavior (β = 0.643, p < 0.001), confirming its mediating role in linking influencing factors to actual adherence. However, stress and fatigue (β = 0.131, p = 0.211), ride conditions (β = 0.198, p = 0.241), and communication and training (β = 0.211, p = 0.058) showed non-significant relationships, suggesting that their direct effects on behavioral intention are limited. The model explains 69.1% of the variance in compliance behavior, demonstrating its robustness. These findings highlight the importance of fostering positive attitudes, ensuring adequate resource availability, and reinforcing organizational support to improve TNVS drivers’ compliance with safety measures. Practical recommendations include implementing educational campaigns, ensuring PPE access, strengthening company policies, and promoting passenger adherence to safety protocols. The study contributes to the broader understanding of health behavior in the ride-hailing sector, offering actionable insights for policymakers, ride-hailing platforms, and public health authorities. Future research should explore additional contextual factors, gender-based differences, and regional variations, as well as assess long-term compliance behaviors beyond the pandemic context. Full article

Driving is integral to many people’s daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver’s physiological state (e.g., fatigue, anger, or stress) can negatively affect their driving performance. This is especially true for young drivers who have limited driving experience. This research focuses on examining the connection between emotional arousal and aggressive driving behavior in young drivers, using predictive analysis based on electrodermal activity (EDA) data through neural networks. The study involved 20 participants aged 18 to 30, who completed 84 driving sessions. During these sessions, their EDA signals and driving behaviors, including acceleration and braking, were monitored using an Empatica E4 wristband and a telematics device. This study conducted two key analyses using neural networks. The first analysis used a comprehensive set of EDA features to predict emotional arousal, achieving an accuracy of 65%. The second analysis concentrated on predicting aggressive driving behaviors by leveraging the top 10 most significant EDA features identified from the arousal prediction model. Initially, the arousal prediction was performed using the complete set of EDA features, from which feature importance was assessed. The top 10 features with the highest importance were then selected to predict aggressive driving behaviors. Another aggressive driving behavior prediction with a refined set of difference features, representing the changes from baseline EDA values, was also utilized in this analysis to enhance the prediction of aggressive driving events. Despite moderate accuracy, these findings suggest that EDA data, particularly difference features, can be valuable in predicting emotional states and aggressive driving, with future research needed to incorporate additional physiological measures for enhanced predictive performance. Full article

Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3 AV takeovers are affected by human factors, traffic environment, and automatic driving systems. On this basis, this study proposes a conceptual framework of L3 AV takeovers. The main findings of this study include the following: (1) non-driving tasks, non-driving posture, individual characteristics, and trust have an impact on takeover behavior; (2) high traffic density, poor road geometry, and extreme weather have a negative impact on the takeover; (3) multimodal interaction design can improve collection performance. Although the existing research has made rich achievements, there are still many challenges. The influence of human factors on takeover performance is controversial, the quantification standard of takeover influencing factors is insufficient, and the prediction accuracy needs to be improved. It is suggested to refine the criteria of driver participation in NDRT, formulate an effective measurement standard of driver fatigue, and develop a takeover prediction model combining driver status and traffic environment conditions. It provides a research basis for the formulation of laws, infrastructure construction, and human–computer interaction design. Full article

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results indicate that these control strategies can effectively reduce vibrations and impacts, significantly improving ride comfort. This improvement is crucial for alleviating driver fatigue and enhancing driving safety. Among them, the MPC control showed superior performance, reducing vibrations by at least 31% under both random and bump road conditions, outperforming PID and Fuzzy PID in terms of effectiveness and robustness. Full article