Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches. These approaches recognize different levels of stress based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring good quality features from these modalities using feature engineering is often a difficult job. Recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. This paper proposes different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset) for the driver’s two and three stress levels. The fuzzy EDAS (evaluation based on distance from average solution) approach is used to evaluate the performance of the proposed models based on different classification metrics (accuracy, recall, precision, F-score, and specificity). Fuzzy EDAS performance estimation shows that the proposed CNN and hybrid CNN-LSTM models achieved the first ranks based on the fusion of BH, E4-Left (E4-L), and E4-Right (E4-R). Results showed the significance of multimodal data for designing an accurate and trustworthy stress recognition diagnosing model for real-world driving conditions. The proposed model can also be used for the diagnosis of the stress level of a subject during other daily life activities.

Aluminium metal matrix composites (AMMCs) using alumina (Al₂O₃) and silicon carbide (SiC) as reinforcement elements are gaining significant interest in various applications because of their excellent properties. In this study, Al₂O₃/SiC with compositions (0.5 wt.%, 1.5 wt.%, and 2.5 wt.% for each) were used as reinforcement elements in an aluminium alloy (AA2024-T6). The samples prepared were AA2024-T6 + (0.5Al₂O₃/0.5SiC), AA2024-T6 + (1.5Al₂O₃/1.5SiC), and AA2024-T6 + (2.5Al₂O₃/2.5SiC) using a stir-casting technique. The experimental study included density calculation mechanical properties, such as tensile strength, compressive strength and hardness. The study also included a microstructure examination of the fracture surface of the tensile specimens. The results showed that incorporating Al₂O₃/SiC as reinforcement materials into aluminium AA2024-T6 significantly improved its properties. Hence, increasing the reinforcement with compositions of 2.5Al₂O₃/2.5SiC into AA2024-T6 showed a drop in density and increased mechanical properties, such as ultimate tensile strength, compressive strength and hardness, compared to the base alloy (AA2024-T6). Furthermore, the scanning electron microscopy analysis revealed the uniform distribution of the reinforcement particles resulting in strong bonding with the matrix. The findings suggest that Al₂O₃/SiC reinforced with AA2024-T6 can be used in applications where a combination of lightweight and high strength is needed.

The purpose of this study is to examine the impact of various process parameters, such as cold work, aging temperature, and aging time, on the yield strength, ultimate tensile strength (UTS), and elongation of AA6061 tubes. The experimental plan is carried out, and the data is analyzed using Design Expert software. Main effects plots and interaction plots are generated to visually examine the effects of individual factors and the interaction between two factors on the output response variables. ANOVA analysis is conducted to assess the statistical significance of the model and individual model coefficients. The results reveal that all input factors had a significant impact on yield, whereas cold work and temperature and their interaction are significant for UTS. However, the model is not significant for elongation. The most notable finding is that the aging temperature’s effect is significant than the other two factors. These study findings can inform future experiments or process optimization efforts by considering the combined impact of these factors and their interactions. The study also found that the optimal temperature range is between 155°C to 170°C, along with a recommended cold work percentage of 10% or more and preferred time of above 10 h up to overage time. The model achieved an overall accuracy rate of over 90%, indicating its ability to predict the response variable with a high degree of precision.

In this study, we employ a parallel Computational Fluid Dynamics (CFD) code integrated with the VTDIRECT95 algorithm, a parallel deterministic global optimization method, to conduct global optimization for an oscillating circular cylinder. We conduct numerical simulations for the flow at a Reynolds number of 500 within the parameter range of 0.1≤Ax≤0.3 and 0.5fst≤fex≤2.5fst where Ax represents the inline oscillating amplitude, fex denotes the forcing oscillation frequency, and fst corresponds to the frequency of a stationary cylinder. To enhance computational efficiency, a combination of VTdirect and a CFD solver is utilized to efficiently identify the synchronization region, thereby reducing computational resources. The results reveal a significant reduction in the lift coefficient within the synchronized region compared to unsynchronized regimes. Furthermore, the study delves into the underlying flow physics behind synchronization and lift suppression. By synchronizing the shedding of vortices, their detrimental effects are nullified, resulting in a reduction in lift. Moreover, the research examines the influence of three-dimensional (3-D) flow by comparing 2-D and 3-D simulations at two different Reynolds numbers. It demonstrates that accounting for 3-D effects yields more accurate predictions of fluid behavior. Synchronization maps and root mean square (rms) lift coefficient plots illustrate the impact of Reynolds number and movement frequency on lift suppression. The findings indicate that achieving synchronization in 3-D flow necessitates stronger amplitudes and higher frequencies. At higher Reynolds numbers, the wake structures become unstable, leading to intricate vortical patterns. Consequently, the synchronization curve shifts towards higher amplitudes and frequencies in 3-D simulations. Understanding these phenomena is vital for reducing lift force in practical applications. This research significantly contributes to knowledge regarding synchronization and lift suppression in fluid flow around vibrating cylinders.