The paper describes the model for the composition evolution in the Al-CuO powder mixture under laser processing conditions, taking into account the processes in two spatial scales: meso and macro levels. Processes at meso level (at the level of the reactive cell) are described within the theory of reactive diffusion and include the new phase's growth and the formation of stresses associated with the changing composition and properties. Information from meso level on composition changes is forwarded to the macrolevel where the temperature is calculated. Averaging stresses over the mesocells gives the components of the macro stress tensor of the second kind, and the properties averaging over the cells leads to the distribution of effective properties of a new composite in the macro-sample.

The generated film pressures, carried loads, and friction coefficients of the hydrodynamic lubricated inclined fixed pad micro thrust bearing with the minimum clearance on the 1 nm scale and with the nanoscale surface roughness are analytically investigated when the effects of the surface elastic deformation and the physically adsorbed layer on the bearing surface are incorporated. The sandwich film and the pure physically adsorbed layer are intrinsically irregularly mixed in this bearing because of the surface roughness. Different flow equations are used to describe their flows. Numerically solving these coupled equations gives the film pressures and then the carried load of this bearing. The shear stresses and friction coefficients on the bearing surfaces are also calculated based on the nanoscale noncontinuum flow theory and the nanoscale multiscale flow theory. The calculation results show that the film pressure ripples in the bearing owing to the surface roughness are reduced by the surface elastic deformation. For the same operating conditions the carried load of the bearing is significantly increased with the increase of the surface roughness, especially for the strong fluid-bearing surface interaction. The film stiffness in the bearing is also significantly increased with the increase of the surface roughness, particularly for lower bearing clearances. For the same operational parameter values, the friction coefficient of the bearing is reduced with the increase of the strength of the interaction between the fluid and the contact surface, and it is also reduced owing to the surface elastic deformation or the higher surface roughness.

Chest X-ray images are an essential diagnostic tool in the medical field, but transmitting and storing these images can raise security concerns. Privacy-preserving techniques in healthcare databases protect sensitive patient information from unauthorized access, breaches, and misuse. Using a combination of elliptic curve cryptography (ECC) for encryption, a convolutional neural network (CNN) for feature extraction, and a long short-term memory (LSTM) network for classification, the aim of this research is to develop a safe and effective classification system for chest X-ray images. The research integrates hyperparameter optimization, essential for LSTM, as it dramatically impacts the model's performance. Additionally, the research employs an oppositionbased learning strategy, the traditional wild horse optimization (WHO) approach and an enhanced WHO (EWHO), to enhance the classification performance. The research objectives are to diagnose and classify chest X-ray images into four types: normal lung, COVID-19-infected lung, lung opacity, and viral pneumonia. The proposed opposition-based learning model improves the accuracy, efficiency, and consistency of diagnostic and treatment decisions. The proposed EWHO-configured LSTM approach attains 97.4% accuracy for an efficient classification system for chest X-ray images. The research highlights the importance of privacy-preserving techniques in healthcare databases to protect sensitive patient information from unauthorized access, breaches, and misuse.

The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the magnetic resonance imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the images in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data, and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. In this study, the MRI images are initially gathered. The gathered images are directly given to the image segmentation process, where the Multi-Scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed random opposition of cicada swarm optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process where the hybrid and dilated convolution-based adaptive residual attention network (HDCARAN) is utilized. The HDCARAN categorizes the lesions from the MRI images very effectively and detects the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously developed multiple sclerosis detection schemes to evaluate the effectiveness of the designed model with respect to several functionality measures. The implementation of HDCARAN results in the detection of lesions in the initial stages, with a 94% specificity level, as well as efficiency performance based on existing models. The model also obtains a 94.5% accuracy, as opposed to the other conventional systems. The results show the proposed method performs better than other machine learning models.

The numerical calculation was carried out for the multiscale hydrodynamic step bearing with ultra-low clearance and nanoscale surface roughness based on the newly developed multiscale approach. The moving surface was assumed as perfectly smooth and the stationary surface was imposed with the sinusoidal surface roughness. Due to the surface roughness, locally the physically adsorbed boundary layer may be only present, while in other areas, the sandwich films consisting of both the adsorbed boundary layers and the intermediate continuum fluid film may be present. In this bearing, the fluid film may be locally severely squeezed so that the local film pressure is very high. In this circumstance, the influences of the film pressure on both the film viscosity and density were considered. The results show that with the increase of the surface roughness, the hydrodynamic pressure and carried load of the bearing are very significantly increased especially for a stronger fluid-bearing surface interaction; the fluid piezo-viscous effect becomes stronger with the increases of both the surface roughness and the fluid-bearing surface interaction strength.

Posterior lumbar interbody fusion (PLIF) is a common technique for decompressing the diseased spinal segment. This study aims to compare the biomechanical effects of four PLIF scenarios. A finite element model of the L3-L4 segment is used to simulate decompression with different scenarios: S1 (PEEK cage), S2 (PEEK cage with graft), S3 (Titanium cage), and S4 (Titanium cage with graft). Range of motion, stress, and micromotion are measured under various loading conditions. S2 demonstrates sufficient stability, reduced micromotion, and lower stress on the adjacent parts of the lumbar segment, indicating that S2 may be a preferred option for PLIF.

This article proposes a novel biomedical system integrating internet of things (IoT) and mixed reality (MR) technologies for detecting, tracking, and preventing asymptomatic corona virus disease (COVID) patients from entering into public places which prevents the further spread of COVID-19 infection. Asymptomatic patients are the very active carriers for virus transmission, and the most challenging conditions in mitigating the virus transmission are contact tracking and contact tracing of asymptomatic patients. The proposed system can be implemented in public places such as theaters, malls, railway stations, airport, markets, conferences, and other gatherings for screening people to detect asymptomatic COVID patients and restrict them from entry. The arrest or decrease in spread of COVID infection during pandemic situation is the most challenging factor around the globe. However, with the proposed system, detection and prevention of asymptomatic COVID patients will result in drastic decrease in the spread of COVID infection during pandemic situation. The proposed system comprises an IoT-based sensing system to get the current sensor values and an MR vision software system to retrieve the pre-saved sensor values from the server. The MR vision system compares the present sensor values and the server values of the human and displays accurately with green MR images for permitted persons and red MR images for restricted asymptomatic COVID patients.

The paper introduces a novel approach, referred to as data-physics driven reduced order homogenization, for continuum damage mechanics. The proposed method combines the benefits of the physics-based reduced order homogenization and data-driven surrogate modeling by striking a balance between accuracy, computational efficiency, and physical interpretability. The primary objective of this hybrid approach is to minimize computational cost associated with online predictions at the macroscopic scale while preserving accuracy and physical interpretability. This is achieved by leveraging a surrogate-based Bayesian inference to extract crucial information at a representative volume element (RVE) level. With the inferred data, online predictions are performed using a data-enhanced reduced order homogenization. Consequently, the computational time required at a macroscopic scale is significantly reduced compared to both the conventional direct numerical simulation and the computational homogenization approach.