In this work, we present a numerical investigation of blood flow in a portion of the human vascular system. More precisely, the present work analyzed the blood flow in the upper portion of the aorta. The aorta and its ramified blood vessels are surrounded by the cardiac muscle. The blood flow generates pressure on the internal surfaces of the artery and its ramifications, thereby causing deformation of the cardiac muscle. The numerical analysis used the Navier–Stokes equations as the governing equations of blood flow for the calculation of the velocity field and pressure distribution in the blood. The neo-Hookean hyperelastic model was used for the description of the behavior of the vessel walls. The velocity and pressure distributions were analyzed. The deformation of the vessel was also investigated. The numerical results could be used to better understand and predict the factors that trigger cardiovascular diseases and distortions of the aorta and as a diagnostic tool in clinical applications.

In this paper, a numerical model allows to analyze the photovoltaic parameters according to the electronic properties of In_xGa_1−xN/GaN MQW solar cells under the effect of temperature, the number of quantum wells and indium composition. The numerical investigation starts from the evaluation through the finite difference (FDM) simulation of the self-consistent method coupled with the photovoltaic parameters taking into account the effects of the spontaneous and piezoelectric polarization. The results found were consistent with the literature. As expected, the temperature had a negative impact on the performance of InGaN/GaN MQW solar cells. However, increasing the number of quantum wells improves cell performance. This positive impact further improves with the increase in the indium rate. The obtained results were 28 mA/cm² for the short-circuit current density, 1.43 V for the open-circuit voltage, and the obtained conversion efficiency was 31% for a model structure based on 50-period InGaN/GaN-MQW-SC under 1-sun AM1.5G.

Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.

This paper investigates a lab-on-disk biosensor with the aim of improving its performance by optimizing the particle swarm algorithm through the application of Box-Behnken Design (BBD). The study concluded that the optimal conditions for the Particle Swarm Optimization (PSO) parameters - social learning factor (c ₁ =0.5), cognitive acceleration factor (c ₂ =2), inertia weight (w=0.65), and swarm size (Ps=176) - resulted in a significant improvement in prediction accuracy, as evidenced by an R-squared value of 99.9% and a low RMSE of 0.05. The results demonstrate the exceptional effectiveness of Box-Behnken Design (BBD) in optimizing PSO parameters for Artificial Neural Networks (ANNs), resulting in improved performance of the lab-on-disk biosensor. These optimized conditions not only improve response time, but also hold potential for broader applications in microfluidic sensing technologies.