Computation

The increasing electricity demand of educational buildings requires the development of intelligent approaches to power supply management that improve energy efficiency and ensure the reliable operation of electrical networks. This study aims to develop a mathematical model for determining the recommended voltage level in the power supply system of an educational building and implement it in a PLC-based SCADA control system. A second-order central composite design was applied to investigate the influence of three factors, including total electrical load, building wiring length, and external supply line length, on the recommended voltage level. The regression model was statistically validated using Cochran’s, Student’s, and Fisher’s criteria. The obtained model demonstrated high adequacy for the experimental data (R² = 0.9934, adjusted R²_ad = 0.9884, RMSE = 0.447 kV, and F = 0.175 < F_cr = 2.30). The developed algorithm was implemented in the OWEN PLC300 controller using the EnLogic programming environment and integrated with the ENTEK SCADA system for automated voltage calculation and load management according to consumer priority. The proposed methodology can be applied in the design and modernisation of automated power supply systems for educational buildings and provides a practical approach to improving the efficiency of electrical energy distribution.

Computational modeling has become an important tool for investigating complex cardiovascular conditions and supporting the development and evaluation of endovascular treatments. In thoracic aortic disease, numerical approaches provide tools for examining the interaction between blood flow, arterial mechanics, and implanted stent-grafts under physiologically relevant conditions. This study reviews the application of computational methods to thoracic endovascular aortic interventions, considering the main approaches used to investigate their hemodynamic, structural, and biomechanical behavior. The database was retrieved from the Web of Science on 20 March 2026, yielding a total of 99 records, of which 94 were retained. A bibliometric analysis is first presented to quantify the available literature, characterizing the evolution of the field, major research directions, and methodological developments. Then, the literature is discussed with a focus on the capabilities of computational methodologies, particularly computational fluid dynamics, the finite element method, and fluid–structure interaction. The literature shows a growth in computational investigations, with computational fluid dynamics being the most frequently employed approach, complemented by finite element and fluid–structure interaction methods. The representative studies examined also illustrate the development of patient-specific and multiphysics approaches alongside more conventional computational models. These developments have expanded the potential of computational simulations for studying disease progression, device performance, treatment strategies, and post-intervention outcomes. However, challenges related to computational cost, physiological boundary conditions, material properties, model assumptions, and experimental or clinical validation remain.

In this study, an integrated finite-difference and deep neural network analysis of the simultaneous multiphysics of coupled momentum, heat, nanoparticle, and pollutant transport in a Sutterby tri-hybrid nanofluid over a quadratically radiative Riga surface is developed. The Fe₃O₄, Al₂O₃, and TiO₂ nanoparticles are considered, and a mixture of propylene glycol and water is used, along with electroosmotic forcing, electromagnetic actuation, nonlinear thermal radiation, viscous dissipation, Brownian motion, thermophoresis, and pollutant transport. The key novelty is the ability to formally connect these strongly coupled nonlinear transport processes with a deep neural network (DNN) surrogate model to allow for the rapid and accurate prediction of complex thermo-fluid and mass-transfer responses. The governing equations are then transformed into nonlinear equations and solved numerically using a finite difference method (FDM) coded in FORTRAN to produce high fidelity numerical data sets that are used to train a feed-forward back-propagation deep neural network (DNN) implemented in Python with PyTorch. The optimized DNN successfully predicted the velocity, temperature, field of nanoparticles, and pollutant concentration, with the RMSE less than (3.2 × 10⁻²) and (R² > 0.987), which indicates that the optimized DNN possesses high predictive accuracy and reliability. The fluid motion is improved by the increased electroosmotic and electromagnetic parameters due to increased electrokinetic and Lorentz forces, and the increased Brinkman number increases the fluid viscous dissipation heating and raises the temperature field. The enhanced Brownian motion facilitates the migration of nanoparticles and greatly affects coupled heat and mass transport while pollutant-related parameters control concentration distributions. The proposed DNN framework has the potential of being an efficient surrogate for fast simulations, parametric evaluation, and optimization for nonlinear multiphysics systems, and has direct applications to thermal management systems in industry, microfluidic processing, electrochemical technologies, electromagnetic flow-control devices, industrial wastewater treatment systems, and pollutant removal systems.