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As industries rely more and more on magnetohydrodynamic (MHD) systems for different uses in power, production, and management of the environment, it becomes essential to optimize these operations. The study seeks to improve the effectiveness and productivity of cooling structures, chemical reaction reactors, and contaminant control methods by investigating these intricate interconnections. Because of this, the work scrutinizes the endothermic/exothermic (EN/EX) chemical processes, convective boundary conditions, and pollutant concentration impacts on MHD nanofluid circulation around a rotating sphere. The governing equations based on the above assumptions are reduced into a system of ordinary differential equations and solved numerically with Runge–Kutta Fehlberg’s fourth- and fifth- order schemes. The obtained numerical outcomes from the numerical scheme are presented with the aid of graphs, and the results show that the rate of mass transfer decreases with an increase in the external pollutant local source and solid volume percentage. For changes in the values of the activation energy parameter and solid fraction, the rate of thermal dispersion drops for the EN case and upsurges for the EX case. The concentration profile shows increment with the addition of the external pollutant source variation parameter and local pollutant external source parameter. The outcomes of the present work can be helpful in cooling equipment, developing advanced methods for controlling pollution, environmental management, MHD generators, and various industrial contexts.

Magnetite and cobalt ferrite (CoFe2O4) nanoparticles are frequently utilized in several applications, including magnetic drug delivery, hyperthermia, magnetic resonance imaging, etc. In the current investigation, the magnetohydrodynamic three-dimensional heat transfer (HT) flow induced by a non-Newtonian Eyring–Powell fluid is incorporated by a carrier sodium alginate (NAC6H7O6)-based CoFe2O4 nanoparticles over a deformable (stretching/shrinking) horizontal plane surface with orthogonal shear stress and power-law velocity. The HT analysis along with the substantial effect of irregular heat source/sink as well as entropy generation is also performed. The similarity variables altered the posited leading equations into ordinary differential (similarity) equations. The function bvp4c in Matlab is then used to solve these equations numerically for various parameter values. Results indicate that, in general, there are two alternative solutions for the phenomenon of suction and deformable parameters. In addition, the essential thermal evaluation is enhanced owing to the significance of CoFe2O4 nanoparticles, magnetic parameter, and irregular heat source/sink.

The investigation of thermal radiation and thermophoretic impacts on nano-based liquid circulation in a microchannel has a significant impact on the cooling of microscale equipment, microliquid devices, and many more. These miniature systems can benefit from the improved heat transfer efficiency made possible by the use of nanofluids, which are designed to consist of colloidal dispersion of nanoparticles in a carrier liquid. Understanding and precisely modeling the thermophoretic deposition (TPD) of nanoparticles on the channel surfaces is of utmost importance since it can greatly affect the heat transmission properties. This work examines the complex interaction between quadratic thermal radiation, magnetohydrodynamics, and TPD in a permeable microchannel. It aims to solve a significant knowledge gap in microfluidics and thermal and mass transport. The governing equations are simplified by applying suitable similarity restrictions, and computing solutions to the resulting equations is done using the Runge‒Kutta Fehlberg fourth‒fifth-order scheme. The results are shown using graphs, and significant engineering metrics are analyzed. The outcomes show that increased Eckert number, magnetic, and porous factors will improve the thermal distribution. Quadratic thermal radiation shows the greater thermal distribution in the presence of these parameters, while Linear thermal radiation shows the least thermal distribution. The rate of thermal distribution is higher in the linear thermal distribution case and least in the nonlinear thermal radiation case in the presence of radiation and solid fraction factors. The outcomes of the present research are helpful in improving the thermal performance in microscale devices, electronic devices cooling, health care equipment, and other microfluidic applications.

Supply chains (SCs) serve many sectors that are, in turn, affected by e-commerce which rely on the make-to-order (MTO) system to avoid a risk in following the make-to-stoke (MTS) policy due to poor forecasting demand, which will be difficult if the products have short shelf life (e.g., refrigeration foodstuffs). The weak forecasting negatively impacts SC sectors such as production, inventory tracking, circular economy, market demands, transportation and distribution, and procurement. The forecasting obstacles are in e-commerce data types that are massive, imbalanced, and chaotic. Using machine learning (ML) algorithms to solve the problem works well because they quickly classify things, which makes accurate forecasting possible. However, it was found that the accuracy of ML algorithms varies depending on the SC data sectors. Therefore, the presented conceptual framework discusses the relations among ML algorithms, the most related sectors, and the effective scope of tackling their data, which enables the companies to guarantee continuity and competitiveness by reducing shortages and return costs. The data supplied show the e-commerce sales that were made at 47 different online stores in Egypt and the KSA during 413 days. The article proposes a novel mechanism that hybridizes the CatBoost algorithm with Dingo Optimization (Cat-DO), to obtain precise forecasting. The Cat-DO has been compared with other six ML algorithms to check its superiority over autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), deep neural network (DNN), categorical data boost (CatBoost), support vector machine (SVM), and LSTM-CatBoost by 0.52, 0.73, 1.43, 8.27, 15.94, and 13.12%, respectively. Transportation costs were reduced by 6.67%. [ABSTRACT FROM AUTHOR]

The manufactured products that rely on molten Acrylonitrile Butadiene Styrene (ABS) via Tederic machine (450 t) suffer from some types of defects such as black dots, shrinkage, and bubbles, which weaken the competitiveness. Therefore, this work proposes the Jidoka recruit's network system (JRNS) to set optimal operating parameters to reduce the processes' output variations and cut down on total process wastage autonomously. The suggested JRNS consists of two sequential stages. The first stage in front propagation relies on response surface methodology (RSM) in classifying the significant operating parameters from 15 candidates experimentally (DOE) to identify the most crucial one. The second stage predicts the processes' deviation by integrating two meta-heuristic methods called harmony search (HS) and weighted superposition attraction (WSPA) in the backpropagation to automatically reset the operating parameters to keep the product within the standard specification to avoid 11 defect chances. JRNS is innovative and has been used to upgrade autonomous control for enhancing the Tederic machine (450 t) process to reduce 70.98% of molding defects. The JRNS (RSM, WSPA + HS) increases the operation efficiency from 94% to 100% and reduces the defects per million opportunity to the six sigma scale. [ABSTRACT FROM AUTHOR]

Supply Chain Networks Distribution (SCND) topology aims to find the best position and size for facilities to ensure optimal products flow based on the Matheuristic approach (i.e. decomposition meta-heuristics). This problem is a multi-objective function designed to reduce the transported costs and associated delivery times. The Matheuristic presents a brilliant hybridize between the meta-heuristics steps and mathematical procedures in solving large-size problems with the slightest deviation ∅. This paper proposed an ant colony-based algorithm evolved by mathematical procedures called Mat-ACO, compared with SA "simulated annealing" and CA "Camel algorithm." The authors deduced that the mathematical solution is limited as the instances grow, significantly if increased than 600 network hotspots. The Mat-ACO, SA, and CA results are close to counterparts obtained by LINGO, with a difference of 2.03%, 2.49%, and 3.75%, respectively, and continue to extract results from more than 1350 network hotspots. The main contribution is to find the optimum tuning parameters, which will reduce the deviation from the exact solution. This paper reveals that no feasible solution can catch the LINGO at large-size problems. At the same time, the CA is superior to SA in the large problem sizes, while Mat-ACO still presents preferred solutions in minimum time. The proposed methodology is classified as a closed-loop network strategy that targets green management. [ABSTRACT FROM AUTHOR]