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This study aims to explore the various factors influencing students in adopting e-learning in educational institutions in Saudi Arabia and analyze the relationship between factors of students' adoption of e-learning and student behavior intention. The study also analyzes perceived opportunities and challenges faced by students in adopting an e-learning system in higher education. A well-structured questionnaire was developed, and information was collected from 509 respondents. The study found that students' behavioral intention to adopt e-learning is highly influenced by performance expectancy, effort expectancy, social influence, facilitating conditions, computer self-efficacy, and internet knowledge. The study confirms the mediating role of student engagement that can act as an alternate path for strengthening the relationship between factors of adoption of e-learning and behavioral intention. In addition, several implications and new lines of investigation are recommended for meeting the educational transformation needs and future sustainability.

Multiple coating assessments of fiber optics utilizing micropolar convection non-Newtonian third-order liquid in the existence of Hall effect are examined and executed throughout this academic article. The wet-on-wet (WOW) coating process is used in the research. The fourth Runge–Kutta–Fehlberg algorithm is used to computationally solve the governing equations which dictate the movement of fluid inside the container. In this research, the RK4-Fehlberg algorithm is applied to get numerical results for a list of nonlinear ordinary differential equations (ODEs) describing liquid motion. Pictorially, the contribution of regulating variables on velocity and temperature profiles is examined. It is observed that the velocity profile enhances as the viscoelastic parameter increases and the velocity profile increases for both the non-Newtonian and Hall current increasing parameters in the presence and absence of magnetic parameter M. It is observed that the velocity of the fluid decreases with the increasing values of the Hartmann number m, Brinkman number Br, and magnetic parameter M. Furthermore, the temperature profile increase for BrBr, K, M, and opposite effect is observed for β\beta increases. The suggested approach is compared to homotopy analysis method (HAM) for verification purpose, and excellent agreement is obtained. In addition, as a restricted scenario, a connection is made with the existing literature.

This paper examines the volatility risk in the KSA stock market (Tadawul), with a specific focus on predicting volatility using the logarithm of the standard deviation of stock market prices (LSCP) as the output variable. To enhance volatility prediction, it proposes the combined use of the dynamic evolving neural fuzzy inference system (DENFIS) and the nonlinear spectral model, maximum overlapping discrete wavelet transform (MODWT). This study utilizes a dataset comprising 4609 observations and investigates the inputs of lag 1 of the close stock price (LCP), the natural logarithm of oil price (Loil), the natural logarithm of cost of living (LCL), and the interbank rate (IB), determined through autocorrelation (AC), partial autocorrelation (PAC), correlation, and Granger causality tests. Regression analysis reveals significant effects of variables on LSCP: LCP has a negative effect, and Loil has a positive effect in the ordinary least square (OLS) model, while LCL and IB have positive effects in the fixed effect model and negative effects in the random effect model. The MODWT-Haar-DENFIS model was developed as we found that the model has the potential to be an effective model for stock market forecasting. The results provide valuable insights for investors and policymakers, aiding in risk management, investment decisions, and the development of measures to mitigate stock market volatility. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve forecast accuracy. The results were compared from a number of mathematical models, which are the non-linear spectral model, autoregressive integrated moving average (ARIMA) model and EGARCH model. The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error (MAPE), Mean absolute scaled error (MASE) and Root means squared error (RMSE). [ABSTRACT FROM AUTHOR]

Good health and reduced inequality are factors of sustainable development. Healthcare systems are considered on68e of the most important activities of the creative economy that arise from research and development activities. Therefore, facilitating access to healthcare is one of the most important challenges guiding the development of the healthcare systems. Access is a complex concept and requires at least four aspects of evaluation. These include whether services are available, whether there is an adequate supply of services, whether people could obtain healthcare, and finally, evaluating whether a population may have access to services. Most countries are working hard to explore the means of providing better healthcare services to their population, especially in the pandemic age of crisis. The Kingdom of Saudi Arabia (KSA) is one such country that is continuously trying to enhance healthcare access to its citizens by adopting different means and policy interventions. The primary objective of this study is to assess whether gender differences exist with unmet healthcare needs among the citizens of the KSA. In this study, we examined the factors affecting the healthcare system in the Kingdom through access to and use of primary healthcare centres in urban and rural areas and whether there is a gender gap in access to healthcare services. In addition, we have tried to explore the current challenges faced by the healthcare system and key points about immediate measures to overcome the crisis in this sector. A well-structured questionnaire was designed covering different dimensions of the study objectives. The population of the study includes both male and female citizens of Makkah city of the KSA. In a survey of 529 respondents, it was found that people's access to the healthcare service system in the area is good. Test statistics confirm the significant difference in healthcare access across the gender categories of respondents. The availability of services, as well as the barriers to access, must be evaluated in the context of varied groups in society's differing perspectives, health requirements, and material and cultural surroundings. Some theoretical and managerial implications, limitations, and scope of future research are also presented in the study. [ABSTRACT FROM AUTHOR]

University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university's e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students' performance and then use artificial neural networks (ANNs) to predict students' performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU's Blackboard learning management system. The student's performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students' performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model's performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests. [ABSTRACT FROM AUTHOR]

In this study, we estimated the performance efficiency of the Jordanian mining and extracting sector based on Data Envelopment Analysis (DEA). The utilized dataset includes 6 out of 15 corporations that reflect around 90% of the total market capitalization under the mining and extracting sector in the Amman Stock Exchange (ASE). The sample consists of 126 observations from 2000 to 2020. It should be noted that estimating the efficiency of the sector based on time series for each company is not mentioned in the literature review. Therefore, we applied BCC (Banker–Charnes–Cooper) models to estimate performance efficiency and compared between input and output models under DEA. We also estimated the average performance efficiency of the sector to detect weaknesses/strengths among companies. The market capitalization and the operating revenue are used to evaluate the companies' performance. In addition to the performance variables as output to the DEA models, the current assets, non-current assets, operating expenses, and general administrative expenses are also used as input variables under the DEA models. This study also examined the effect of Gross Domestic Product (GDP) growth and Return on Assets (ROA) on performance efficiency scores for BCC models. In the results, we found that there are differences in performance efficiency across time series in each company based on dynamic BCC models. It is observed that the performance efficiency of NAST Company is better than the other companies based on BCC (Input/output). The GDP growth and ROA reveal the effect on efficiency performance under BCC models. The proposed model can be used to improve the performance efficiency of companies in stock exchange markets. [ABSTRACT FROM AUTHOR]

Higher-order correlations measured in lead collisions examined the geometrical strength as well as the coherence–chaotic characteristics of the particles creation region. The three- and four-particle quantum correlations can delineate the system of pions at small relative momentum region and observe a significant innovative penchant about the granular sources with various parameters. Such conglomerate of correlation functions and their corresponding genuine correlations are rarely analyzed at higher-order quantum computation via invariant momenta to search the granular structure of pion emanation sources. We evince and examine the pions excretion from a dispersed collection of fluid droplets with coherence snippets that produce quantum interferences to explore the nature of the fluid and the characteristics of the chaos fraction in collisions of heavy nuclei. The cumulant correlations and the normalized correlators with their intercepts contained the existence of amalgamation facet of hadronic particles, and the partons plasma. Specifically, we analyze such hybrid fluid with various correlations functions via droplet numbers to probe the trifle of chaos–coherence fraction of pions condensate by using the source with spherical structure droplets. [ABSTRACT FROM AUTHOR]

A brushless double-fed induction generator (BDFIG) has shown tremendous success in wind turbines due to its robust brushless design, smooth operation, and variable speed characteristics. However, the research regarding controlling of machine during low voltage ride through (LVRT) need greater attention as it may cause total disconnection of machine. In addition, the BDFIG based wind turbines must be capable of providing controlled amount of reactive power to the grid as per modern grid code requirements. Also, a suitable dynamic response of machine during both normal and fault conditions needs to be ensured. This paper, as such, attempts to provide reactive power to the grid by analytically calculating the decaying flux and developing a rotor side converter control scheme accordingly. Furthermore, the dynamic response and LVRT capability of the BDFIG is enhanced by using one of the very intelligent optimization algorithms called the Salp Swarm Algorithm (SSA). To prove the efficacy of the proposed control scheme, its performance is compared with that of the particle swan optimization (PSO) based controller in terms of limiting the fault current, regulating active and reactive power, and maintaining the stable operation of the power system under identical operating conditions. The simulation results show that the proposed control scheme significantly improves the dynamic response and LVRT capability of the developed BDFIG based wind energy conversion system; thus proves its essence and efficacy. [ABSTRACT FROM AUTHOR]

Chemical graph theory is a field of mathematical chemistry that links mathematics, chemistry, and graph theory to solve chemistry-related issues quantitatively. Mathematical chemistry is an area of mathematics that employs mathematical methods to tackle chemical-related problems. A graphical representation of chemical molecules, known as the molecular graph of the chemical substance, is one of these tools. A topological index (TI) is a mathematical function that assigns a numerical value to a (molecular) graph and predicts many physical, chemical, biological, thermodynamical, and structural features of that network. In this work, we calculate a new topological index namely, the Sombor index, the Super Sombor index, and its reduced version for chemical networks. We also plot our computed results to examine how they were affected by the parameters involved. This document lists the distinct degrees and degree sums of enhanced mesh network, triangular mesh network, star of silicate network, and rhenium trioxide lattice. The edge partitions of these families of networks are tabled which depend on the sum of degrees of end vertices and the sum of the degree-based edges. These edge partitions are used to find closed formulae for numerous degree-based topological indices of the networks. [ABSTRACT FROM AUTHOR]

The present work discusses the 2D unsteady flow of second grade hybrid nanofluid in terms of heat transfer and MHD effects over a stretchable moving flat horizontal porous plate. The entropy of system is taken into account. The magnetic field and the Joule heating effects are also considered. Tiny-sized nanoparticles of silicon carbide and titanium oxide dispersed in a base fluid, kerosene oil. Furthermore, the shape factors of tiny-sized particles (sphere, bricks, tetrahedron, and platelets) are explored and discussed in detail. The mathematical representation in expressions of PDEs is built by considering the heat transfer mechanism owing to the effects of Joule heating and viscous dissipation. The present set of PDEs (partial differential equations) are converted into ODEs (ordinary differential equations) by introducing suitable transformations, which are then resolved with the bvp4c (shooting) scheme in MATLAB. Graphical expressions and numerical data are obtained to scrutinize the variations of momentum and temperature fields versus different physical constraints. [ABSTRACT FROM AUTHOR]

The historical analysis demonstrates that plasma scientists produced a variety of numerical methods for solving "kinetic" models, i.e., the Vlasov-Maxwell (VM) system. Still, on the other hand, a significant fact or drawback of most algorithms is that they do not preserve conservation philosophies. This is a crucial fact that cannot be disregarded since the Vlasov Maxwell system is associated with conservation rules and is capable of assessing after the accomplishment of certain helpful mathematical actions. To examine the fractional-order routine of charged particles, we constructed a fractional-order plasma model and proposed a higher-order conservative numerical approach based on operational matrices theory and Shifted Gegenbauer estimations. Numerical convergence is investigated to confirm its competence and compatibility. This concept may be used in problems involving variable order and multidimensionality, such as those involving Vlasov and Boltzmann systems. [ABSTRACT FROM AUTHOR]

In this study, we have used Lie group analysis procedure to propose a novel model for transforming the governing equations of double diffusive MHD hyperbolic tangent fluid flow model into a system of nonlinear ordinary differential equations (ODEs). The solution of these equations is then investigated numerically by employing Shooting method. We also reported and presented our results graphically illustrating the results and analysis of physical parameters on concentration, velocity, and temperature profiles and on other physical quantities present in the flow model. The results show that fluid temperature increases with rise in the modified Dufour and velocity slip parameters whereas opposite behavior is observed for thermal slip parameter. Moreover, the Nusselt number declines with enhanced values of modified Dufour parameter whereas its opposite effect has been observed for Dufour-solutal Lewis number and Prandtl number. [ABSTRACT FROM AUTHOR]

In this work, fundamental flow problems, namely, Couette flow, fully developed plane Poiseuille flow, and plane Couette–Poiseuille flow of a third-grade non-Newtonian fluid between two horizontal parallel plates separated by a finite distance in a fuzzy environment are considered. The governing nonlinear differential equations (DEs) are converted into fuzzy differential equations (FDEs) and explain our approach with the help of the membership function (MF) of triangular fuzzy numbers (TFNs). Adomian decomposition method (ADM) is used to solve fundamental flow problems based on FDEs. In a crisp environment, the current findings are in good accord with their previous numerical and analytical results. Finally, the effect of the α -cut α ∈ 0,1 and other engineering constants on fuzzy velocity profile are invested in graphically and tabular forms. Also, the variability of the uncertainty is studied through the triangular MF. [ABSTRACT FROM AUTHOR]

Heat transfer investigation in the nanofluids is significant for real world applications. The investigation of heat transfer over a stretchable magnetized surface has broad applications in various industries. Therefore, heat transfer featuring in the nanofluid synthesized by various shaped Cu and H2O is organized over a shrinking surface. The problem is organized properly via similarity equations by inducing the influences of magnetic field. Then, OVIM is adopted and performed the solutions for the particular model. The results are furnished for the governing quantities over the feasible region and deeply discussed in the view of their physical significance. It is examined that the nanoliquids angular motion and shear stresses drops by strengthen magnetic field effects. Moreover, nanoliquid containing brick Cu-particles is better heat conductor and could be used broadly for industrial applications as for as heat transport concerned. In end, authentication of the study is provided by comparing the results with previous science literature and an excellent agreement is seems between them. [ABSTRACT FROM AUTHOR]

Geometry, electrostatics, and single-electron tunneling contribute to the nonlinearity in the quantum dot embedded nanomechanical resonator, while "Duffing term" is a kind of mathematics describing the third-order nonlinearity of the system as a whole. We study theoretically the influence of a variation of a mathematical parameter Fuffing term on the actual physical effect. The position probability distribution, the average current, and the displacement fluctuation spectrum with the different Duffing parameter and electromechanical coupling are obtained through numerically calculating the Fokker Planck equation. The mechanical bistability has been described by these quantities under different electromechanical coupling and Duffing parameters. We conclude that the nonlinearities of the nanotube resonator contribute to the mechanical bistability, which induces the asymmetry of the position probability distribution, compresses the current, and softens or stiffens the mechanical resonance frequency as the same as the electromechanical coupling to use it in mechanical engineering. [ABSTRACT FROM AUTHOR]

Schnakenberg model is known as one of the influential model used in several biological processes. The proposed model is an autocatalytic reaction in nature that arises in various biological models. In such kind of reactions, the rate of reaction speeds up as the reaction proceeds. It is because when a product itself acts as a catalyst. In fact, model endows fractional derivatives that got great advancement in the investigation of mathematical modeling with memory effect. Therefore, in the present paper, the authors develop a scheme for the solution of fractional order Schnakenberg model. The proposed model describes an auto chemical reaction with possible oscillatory behavior which may have several applications in biological and biochemical processes. In this work, the authors generalized the concept of integer order Schnakenberg model to fractional order Schnakenberg model. We provided the approximate solution for the underlying generalized nonlinear Schnakenberg model in the sense of Caputo differential operator via Laplace Adomian decomposition method (LADM). Furthermore, we established the general scheme for the considered model in the form of infinite series by the aforementioned technique. The consequent results obtained by the proposed technique ensure that LADM is an effective and accurate techniques to handle nonlinear partial differential equations as compared to the other available numerical techniques. Finally, the obtained numerical solution is visualized graphically by MATLAB to describe the dynamics of desired solution. [ABSTRACT FROM AUTHOR]

Normalized chaotic parameters examine the characterization of the particle production fluids produced at unusual energies and investigate a remarkable behavior in quantum measurement. The analogous characterization can be analyzed to probe the chaotic systems of boson particles creating sources of extraordinary energy. We observe that the bosons appear to be the appropriate aspirants of chaos fractions, and the normalized chaotic parameters evaluate the presence of such conglomerate phases significantly. The core point of this manuscript is that we calculate and examine the normalized chaotic parameters by differential equations to analyze the characteristics of the chaotic systems and their applications in thermal as well as in mechanical engineering. With such an efficient and distinctive approach, we perceive significant consequences for the correlator at higher temperature regimes. [ABSTRACT FROM AUTHOR]

In this study, a new computing model is developed using the strength of feed-forward neural networks with the Levenberg–Marquardt scheme-based backpropagation technique (NN-BLMS). It is used to find a solution for the nonlinear system obtained from the governing equations of the magnetohydrodyanmic (MHD) boundary layer flow over a stretching sheet. Moreover, the partial differential equations (PDEs) for the MHD boundary layer flow over a stretching sheet are converting into ordinary differential equations (ODEs) with the help of similarity transformation. A dataset for the proposed NN-BLMM-based model is generated at different scenarios by a variation of various embedding parameters: Deborah number β and magnetic parameter (M). The training (TR), testing (TS), and validation (VD) of the NN-BLMS model are evaluated in the generated scenarios to compare the obtained results with the reference results. For the fluidic system convergence analysis, a number of metrics, such as the mean square error (MSE), error histogram (EH), and regression (RG) plots, are utilized for measuring the effectiveness and performance of the NN-BLMS infrastructure model. The experiments showed that comparisons between the results of proposed model and the reference results match in terms of convergence up to E-02 to E-10. This proves the validity of the NN-BLMS model. Furthermore, the results demonstrated that there is a decrease in the thickness of the boundary layer by increasing the Deborah number and magnetic parameter. The importance of the experiment can be seen due to its industrial applications such as MHD power generation, MHD generators, and MHD pumps. [ABSTRACT FROM AUTHOR]

This research article deals with the nonlinear thermally radiated influences on non-Newtonian nanofluid considering Jeffrey fluid in a rotating system. The governing equations of the nanofluid have been transformed to a set of differential nonlinear equations, using suitable similarity variables. The Homotopy Analysis Method (HAM) and Runge–Kutta Method of order 4 (RK Method of order 4) are used for the solution of the modeled problem. The variation of the skin friction, Nusselt number, Sherwood number, and their impacts on the velocity distribution, temperature distribution, and concentration distribution have been examined. The influence of the Hall effect, rotation, Brownian motion, porosity, and thermophoresis analysis are also investigated. Moreover, for comprehension of the physical presentation of the embedded parameters, Deborah number β , viscosity parameter R , rotation parameter Kr , Brownian motion parameter Nb , porosity parameter γ , magnetic parameter M , Prandtl number Pr , thermophoretic parameter Nt , and Schmidt number Sc have been plotted and deliberated graphically. For large values of Brownian parameter, the kinetic energy increases, which in turn increases the temperature distribution, while the thermal boundary layer thickness decreases by increasing the radiation parameter, and the Hall parameter increases the motion of the fluid in horizontal direction. Also, the mass flux has been observed as a decreasing function at the lower stretching plate. [ABSTRACT FROM AUTHOR]

The current study provides an analytical approach to analyze the blood flow through a stenosed artery by using the Carreau fluid model. The flow governing equations are derived under the consideration of mild stenosis. Mathematical analysis has been carried out by considering the blood as non-Newtonian nature. Then, the analytical solution has been investigated by using the regular perturbation technique. The solutions obtained by this perturbation are up to the second-order in dimensionless Weissenberg number We . The performed computations of various parameter values such as velocity, wall shear stress, shear stress, and resistance impedance at the stenotic throat are discussed in detail for different values of Weissenberg number We . The obtained results demonstrate that for shear-thinning fluid, the fluid velocity increases with the increasing parameter m while opposite behavior is observed with the increase in We. Hence, the presented numerical analysis reveals many aspects of the flow by considering the blood as a non-Newtonian Carreau fluid model, and the presented model can be equally applicable to other bio-mathematical studies. [ABSTRACT FROM AUTHOR]

Online Food Delivery Platforms (OFDPs) has witnessed phenomenal growth in the past few years, especially this year due to the COVID-19 pandemic. This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers. The Presence of several multinational and national companies in this sector has enhanced the competition and companies are trying to adapt various marketing strategies and exploring the brand experience (BEX) dimension that helps in enhancing the brand equity (BE) of OFDPs. BEXs are critical for building brand loyalty (BL) and making companies profitable. Customers can experience different kinds of brand experiences through feeling, emotions, affection, behavior, and intellect. The present research work is taken up to analyze the factors affecting BEX and its impact on BL and BE of the OFDPs and analyze the mediating role of BL in the relationship between BEX and BE of the OFDPs in the Indian context. A survey of 457 Indian customers was carried out. A questionnaire was used for data collection and a mediation study was used to test hypothesized relationships. Our computational analysis reveals that BEX influences the BL and BE of OFDPs. The study further indicates that BL mediates the relationship between BEX and BE of OFDPs. The effective marketing and relationship management practices will help company to enhance BEX that will enable in enhancing BL and raising BE of their product. It therefore provides a more thorough analysis of BEX constructs and their consequences than previous research. Some of the managerial implication, limitations, and scope of future research are also presented in the study. [ABSTRACT FROM AUTHOR]

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms. A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method. The temperature of the base surface is higher than the fin surface, and the fin tip is kept adiabatic or cooled by convection heat transfer. The other pertinent parameters include Rayleigh number (100 ≤ Ra ≤ 104), Darcy number, (10−4 ≤ Da ≤ 10−2), relative thermal conductivity ratio of solid phase to fluid (1000 ≤ kr ≤ 8000), Nusselt number (10 ≤ Nu ≤ 100), porosity (0.1 ≤ φ ≤ 0.9). The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic algorithms. In computer science, metaheuristic algorithms are one of the most widely used techniques for optimization problems. In this research, three metaheuristic algorithms, including the firefly algorithm (FFA), particle swarm algorithm (PSO), and hybrid algorithm (FFA-PSO) are employed to examine the performance of square fins. It is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms. [ABSTRACT FROM AUTHOR]

This article deals with three-dimensional non-Newtonian Jeffrey fluid in rotating frame in the presence of magnetic field. The flow is studied in the application of Hall current, where the flow is assumed in steady states. The upper plate is considered fixed, and the lower is kept stretched. The fundamental equations are transformed into a set of ordinary differential equations (ODEs). A homotopy technique is practiced for a solution. The variation in the skin friction and its effects on the velocity fields have been examined numerically. The effects of physical parameters are discussed in various plots. [ABSTRACT FROM AUTHOR]

Loss given default (LGD) is a key parameter in credit risk management to calculate the required regulatory minimum capital. The internal ratings-based (IRB) approach under the Basel II allows institutions to determine the loss given default (LGD) on their own. In this study, we have estimated LGD for a credit portfolio data by using beta regression with precision parameter (O) and mean parameter (µ). The credit portfolio data was obtained from a banking institution in Jordan; for the period of January 2010 untilDecember 2014. In the first stage, we have used the "outstandingamount" and "amount of borrowing" to find LGD of each default borrower (494 out of 4393 borrower). In the second stage, we fit univariate parametric distributions to the LGD data to obtain the beta distribution. After that, we have estimated the values of O based on microeconomic variables (SPP, OE and LR). Moreover, we have estimated the values of µ based on macroeconomic variables (GDP and Inflation rate). Finally, we have compared between six different link functions (Logit, loglog, probit, cloglog, cauchit, and log), which have used with O and µ. The results show that Beta regression with probit link function has the highest R-squared with accepted measurements for logL, AIC and BIC. [ABSTRACT FROM AUTHOR]

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems. Based on mass, energy, and entropy balance, three models are developed for thermal resistance, hydraulic resistance, and entropy generation rate in the heat sink. The major axis is used as the characteristic length, and the maximum velocity is used as the reference velocity. The entropy generation rate comprises the combined effect of thermal resistance and pressure drop. The total EGR is minimized by utilizing the firefly algorithm. The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors. It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm. It is demonstrated that the performance of FFA is much better than PPA. [ABSTRACT FROM AUTHOR]

In this study, multilayer perception neural network (MLPNN) was employed to predict thermal conductivity of PVP electrospun nanocomposite fibers with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. This is the second attempt on the application of MLPNN with prey predator algorithm for the prediction of thermal conductivity of PVP electrospun nanocomposite fibers. The prey predator algorithm was used to train the neural networks to find the best models. The best models have the minimal of sum squared error between the experimental testing data and the corresponding models results. The minimal error was found to be 0.0028 for MWCNTs model and 0.00199 for Ni-Zn ferrites model. The predicted artificial neural networks (ANNs) responses were analyzed statistically using z-test, correlation coefficient, and the error functions for both inclusions. The predicted ANN responses for PVP electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters. [ABSTRACT FROM AUTHOR]

In this work, we studied the impacts of transmitting light, nonlinear thermal, and micropolar fluid mechanics on a wire surface coating utilizing non-Newtonian viscoelastic flow. Models with temperature-dependent variable viscosity were used. The boundary layer equations governing the flow and heat transport processes were solved using the Runge–Kutta fourth order method. A distinguished constituent of this study was the use of a porous matrix that acted as an insulator to reduce heat loss. In this paper we discuss the effects of numerous development parameters, including β 0 , Q ,   m ,   Ω ,   K p , and B r (non-Newtonian parameter, heat-producing parameter, viscosity parameter, variable viscosity parameter, porosity parameter, and Brinkman number, respectively). Furthermore, the effects of two other parameters, D and M, are also discussed as they relate to velocity and temperature distributions. We observed that the velocity profiles decreased with increasing values of K p . Fluid velocity increased as the values of M ,   B r ,   N , and D increased, while it decreased when the values of K p , Q and D increased. For increasing values of M , the temperature profile showed increasing behavior, while B r   and   Q showed decreasing behavior. Furthermore, the present work is validated by comparison with HAM and previously published work, with good results. [ABSTRACT FROM AUTHOR]

E-commerce industry has witnessed a phenomenal growth globally due to the sudden spread of the COVID-19 pandemic and the advancement of mobile Internet technology, with fast adaption of online shopping technologies by the customers. Previously, online shopping was only available in a few product categories and to a select group of consumers. The COVID-19 guidelines related to safety, physical distancing, closure, lockdown, and other restrictions have insisted that consumers shop online. Because of e-commerce growth, the grocery (FMCG) industry is also equipped with advanced technologies such as the Internet of Things (IoT), cloud computing, and block chain technology. This paper analyzes the UTAUT2 model and its influence on perceived risk and consumer trust in online purchase intention of grocery categories of products among Indian customers. We tried to analyze the growth potential of new technologies in grocery retail and formulated the hypotheses. The results showed that the spread of COVID-19 pandemic had a significant influence on the online shopping behavior of Indian customers. The outcome of the study partly assists businesses in understanding the impact of the factors of consumer adaption of technology, perceived risk associated with online transaction, consumer trust in online technologies and consumer online purchase intention of grocery products. To promote e-commerce in India, the current study suggests that marketers should try to develop consumer trust and lowering the perceived risk associated with online shopping. Some management implications and future area of study based on empirical findings are also highlighted in the present research work. [ABSTRACT FROM AUTHOR]

This study investigates how electronic work of mouth (eWOM) mediates the relationship between marketing relations and brand resonance. Based on the information obtained from 473 customers using an online questionnaire, this study analyses the relationship between eWOM, relationship marketing practices and the brand resonance of lifestyle products in an Indian context. The results from the multiple regression analysis indicate that the proposed hypotheses are valid, that relationship marketing significantly affects brand resonance, and that eWOM significantly mediates the relationship between the relationship marketing and brand resonance of branded apparel. The findings suggest that personalized attention and tangible rewards are effective relationship marketing strategies, and that these relationship-marketing practices—in association with eWOM—build up the strong brand resonance of branded apparel. The present study recommends that marketers should place emphasis on effective online and offline relationship marketing strategies, and should design appropriate eWOM strategies to enhance brand loyalty, brand attachment, brand community and brand engagement. Some of the managerial implications and the future scope of study based on the empirical findings are also highlighted in the present research work. [ABSTRACT FROM AUTHOR]

The exponential growth of advertising in enhancing consumer impulsivity has drawn the interest of many researchers to explore the various dimensions of advertising and its effective modeling. The branded apparel product market has grown significantly, a large number of competitors have entered into the market with variant quality, and there are a number of attractive advertising practices. The main aim of this study was to analyze the advertising practices and their relationship with consumer product involvement and the impulsive buying behavior of consumers in branded apparel in India. In a survey of 445 Indian customers, the study indicated that advertising practices significantly affect consumer involvement in branded apparel products and enhance their impulsivity toward products. The study also indicated that the level of consumer involvement in the branded apparel product significantly mediates the relationship between advertising practices and consumer impulsive buying behavior. Effective advertising practices will help companies to enhance consumer involvement that will enable branded apparel companies in enhancing consumer impulsiveness toward products. Some of the managerial implications, limitations, and scope of future research are also presented in the study. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models' performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN's prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021. [ABSTRACT FROM AUTHOR]

Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research. [ABSTRACT FROM AUTHOR]

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG's voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures. [ABSTRACT FROM AUTHOR]

Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers' consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson's chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms. [ABSTRACT FROM AUTHOR]

In this study, pressure-driven flow in the slip regime is investigated in rectangular microducts. In this regime, the Knudsen number lies between 0.001 and 0.1. The duct aspect ratio is taken as 0 ≤ ε ≤ 1 . Rarefaction effects are introduced through the boundary conditions. The dimensionless governing equations are solved numerically using MAPLE and MATLAB is used for artificial neural network modeling. Using a MAPLE numerical solution, the shear stress and heat transfer rate are obtained. The numerical solution can be validated for the special cases when there is no slip (continuum flow), ε = 0 (parallel plates) and ε = 1 (square microducts). An artificial neural network is used to develop separate models for the shear stress and heat transfer rate. Both physical quantities are optimized using a particle swarm optimization algorithm. Using these results, the optimum values of both physical quantities are obtained in the slip regime. It is shown that the optimal values ensue for the square microducts at the beginning of the slip regime. [ABSTRACT FROM AUTHOR]

A rectangular microchannel heat sink is modeled by employing thermal resistance and pressure drop networks. The available correlations for both thermal resistance and pressure drop are utilized in optimization. A multi-objective optimization technique, the prey–predator algorithm, is employed with the objective to find the optimal values for the heat sink performance parameters, i.e., thermal resistance and the pumping power of the heat sink. Additionally, a radial basis function neural network is used to investigate a relationship between these parameters. Full training based on the prey–predator algorithm with the sum of the squared error function is used to achieve the best performance of the model. The analysis of variance method is also employed to test the performance of this model. This study shows that the multi-objective function based on the prey–predator algorithm and the neural networks is suitable for finding the optimal values for the microchannel heat sink parameters. The minimum values of the multi-objective function are found to be “pumping power = 2.79344” and “total thermal resistance = 0.134133”. [ABSTRACT FROM AUTHOR]