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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]

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]

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]

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]