Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

This paper explores the numerical optimization of heat and mass transfer in the buoyancy-driven Al2O3-water nanofluid flow containing electrified Al2O3-nanoparticles adjacent to a symmetrically-vertical plane wall. The proposed model becomes a set of nonlinear problems through similarity transformations. The nonlinear problem is solved using the bvp4c method. The results of the proposed model concerning heat and mass transfer with nanoparticle electrification and buoyancy parameters are depicted in the Figures and Tables. It was revealed that the electrification of nanoparticles enhances the heat and mass transfer capabilities of the Al2O3 water nanoliquid. As a result, the electrification of nanoparticles could be an important mechanism to improve the transmission of heat and mass in the flow of Al2O3-water nanofluids. Furthermore, the numerical solutions of the nanofluid model of heat/mass transfer using the deep neural network (DNN) along with the procedure of Bayesian regularization scheme (BRS), DNN-BRS, was carried out. The DNN process is provided by taking eight and ten neurons in the first and second hidden layers along with the log-sigmoid function

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

Peer reviewed: TrueThis paper explores the numerical optimization of heat and mass transfer in the buoyancy-driven Al2O3-water nanofluid flow containing electrified Al2O3-nanoparticles adjacent to a symmetrically-vertical plane wall. The proposed model becomes a set of nonlinear problems through similarity transformations. The nonlinear problem is solved using the bvp4c method. The results of the proposed model concerning heat and mass transfer with nanoparticle electrification and buoyancy parameters are depicted in the Figures and Tables. It was revealed that the electrification of nanoparticles enhances the heat and mass transfer capabilities of the Al2O3 water nanoliquid. As a result, the electrification of nanoparticles could be an important mechanism to improve the transmission of heat and mass in the flow of Al2O3-water nanofluids. Furthermore, the numerical solutions of the nanofluid model of heat/mass transfer using the deep neural network (DNN) along with the procedure of Bayesian regularization scheme (BRS), DNN-BRS, was carried out. The DNN process is provided by taking eight and ten neurons in the first and second hidden layers along with the log-sigmoid function.</jats:p

Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling.

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year's Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted

Physicians’ knowledge and attitudes in Saudi Arabia regarding implantable cardiac defibrillators

Objectives: To evaluate knowledge and attitude of physicians involved in the management of patients with heart failure regarding implantable cardioverter-defibrillator (ICD). Methods: We conducted personal interviews with physicians involved in treating patients with heart failure. Between October 2015 and February 2016, the study was conducted in hospitals in the Riyadh region where no cardiac electrophysiology service was available. Every participant was met in person and received an oral questionnaire that aimed to assess basic knowledge regarding ICD indications and benefits. Results: Sixty-three physicians were met from 13 hospitals (14 consultants and 49 specialists). Forty-one percent of participants use the recommended cut-off level of left ventricular ejection fraction (LVEF) which is ≤35% as the LVEF criterion for ICD referral in patients with cardiomyopathy. Only 50% of the consultants use ≤35% as the LVEF criterion for ICD referral. Seventy percent of the participants thought that ICD may improve heart failure symptoms. Forty-eight percent of physicians have a defined channel to refer patients to higher centers for ICD implant. There was no statistically significant difference between physicians’ knowledge when we categorized them according to three different factors: (1) physician’s specialty (cardiology vs. internal medicine); (2) physician’s degree (consultant vs. specialist); and (3) physician’s location (inside vs. outside Riyadh city). Conclusion: There is a lack of knowledge of current clinical guidelines regarding ICD implantation for patients with heart failure at general hospitals in Saudi Arabia. This finding highlights the need to improve the dissemination of guidelines to practitioners involved in managing patients with heart failure in an effort to improve ICD utilization. Keywords: Cardiac defibrillator, Heart failure, Physicians’ knowledge, Saudi Arabi

Effects of Joule heating and reaction mechanisms on couple stress fluid flow with peristalsis in the presence of a porous material through an inclined channel

The objective of this study is to assess the flow behavior of the peristalsis mechanism of a couple stress fluid in incorporating a porous material. In addition, reaction mechanism and Ohmic heating are also taken into consideration with slip boundary conditions. For the purposes of mathematical simulation, we assume a long-wavelength approximation, ignoring the wave number and taking a low Reynolds number into account. The obtained outcome is shown in a graphical manner and then analyzed. The results of this investigation reveal that when the Hartmann number improves, the pattern of velocity noticeably decelerates. The Lorentz forces have a retarding impact on the velocity of the fluid from a physical standpoint. As the couple stress variable rises, so does the velocity of the fluid. As the couple stress component increases, the skin friction coefficient increases in one region of the fluid channel and falls in another region, between x = 0.5 and x = 1. As the thermal slip variable rises, more heat is transferred through the surface to the fluid, resulting in a rise in the temperature profile. When the couple stress variable is raised, the Nusselt number rises, while the thermal radiation factor causes the Nusselt number to decline. The results showed a positive relationship between the Sherwood number and the reaction mechanism parameter. This study demonstrates the potential use of this research in the fields of a career in engineering, namely, in enhancing hydraulic systems, as well as in medicine, particularly in optimizing gastrointestinal processes. The process of dissection facilitates the unimpeded circulation of blood and lymph inside the vascular system of the body, enabling the delivery of oxygen to tissues and the elimination of waste materials

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria&ndash;Lukman estimator, and Poisson modified Kibria&ndash;Lukman estimator. Despite different Poisson biased regression estimators being proposed, there has been no analysis of the robust version of these estimators to deal with the two above-mentioned problems simultaneously, except for the robust Poisson ridge regression estimator, which we have extended by proposing three new robust Poisson one-parameter regression estimators, namely, the robust Poisson Liu (RPL), the robust Poisson Kibria&ndash;Lukman (RPKL), and the robust Poisson modified Kibria&ndash;Lukman (RPMKL). Theoretical comparisons and Monte Carlo simulations were conducted to show the proposed performance compared with the other estimators. The simulation results indicated that the proposed RPL, RPKL, and RPMKL estimators outperformed the other estimators in different scenarios, in cases where both problems existed. Finally, we analyzed two real datasets to confirm the results

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria–Lukman estimator, and Poisson modified Kibria–Lukman estimator. Despite different Poisson biased regression estimators being proposed, there has been no analysis of the robust version of these estimators to deal with the two above-mentioned problems simultaneously, except for the robust Poisson ridge regression estimator, which we have extended by proposing three new robust Poisson one-parameter regression estimators, namely, the robust Poisson Liu (RPL), the robust Poisson Kibria–Lukman (RPKL), and the robust Poisson modified Kibria–Lukman (RPMKL). Theoretical comparisons and Monte Carlo simulations were conducted to show the proposed performance compared with the other estimators. The simulation results indicated that the proposed RPL, RPKL, and RPMKL estimators outperformed the other estimators in different scenarios, in cases where both problems existed. Finally, we analyzed two real datasets to confirm the results

Analysis of the convective heat transfer through straight fin by using the Riemann-Liouville type fractional derivative: Probed by machine learning

This work aims to analyze the transfer of heat through new fractional-order convective straight fins by using the Riemann-Liouville type fractional derivatives. The convection through the fins is considered in such a way that the thermal conductivity depends on the temperature. The transformed fractional-order problems are constituted through an optimization problem in such a way that the L2 norm remains minimal. The objective functions are further analyzed with the hybrid Cuckoo search (HCS) algorithm that use the artificial neural network (ANN) mechanism. The impacts of the fractional parameter β, the thermo-geometric parameter of fin ψ, and dimensionless thermal conductivity α are explained through figures and tables. The fin efficiency during the whole process is explained with larger values of ψ. It is found that the larger values of ψ decline the fin efficacy. The fractional parameter declines the thermal profile as we approach the integer order. The convergence of HCS algorithm is performed in each case study. The residual error touches E−14 for the integer order of α. The present results are validated through Table 6 by comparing with HPM, VIM and LHPM, while the error for HCS-ANN touches E−13. This proves that the proposed HCS is efficient

Exploring Families of Solitary Wave Solutions for the Fractional Coupled Higgs System Using Modified Extended Direct Algebraic Method

In this paper, we suggest the modified Extended Direct Algebraic Method (mEDAM) to examine the existence and dynamics of solitary wave solutions in the context of the fractional coupled Higgs system, with Caputo’s fractional derivatives. The method begins with the formulation of nonlinear differential equations using a fractional complex transformation, followed by the derivation of solitary wave solutions. Two-dimensional, Three-dimensional and contour graphs are used to investigate the behavior of traveling wave solutions. The research reveals many families of solitary wave solutions as well as their deep interrelationships and dynamics. These discoveries add to a better understanding of the dynamics of the fractionally coupled Higgs system and have potential applications in areas that use nonlinear Fractional Partial Differential Equations (FPDEs)

Melting Heat Transfer Rheology in Bioconvection Cross Nanofluid Flow Confined by a Symmetrical Cylindrical Channel with Thermal Conductivity and Swimming Microbes

Nonlinear thermal transport of non-Newtonian polymer flows is an increasingly important area in materials engineering. Motivated by new developments in this area which entail more refined and more mathematical frameworks, the present analysis investigates the boundary-layer approximation and heat transfer persuaded by a symmetrical cylindrical surface positioned horizontally. To simulate thermal relaxation impacts, the bioconvection Cross nanofluid flow Buongiorno model is deployed. The study examines the magnetic field effect applied to the nanofluid using the heat generated, as well as the melting phenomenon. The nonlinear effect of thermosolutal buoyant forces is incorporated into the proposed model. The novel mathematical equations include thermophoresis and Brownian diffusion effects. Via robust transformation techniques, the primitive resulting partial equations for momentum, energy, concentration, and motile living microorganisms are rendered into nonlinear ordinary equations with convective boundary postulates. An explicit and efficient numerical solver procedure in the Mathematica 11.0 programming platform is developed to engage the nonlinear equations. The effects of multiple governing parameters on dimensionless fluid profiles is examined using plotted visuals and tables. Finally, outcomes related to the surface drag force, heat, and mass transfer coefficients for different influential parameters are presented using 3D visuals