Optimality of Solution with Numerical Investigation for Coronavirus Epidemic Model

The novel coronavirus disease, coined as COVID-19, is a murderous and infectious disease initiated from Wuhan, China. This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far. In this article, the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed. To counteract these types of diseases or restrict their spread, mankind depends upon mathematical modeling and medicine to reduce, alleviate, and anticipate the behavior of disease dynamics. The existence and uniqueness of the solution for the proposed system are investigated. Also, the solution to the considered system is made possible in a well-known functions space. For this purpose, a Banach space of function is chosen and the solutions are optimized in the closed and convex subset of the space. The essential explicit estimates for the solutions are investigated for the associated auxiliary data. The numerical solution and its analysis are the crux of this study. Moreover, the consistency, stability, and positivity are the indispensable and core properties of the compartmental models that a numerical design must possess. To this end, a nonstandard finite difference numerical scheme is developed to find the numerical solutions which preserve the structural properties of the continuous system. The M-matrix theory is applied to prove the positivity of the design. The results for the consistency and stability of the design are also presented in this study. The plausibility of the projected scheme is indicated by an appropriate example. Computer simulations are also exhibited to conclude the results.     

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months. A former study divided the disease into four main stages. These stages have proven helpful for doctors to analyze the liver’s condition. The traditional way to check the staging is the biopsy, which is a painful and time-consuming process. This article aims to provide an effective and efficient approach to predict hepatitis C staging. For this purpose, the proposed technique uses a fine Gaussian SVM learning algorithm, providing 97.9% accurate results.     

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents, economically damaging traffic jams, hijacking, motivating to wrong routes, and financial losses for businesses and governments. Smart and autonomous vehicles are connected wirelessly, which are more attracted for attackers due to the open nature of wireless communication. One of the problems is the rogue attack, in which the attacker pretends to be a legitimate user or access point by utilizing fake identity. To figure out the problem of a rogue attack, we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link. We consider the communication link between vehicle-tovehicle, and vehicle-to-infrastructure. We evaluate the performance of our proposed technique by measuring the rogue attack probability, false alarm rate (FAR), misdetection rate (MDR), and utility function of a receiver based on the test threshold values of reinforcement learning algorithm. The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.     

Time‐between‐events control charts for an exponentiated class of distributions of the renewal process

Time‐between‐events control charts are commonly used to monitor high‐quality processes and have several advantages over the ordinary control charts. In this article, we present some new control charts based on the renewal process, where a class of absolutely continuous exponentiated distributions is assumed for the time between events. This class includes the generalized exponential, generalized Rayleigh, and exponentiated Pareto distributions. Although we discuss the design structure for all the mentioned distributions, our main focus will be on the generalized exponential distribution due to its practical relevance and popularity. Since the generalized exponential distribution is a generalization of the traditional exponential distribution, the new control chart is more flexible than the existing exponential time‐between‐events charts. The control chart performance is evaluated in terms of some useful measures, including the average run length (ARL), the expected quadratic loss, continuous ranked probability, and the relative ARL. The effect of parameter estimation using the maximum likelihood and Bayesian methods on the ARL is also discussed in this article. The study also presents an illustrative example and 4 case studies to highlight the practical relevance of the proposal.     

Retention and stability of bioactive compounds in functional peach beverage using pasteurization,microwave and ultrasound technologies

Food Science and Biotechnology - The peach functional beverages pasteurized for 10 min at 90 °C, microwaved for 1.5 min at 850 W of power and sonicated for...     

Characteristics and gel properties of gelatin from goat skin as affected by spray drying

Gelatin powder from goat skin prepared by spray drying at various inlet temperatures (160–200°C) was characterized. Predominant particle sizes were in the range of 4.65–5.14?µm. Gelatin powder was mostly concave in shape with varying sizes, depending on inlet temperatures used. All gelatin powders were creamy whitish. Powder generally became more yellowish as the inlet temperature of spray drying increased (p?p?p?p?>?0.05). Goat skin gelatin spray dried with inlet temperatures of 160 or 180°C had higher gel strength than commercial bovine gelatin (p?相似文献   

Separation and identification of isomeric conjugated fatty acids by high-performance liquid chromatography with photodiode array detection

A simple, high-performance liquid chromatographic method is described for the separation of tetraenoic, trienoic and dienoic conjugated fatty acids on a Zorbax ODS reversed-phase column using acetonitrile/tetrahydrofuran (95∶5, vol/vol) at a flow rate of 1.2 mL/min as mobile phase. Also described is the separation of the isomeric conjugated fatty acids with acetonitrile/water/tetrahydrofuran (90∶90∶1, by vol) as mobile phase. The simultaneous detection and identification of the separated geometrical isomers in the eluant was accomplished using photodiode array detection.     

Heat transfer in pipe flow of a Johnson–Segalman fluid

A mathematical model for flow and heat transfer has been presented for Johnson–Segalman fluid in a pipe. Employing homotopy analysis method, the developed equations are solved analytically. The velocity and temperature fields are obtained. The dependence of the flow quantities on the material properties and Brinkman number is determined.