Abstract

Pulsatile blood flow in a subject-specific human aortic arch and its major branches is studied computationally for a peak Reynolds number of 1553 and a Womersley number of 22.74. The aortic geometry is constructed from the CT-scan images of a subject. The aorta has out-of-plane curvature and significant area variation along the flow direction. A physiologically representative pulsatile velocity waveform is applied as boundary condition at the inlet of the aorta. The primary velocity profiles are skewed towards the inner wall of the ascending aorta during the entire cardiac cycle. In the decelerating phase, reverse flow is noted along the inner wall and the magnitude of maximum velocity is about 50 % of the peak flow condition. Flow separation is observed in the inner wall of the ascending aorta during the decelerating and reverse flow phases of the cardiac cycle. In the accelerating phase, however, flow separation does not occur. The major observation of the present work is the existence of complex and asymmetrical vortical flow structures which are not observed either in simple curved pipes or in idealized aortic arch computational studies. The relative strength of the secondary flow with respect to the primary flow is quantified by means of Relative Secondary Kinetic Energy whose highest value is evaluated to be 1.202 occurring near the entrance of the right carotid artery during the maximum reverse flow condition. High values of wall shear stress is observed at distal of the left and right subclavian arteries, the bifurcation of brachiocephalic artery between right subclavian artery and right carotid artery, and proximal inner wall of descending aorta during the cardiac cycle. The wall shear stress at the bifurcations of the branches are low and oscillatory and generally correlates with the preferential sites for atherosclerosis. The flow structures on the aorta wall are explicitly highlighted by the limiting streamlines. The application of limiting streamlines to clearly elucidate the complex on-wall flow structures is one of the key contributions of the present study. During the decelerating and reverse flow phases several critical points are observed on the aortic wall. These complex flow structures vanish during the accelerating phase. The observations made in the present study will be helpful in creating accurate and clinically useful computational models.

Abstract

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

Abstract

The article's purpose is twofold. First, we wish to draw attention to the insufficiently known field of continuous-time difference equations. These equations are paradigmatic for modeling complexity and chaos. Even the simplest equation , easily leads to complex dynamics, its solutions are perfectly suited to simulate strong nonlinear phenomena such as large-to-small cascades of structures, intermixing, formation of fractals, etc. Second, in the main body of the article we present a small but very important part of the theory behind the above equation marked by . Just as the discrete-time analog of this equation induces the one-dimensional dynamical system on some interval , so the equation induces the infinite-dimensional dynamical system on the space of functions . In the latter case, not only are the long-term behaviours of solutions critically dependent on the limit behaviour of the sequence (as in the discrete case) but also on the internal structure of as . Assuming to be continuous, we consider the iterations of as the semigroup generated by on the space of continuous maps, and introduce the notion of a limit semigroup for in a wider map space in order to investigate asymptotic properties of . We construct a limit semigroup in the space of upper semicontinuous maps. This enables us to describe both of the aforementioned aspects of our interest around the iterations of.

Abstract

In this paper, a new method of detection of election fraud is proposed. This method is based on the calculation of the ratio of two standard normal random variables; estimation of parameters of obtained sample and comparison of these estimates with known theoretical values of parameters. Also in the paper, there is an example of the application of the described method.

Abstract

Nowadays, graphs including bigraphs are mostly used in various real-world applications such as search engines and social networks. The bigraph or bipartite graph is a graph whose vertex set is split into two disjoint vertex sets such that there is no edge between the same vertex set. The bipartite graphs are colored using only two colors. This article checks if a given graph is bipartite or not and finds the color assignments of the bipartite graph using Java implementation.

Abstract

Recently graph coloring is applied in some real-world applications that involve different types of networks including bipartite graphs. There are two colors are used to color any bipartite graph in which the vertex set is colored with the same integer. This research develops an algorithm for coloring a bipartite graph and the results are tested on sample instances.

Abstract

Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.

Abstract

There are different traditional and nontraditional datasets available to investigate the performance of recommender systems. This article focuses on the different datasets required for the investigation of recommender systems.

Abstract

Electronics and computer engineering is a scientific and technical field in which electronic and computer systems are studied and their use. They are related to the technical professions within which electronic devices, systems, and computer programs are realized. The past half-century has been greatly marked by the development of electronics, computing, and their application in communications, medicine, and industry. This field has become the fundamental technical infrastructure of modern society. Today, there is almost no electronic device that does not use an electronic computer, and there is also not a single computer that is almost exclusively made of electronic components. This article explores the need for electronic components in digital computing.

Abstract

Everyone likes movies irrespective of color, gender, age, location, and race. The most important thing is how the users are getting our unique combinations of choices concerning the preferences of the movies. This article focuses on the creation of a movie recommendation system using item-based collaborative filtering.

Abstract

One of the most important applications of data science is the recommendation system. Every organization requires a good recommendation system to express a large range of items. This research focuses on the creation of a book recommender system using collaborative filtering.

Abstract

Graph coloring, an NP-complete problem is used in many real-world applications. The minimum color, that is, the chromatic number of a connected graph is determined using different soft computing strategies. This article gives some of the solutions obtained for queen graphs using evolutionary methods.

Abstract

Wireless networking is the connection of computers, digital communication devices, network equipment, and various other devices via radio waves. It is applied in places where the wired infrastructure cannot be installed or the price of introducing such a structure is too high. In addition, it has some features that are a great advantage over wired networking, such as customer mobility, easy expandability, and fast and low-cost temporary networking. Wireless technology allows us mobility and ease of use, but most users do not think about security. Users are insufficiently informed about the dangers of the Internet. Many of them do not pay attention to that and access important data such as bank accounts, e-mail, and any other contents that must be preserved and hidden. Today, there are more and more malicious actions, where hackers use various methods and technologies to attack users' accounts, bypassing all protections. Today, the issue of security is one of the priorities for every Internet user. Due to its characteristics, wireless communication is exposed to attacks due to the way they are sent, and there is a possibility of intercepting information.

Abstract

In this study, the system identification of a multiple input multiple-output (MIMO) water tank has been investigated. The given experimental system is a first-order system. Its transfer function was evaluated and the time constant was predicted. Once the parameters of the transfer functions (delay, gain, and time constant) were determined for each step input. This model each of the step responses using MatLab’s Simulink. These simulated responses were then compared to the observed experimental responses.

Abstract

Recently recommender systems are developed for a wide variety of applications. This article focuses on the applications, real-world examples, types, and analysis of various recommender systems.

Abstract

The task of recommending products to customers based on their interests is important in business. It is possible to accomplish this with machine learning. To reduce human effort by proposing movies based on the user's interests efficiently and effectively without wasting much time in pointless browsing, the movie recommendation system is designed to assist movie aficionados. This work focuses on developing a movie recommender system using a model that incorporates both cosine similarity and sentiment analysis. Cosine similarity is a standard used to determine how similar two items are to one another. An examination of the emotions expressed in a movie review can determine how excellent or negative a review is and, consequently the overall rating for a film. As a result, determining whether a review is favorable or adverse may be automated because the machine learns by training and evaluating the data. Comparing different systems based on content-based approaches will produce results that are increasingly explicit as time passes.

Abstract

An algorithm is defined as a finite step-by-step procedure to accomplish a required result. It is also defined as a sequence of computational operations that convert the given input into the required output. In general, in the worst case, an algorithm is said to be optimal if there are no algorithms that perform a less basic number of well-defined operations, in the worst case. This paper presents an optimal algorithm for finding the second smallest among n numbers. The complexity of the proposed algorithm and its advantages are also analyzed in this paper.

Abstract

Nowadays, recommendation systems are existing everywhere in the internet world, online people are presented with the required needs not just for actual physical products, but also for several other things such as songs, places, books, friends, movies, and many more requirements. Most of the systems are developed with the basic collaborative and hybrid filtering, where the people or users are recommended items that the choices are based on the right preferences of other people by applying the machine intelligence strategies. In this research, the importance of network modeling is analyzed in solving real-world problems.