An epidemiological model for computer virus with Atangana-Baleanu fractional derivative

The Era of data is transubstantiating into a Big Data model in this technological world in the early 21st century. In 2005, Roger Mougalas coined a combination of data for this future world of the human race. The information helps to find specific solutions for any physical problem under Catastrophic circumstances in high populations such as Covid-19. To store massive data and historical events in a computer, the possibility of damage occurred to the complete data. Hence, viruses are a crucial threat to such data worth millions and billions. For this purpose, we spend enormous costs and efforts to build defensive strategies to save that information. Analyzing the expansion and extension of viruses helps to protect data and prevent viruses. In this manuscript, we study optimal control analysis for the suggested model in the sense of the Atangana-Baleanu derivative (AB-derivative). We employed a fixed point theorem to analyze the solutions for the fractional order computer virus model. We verified the results numerically and expressed them graphically.

Study of fractional order dynamics of nonlinear mathematical model

This manuscript is devoted to establishing some theoretical and numerical results for a nonlinear dynamical system under Caputo fractional order derivative. Further, the said system addresses an infectious disease like COVID-19. The proposed system involves natural death rates of susceptible, infected and recovered classes respectively. By using nonlinear analysis feasible region and boundedness have been established first in this study. Global and Local stability analysis along with basic reproduction number have also addressed by using the next generation matrix method. Upon using the fixed point approach, existence and uniqueness of the approximate solution for the mentioned problem has also investigated. Some stability results of Hyers-Ulam (H-U) type have also discussed. Further for numerical treatment, we have exercised two numerical schemes including modified Euler method (MEM) and nonstandard finite difference (NSFD) method. Further the two numerical schemes have also compared with respect to CPU time. Graphical presentations have been displayed corresponding to different fractional order by using some real data.

Clinicians as Leaders: Impact and Challenges

Healthcare has always been a complex system phenomenon which needs accountability from the leading clinical and management roles. Adequate and competent leadership is recognized as a driving point for a successful healthcare division. Medical professionals taking charge for quality improvement are well placed yet the style to deliver their leadership qualities bears a massive significance. While exceptional clinical prowess is of tremendous importance, harmonious teamwork, inefficiency reduction and patient communication and safety lead to noteworthy health management outcomes. Explores not explains the importance of leadership, qualities and styles of a leader, various leadership theories, the impact of leadership in medical education and the current issues related to medical leadership.

Pathogen-sugar interactions revealed by universal saturation transfer analysis.

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an "end-on" manner. uSTA-guided modeling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis.

Harvesting Energy from Ocean: Technologies and Perspectives

The optimal utilization of renewable energies is a crucial factor toward the realization of sustainability and zero carbon in a future energy system. Tidal currents, waves, and thermal and salinity gradients in the ocean are excellent renewable energy sources. Ocean tidal, osmotic, wave, and thermal energy sources have yearly potentials that exceed the global power demand of 22,848 TWh/y. This paper extensively reviews the technologies related to energy harvesting from waves, tidal, ocean thermals, and the salinity gradient. Moreover, the socio-economic, social, and environmental aspects of the above technologies are also discussed. This paper provides a better picture of where to invest in the future energy market and highlights research gaps and recommendations for future research initiatives. It is expected that a better insight into ocean energy and a deep understanding of various potential devices can lead to a broader adoption of ocean energy. It is also clear that further research into control strategies is needed. Policy makers should provide financial support for technologies in the demonstration stage and employ road mapping to accelerate the cost and risk reductions to overcome economic hurdles. To identify traditional and online sources on the topic, the authors used electronic databases and keyword searching approaches. Among them, the International Renewable Energy Agency data were the primary database utilized to locate sources.

A Study of the Understanding of Patients Who Have Recovered from COVID-19: The Case of Civil Hospital Jhangi Wala, District Bahawalpur, Punjab, Pakistan

This study is an attempt to understand the coronavirus and COVID-19 from the viewpoint of recovered patients at Civil Hospital Jhangi Wala, District Bahawalpur. It investigates the feelings and experiences of those patients who suffered due to being infected with the novel coronavirus and who ultimately defeated the disease. A total of 310 Corona patients shared their feelings, symptoms, coping strategies, level of fear, and perceptions about the disease. The study used inferential statistics to quantify the data and determine the results. The study concluded that those people who were unafraid of the disease and who did not follow the Corona SOPs got the infection in the district of Bahawalpur. By profession, they were shopkeepers and laborers. The majority of the patients were those who had co-morbidities such as asthma and diabetes. The study recommended that those who are feeling symptoms of the flu such as fever, sneezing, loss of taste, loss of smell, sore throat, and/or having difficulty breathing must observe social distances and quarantine themselves for proper curation. [ FROM AUTHOR] Copyright of Journal of Hospital Librarianship is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

Hospital admission and care of COVID-19 patients problem based on spherical hesitant fuzzy decision support system

Abstract The emergency response to the health care management in the hospital do not have enough systems for providing medical service to the COVID19 patients (e.g., scheduled or nonemergency). Therefore, in this paper, we developed an emergency decision support model for consideration of patients care and admission scheduling (PCAS). The complex decision support model assigns a set of patients into a number of restricted resources like rooms, time slots, and beds depending on satisfying a number of predefined constraints such as disease severity, waiting time, and disease types. This is a crucial issue with multi-criteria decision making (MCDM). In this paper, we first begin an assessment into the admission and care to tackle this issue and collect four factors effecting the admission and care of COVID-19 patients that form a system of criteria. While there is a lot of vague and uncertain data that can be effectively depicted for these indicators by the spherical hesitant fuzzy set, then, we implement a strong MCDM method based on list of aggregation operators to address the patients' hospital admission and care. Last of all, a numerical real-life application about PCAS is provided to demonstrate the validity of the proposed approaches along with relevant discussions, the merits of proposed approaches are also analyzed by validity test. The proposed methodology has been shown to help hospitals manage the admissions and care of COVID-19 patients in a flexible manner.

A fractional order Covid-19 epidemic model with Mittag-Leffler kernel.

In this article, we are studying fractional-order COVID-19 model for the analytical and computational aspects. The model consists of five compartments including; ` ` S c ″ which denotes susceptible class, ` ` E c ″ represents exposed population, ` ` I c ″ is the class for infected people who have been developed with COVID-19 and can cause spread in the population. The recovered class is denoted by ` ` R c ″ and ` ` V c ″ is the concentration of COVID-19 virus in the area. The computational study shows us that the spread will be continued for long time and the recovery reduces the infection rate. The numerical scheme is based on the Lagrange's interpolation polynomial and the numerical results for the suggested model are similar to the integer order which gives us the applicability of the numerical scheme and effectiveness of the fractional order derivative.

Stability analysis of fractional nabla difference COVID-19 model.

Microorganisms lives with us in our environment, touching infectious material on the surfaces by hand-mouth which causes infectious diseases and some of these diseases are rapidly spreading from person to person. These days the world facing COVID-19 pandemic disease. This article concerned with existence of results and stability analysis for a nabla discrete ABC-fractional order COVID-19. The nabla discrete ABC-fractional operator as more general and applicable in modeling of dynamical problems due to its non-singular kernel. For the existence and uniqueness theorems and Hyers-Ulam stability, we need to suppose some conditions which will play important role in the proof of our main results. At the end, an expressive example is given to provide an application for the nabla discrete ABC-fractional order COVID-19 model.

Haar wavelet collocation approach for the solution of fractional order COVID-19 model using Caputo derivative

This article is devoted to study a compartmental mathematical model for the transmission dynamics of the novel Coronavirus-19 under Caputo fractional order derivative. By using fixed point theory of Schauder’s and Banach we establish some necessary conditions for existence of at least one solution to model under investigation and its uniqueness. After the existence a general numerical algorithm based on Haar collocation method is established to compute the approximate solution of the model. Using some real data we simulate the results for various fractional order using Matlab to reveal the transmission dynamics of the current disease due to Coronavirus-19 through graphs.

Fractional order mathematical modeling of COVID-19 transmission.

In this article, the mathematical model with different compartments for the transmission dynamics of coronavirus-19 disease (COVID-19) is presented under the fractional-order derivative. Some results regarding the existence of at least one solution through fixed point results are derived. Then for the concerned approximate solution, the modified Euler method for fractional-order differential equations (FODEs) is utilized. Initially, we simulate the results by using some available data for different fractional-order to show the appropriateness of the proposed method. Further, we compare our results with some reported real data against confirmed infected and death cases per day for the initial 67 days in Wuhan city.

Computational and theoretical modeling of the transmission dynamics of novel COVID-19 under Mittag-Leffler Power Law

In the current article, we studied the novel corona virus (2019-nCoV or COVID-19) which is a threat to the whole world nowadays. We consider a fractional order epidemic model which describes the dynamics of COVID-19 under nonsingular kernel type of fractional derivative. An attempt is made to discuss the existence of the model using the fixed point theorem of Banach and Krasnoselskii's type. We will also discuss the Ulam-Hyers type of stability of the mentioned problem. For semi analytical solution of the problem the Laplace Adomian decomposition method (LADM) is suggested to obtain the required solution. The results are simulated via Matlab by graphs. Also we have compare the simulated results with some reported real data for Commutative class at classical order.