ABSTRACT
The health organizations around the world are currently facing one of the greatest challenges, to overcome the current global pandemic, COVID-19. It erupted in December 2019, in Wuhan City, China. It spreads rapidly throughout the world within couple of months. In this paper, the data of the COVID-19 have been collected, organized, analyzed and interpreted using the discrete-time model of SIR epidemic model. Moreover, results for several countries from different regions of the world have been obtained. Furthermore, comparative study has been carried out for the countries under consideration. The comparison was performed for the data of different countries on same dates of each month. However, the calculations are carried out for thirteen consecutive weeks, to investigate the rate of spread and the control of the disease in these countries. This guides us to some important concepts like factors favoring the spread of virus and those resisting the spread. Different regions are studied and their data have been evaluated to know which regions are the most effected. This study helps to know the important factors about the behavior of the coronavirus in different environments, such as lockdowns, temperatures, humidity and other restrictions. The proposed concepts and equations can be used to project the upcoming behavior of the pandemic.
ABSTRACT
In modern times, the organizational managements greatly depend on decision-making (DM). DM is considered the management's fundamental function that helps the businesses and organizations to accomplish their targets. Several techniques and processes are proposed for the efficient DM. Sometimes, the situations are unclear and several factors make the process of DM uncertain. Fuzzy set theory has numerous tools to tackle such tentative and uncertain events. The complex picture fuzzy set (CPFS) is a super powerful fuzzy-based structure to cope with the various types of uncertainties. In this article, an innovative DM algorithm is designed which runs for several types of fuzzy information. In addition, a number of new notions are defined which act as the building blocks for the proposed algorithm, such as information energy of a CPFS, correlation between CPFSs, correlation coefficient of CPFSs, matrix of correlation coefficients, and composition of these matrices. Furthermore, some useful results and properties of the novel definitions have been presented. As an illustration, the proposed algorithm is applied to a clustering problem where a company intends to classify its products on the basis of features. Moreover, some experiments are performed for the purpose of comparison. Finally, a comprehensive analysis of the experimental results has been carried out, and the proposed technique is validated.
Subject(s)
Algorithms , Fuzzy Logic , Cluster Analysis , UncertaintyABSTRACT
In today’s world, the countries that have easy access to energy resources are economically strong, and thus, maintaining a better geopolitical position is important. Petroleum products such as gas and oil are currently the leading energy resources. Due to their excessive worth, the petroleum industries face many risks and security threats. Observing the nature of such problems, it is asserted that the complex bipolar fuzzy information is a better choice for modeling them. Keeping the said problem in mind, this article introduces the novel structure of complex bipolar fuzzy relation (CBFR), which is basically used to find out the relationships between complex bipolar fuzzy sets (CBFSs). Similarly, the types of CBFRs are also defined, which is helpful during the process of analyzing and interpreting the problem. Moreover, some useful results and interesting properties of the proposed structures are deliberated. Further, a new modeling technique based on the proposed structures is initiated, which is used to investigate the security risks to petroleum industries. Furthermore, a detailed comparative analysis proves the advantages and supremacy of CBFRs over other structures. Therefore, the results achieved by the proposed methods are substantially reliable, practical and complete.
ABSTRACT
The emergence of SARS-CoV-2 has been reported during December 2019, in the city of Wuhan, China. The transmission of this virus via human to human interaction has already been described. The novel virus has become pandemic and declared as a comprehensive emergency worldwide by World Health Organization due to its exponential spread within and outside China. There is a need of time to create a therapeutic agent and a vaccine to cure and control this lethal SARS-CoV-2. Conventionally, the vaccine development process is time taking, tiresome and requires more economical inputs with manpower. However, bioinformatics offers a key solution to compute the possibilities. The present study focuses on the utilization of bioinformatics platforms to forecast B and T cell epitopes that belong to SARS-CoV-2 spike glycoprotein. The protein is thought to have an involvement in triggering of momentous immune response. NCBI database was explored to collect the surface glycoprotein sequence and was analyzed to determine the immunogenic epitopes. This prediction analysis was carried out using IEDB web based server and the prediction of protein structure was done by homology modeling approach. This study resulted in prediction of 5 T cell and 13 B cell epitopes. Moreover, GPGPG linker was used to make these predicted epitopes a single peptide prior to further analysis. Afterwards, a 3D model of the final vaccine peptide was constructed, and the structure quality of the final construct was checked by Ramachandran Plot analysis and ProSA-web. Moreover, docking analysis highlighted three interactions of epitope against HLA-B7 including Lys 178, Gol 303 and Thr 31 residues. In conclusion, the predicted multi epitope peptide can be suggested as therapeutic or prophylactic candidate vaccine against SARS-CoV-2 after further confirmation by immunological assays.
ABSTRACT
As the COVID-19 pandemic has spread across the globe, questions have arisen about the approach healthcare systems should adopt in order to optimally manage patient influx. With a focus on the impact of COVID-19 on the NHS, we describe the frontline experience of a severely affected hospital in close proximity to London. We highlight a protocol-driven approach, incorporating the use of CT in the rapid triage, assessment and cohorting of patients, in an environment where there was a lack of readily available, onsite RT-PCR testing facilities. Furthermore, the effects of the protocol on the effective streamlining of patient flow within the hospital are discussed, as are the resultant improvements in clinical management decisions within the acute care service. This model may help other healthcare systems in managing this pandemic whilst assessing their own needs and resources.