Rossthornton3188

The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.Given the importance that two-stage Data Envelopment Analysis (DEA) models have attained in recent years, this paper presents a systematic review of the literature on the topic focusing on the banking industry. We discuss the two-stage terminology itself, which is not yet not consolidated. We also discuss the current state-of-the-art and present opportunities, as well as challenges, for future studies. We analyse 59 papers, divided them into ten classes that cover various perspectives of two stage DEA studies, such as the economic context, geographic region of the banking units, methodological characteristics, and type of the models, either internal or external. Additionally, we investigate several controversial points regarding two-stage DEA models, such as the variable selection approach, the technique used in the second stage, and the possible impact of non-discretionary variables on efficiency. Results of the literature review indicate the lack of a uniform or universal terminology for two-stage DEA models in the baking industry. Moreover, the main objective of most papers involves extending or improving DEA models. Radial models, with variable returns of scale, and the intermediation approach are the most frequent configurations. Finally, we identify seven gaps in the literature for both internal and external two-stage DEA models and two specific gaps to external ones. Each gap is discussed in depth in the text and can be considered opportunities for future studies.The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.Spherical fuzzy sets (SFSs) have gained great attention from researchers in various fields. The spherical fuzzy set is characterized by three membership functions expressing the degrees of membership, non-membership and the indeterminacy to provide a larger preference domain. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and vagueness information. The similarity measure is one of the essential and advantageous tools to determine the degree of similarity between items. Several studies on similarity measures have been developed due to the importance of similarity measure and application in decision making, data mining, medical diagnosis, and pattern recognition in the literature. The contribution of this study is to present some novel spherical fuzzy similarity measures. We develop the Jaccard, exponential, and square root cosine similarity measures under spherical fuzzy environment. Each of these similarity measures is analyzed with respect to decision-makers' optimistic or pessimistic point of views. Then, we apply these similarity measures to medical diagnose and green supplier selection problems. These similarity measures can be computed easily and they can express the dependability similarity relation apparently.COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. Tacrolimus cost HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature segy outperforms recent techniques as it introduces the maximum accuracy rate.The Process Safety Management (PSM) systems at the operating facilities in the Oil & Gas and in Chemical manufacturing industries have matured over the years and have become, at most facilities, very robust and sophisticated. These programs are administrated by Process Safety (PS) teams at both the corporate business units and plant levels and have been effective in reducing the number and severity of PS events across the industries over the past 25 years or so. Incidents however are occurring at a regular interval and in recent times several noteworthy PS events have occurred in the United States which have brought into question the effectiveness of the PSM programs at play. These facilities have been applying their PSM programs with the expectation that the number and severity of PS events would decrease over time. The expected result has not been realized, especially in context to those facilities that have undergone the recent incidents. Current paper reviews a few publicly available PS performance reports of Oil & Gas and Chemical manufacturing industries.