Sustainable assessment of plastic and mixed waste disposal problem during COVID-19 pandemic: an integrated multi-criteria decision-making approach.

This article focuses on India's inorganic solid waste disposal problem, with a particular emphasis on plastic and mixed waste. It aims to identify the current COVID-19 pandemic situation as well as provide a suitable disposal technique for wastes that are specifically related to municipal solid waste management. We propose an integrated approach to disposing of paper and plastic and mixed wastes in an interval-valued q-rung orthopair fuzzy (IVq-ROF) environment for this problem. In this case, we use the FUCOM method to calculate the weight values of the criteria and the MABAC method to rank the alternatives based on the chosen criteria. To confirm the effectiveness of the proposed method, a numerical illustration is provided, and validation of the suggested method is also shown.

Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19

Coronavirus Disease 2019 (COVID-19), a new illness caused by a novel coronavirus, a member of the corona family of viruses, is currently posing a threat to all people, and it has become a significant challenge for healthcare organizations. Robotics are used among other strategies, to lower COVID’s fatality and spread rates globally. The robot resembles the human body in shape and is a programmable mechanical device. As COVID is a highly contagious disease, the treatment for the critical stage COVID patients is decided to regulate through medication service robots (MSR). The use of service robots diminishes the spread of infection and human error and prevents frontline healthcare workers from exposing themselves to direct contact with the COVID illness. The selection of the most appropriate robot among different alternatives may be complex. So, there is a need for some mathematical tools for proper selection. Therefore, this study design the MAUT-BW Delphi method to analyze the selection of MSR for treating COVID patients using integrated fuzzy MCDM methods, and these alternatives are ranked by influencing criteria. The trapezoidal intuitionistic fuzzy numbers are beneficial and efficient for expressing vague information and are defuzzified using a novel algorithm called converting trapezoidal intuitionistic fuzzy numbers into crisp scores (CTrIFCS). The most suitable criteria are selected through the fuzzy Delphi method (FDM), and the selected criteria are weighted using the simplified best-worst method (SBWM). The performance between the alternatives and criteria is scrutinized under the multi-attribute utility theory (MAUT) method. Moreover, to assess the effectiveness of the proposed method, sensitivity and comparative analyses are conducted with the existing defuzzification techniques and distance measures. This study also adopt the idea of a correlation test to compare the performance of different defuzzification methods.

Analysis of Vaccine efficacy during the COVID-19 pandemic period using CSF-ELECTRE-I approach

COVID-19 vaccinations have been shown to be safe, efficacious, and life-saving. They, like other vaccines, do not entirely protect everyone who receives them, and no one knows how effectively they can prevent people from spreading the virus to others or whether the booster dosage is dangerous to some vulnerable people. So, in addition to getting vaccinated, we must continue with additional efforts to combat the pandemic. Quantitatively, the pragmatic, appropriate, and phenomenal mechanism of the complex spherical fuzzy set enhances the decision-making efficacy and the ordering quality of the ELECTRE I method to include a profitable and optimal approach for MAGDM. In the CSF environment, critically ill patients are investigated systematically using a pairwise comparison based ELECTRE-I technique. In this paper, we improve the precision of the CSF-based ELECTRE-I approach to an unique score function. The suggested approach’s comparability is examined with techniques that should provide equal importance to the alternatives, and the presented score function’s reliability is validated using the existing score function with the two cases.

A dual hesitant q-rung orthopair enhanced MARCOS methodology under uncertainty to determine a used PPE kit disposal.

Healthcare waste management is regarded as the most critical concern that the entire world is currently and will be confronted with in the near future. During the COVID-19 pandemic, the significant growth in medical waste frightened the globe, prompting it to investigate safe disposal methods. Plastics are developing as a severe environmental issue as a result of their increased use during the COVID-19 pandemic which has triggered a global catastrophe and prompted concerns about plastic waste management. One of the biggest challenges in this circumstance is the disposal of discarded PPE kits. The purpose of this research is to find a viable disposal treatment procedure for enhanced personal protective equipment (PPE) (facemasks, gloves, and other protective equipment) and other single-use plastic medical equipment waste in India during the COVID-19 crises, which will aid in effectively reducing their increasing quantity. To analyse the PPE waste disposal problem in India, we used the fuzzy Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) technique, which included the dual hesitant q-rung orthopair fuzzy set. The fuzzy Best Worst Method (BWM), which is compatible with the existing MCDM approaches, is used to establish the criteria weights. Sensitivity and comparative analyses are utilised to confirm the stability and validity of the proposed strategy.

Knowledge-based normative safety measure approach: systematic assessment of capabilities to conquer COVID-19.

In this research article, we have introduced a knowledge-based approach to regional/national security measures. Proposed Knowledge-based Normative Safety Measure algorithm for safety measures helps to take practical actions to conquer COVID-19. We analyzed based on five dimensions: the correlation between detected cases and confirmed cases, social distance, the speed of detected cases, the correlation between imported cases and inbound cases, and the proportion of masks worn. It prompts actions based on the security level of the region. Through the use of our proposed algorithm, the government has accelerated the implementation of social distancing, accelerated test cases, and policies, etc., to prevent people from contracting COVID-19. This idea can be a very effective way to realize the impending danger and take action in advance. Help speed up the process of controlling the COVID-19. In pandemic times, it can be helpful to understand better. Holding the normative safety measure at a high level leads nations to perform excellently on triple T's (testing, tracking, and treatment) policy and other safety acts. The proposed NSM approach facilitates for improve the governance of cities and communities.

An intuitionistic fuzzy decision support system for COVID-19 lockdown relaxation protocols in India.

In January 2020, the World Health Organization (WHO) identified a world-threatening virus, SARS-CoV-2. To diminish the virus spread rate, India implemented a six-month-long lockdown. During this period, the Indian government lifted certain restrictions. Therefore, this study investigates the efficacy of India's lockdown relaxation protocols using fuzzy decision-making. The decision-making trial and evaluation laboratory (DEMATEL) is one of the fuzzy MCDM methods. When it is associated with intuitionistic fuzzy circumstances, it is known as the intuitionistic fuzzy DEMATEL (IF-DEMATEL) method. Moreover, converting intuitionistic fuzzy into a crisp score (CIFCS) algorithm is an aggregation technique utilized for the intuitionistic fuzzy set. By using IF-DEMATEL and CIFCS, the most efficient lockdown relaxation protocols for COVID-19 are determined. It also provides the cause and effect relationship of the lockdown relaxation protocols. Additionally, the comparative study is carried out through various DEMATEL methods to see the effectiveness of the result. The findings would be helpful to the government's decision-making process in the fight against the pandemic.

The COVID-19 Vaccine Preference for Youngsters Using PROMETHEE-II in the IFSS Environment

Extensive decision-making during the vaccine preparation period is unpredictable. An account of the severity of the disease, the younger people with COVID-19 comorbidities and other chronic diseases are also at a higher risk of the COVID-19 pandemic. In this research article, the preference ranking structure for the COVID-19 vaccine is recommended for young people who have been exposed to the effects of certain chronic diseases. Multiple Criteria Decision-Making (MCDM) approach effectively handles this vague information. Furthermore, with the support of the Intuitionistic Fuzzy Soft Set (IFSS), the entries under the new extension of the Preference Ranking Organization Method for Enrichment Evaluation-II (PROMETHEE-II) is suggested for Preference Ranking Structure. The concept of intuitionistic fuzzy soft sets is parametric in nature. IFSS suggests how to exploit an intuitionistic ambiguous input from a decision-maker to make up for any shortcomings in the information provided by the decider. The weight of the inputs is calculated under the Intuitionistic Fuzzy Weighted Average (IFWA) operator, the Simply Weighted Intuitionistic Fuzzy Average (SWIFA) operator, and the Simply Intuitionistic Fuzzy Average (SIFA) operator. An Extended PROMETHEE-based ranking, outranking approach is used, and the resultant are recommended under the lexicographic order. Its sustainability and feasibility are explored for three distinct priority structures and the possibilities of the approach. To demonstrate the all-encompassing intuitionistic fuzzy PROMETHEE approach, a practical application regarding COVID-19 severity in patients is given, and then it is compared to other existing approaches to further explain its feasibility, and the sensitivity of the preference structure is examined according to the criteria.

Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period.

In this research article, we introduced an algorithm to evaluate COVID-19 patients admission in hospitals at source shortage period. Many researchers have expressed their conclusions from different perspectives on various factors such as spatial changes, climate risks, preparedness, blood type, age and comorbidities that may be contributing to COVID-19 mortality rate. However, as the number of people coming to the hospital for COVID-19 treatment increases, the mortality rate is likely to increase due to the lack of medical facilities. In order to provide medical assistance in this situation, we need to consider not only the extent of the disease impact, but also other important factors. No method has yet been proposed to calculate the priority of patients taking into account all the factors. We have provided a solution to this in this research article. Based on eight key factors, we provide a way to determine priorities. In order to achieve the effectiveness and practicability of the proposed method, we studied individuals with different results on all factors. The sigmoid function helps to easily construct factors at different levels. In addition, the cobweb solution model allows us to see the potential of our proposed algorithm very clearly. Using the method we introduced, it is easier to sort high-risk individuals to low-risk individuals. This will make it easier to deal with problems that arise when the number of patients in hospitals continues to increase. It can reduce the mortality of COVID-19 patients. Medical professionals can be very helpful in making the best decisions.