Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach.

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

A systematic review of K-12 education responses to emergency remote teaching during the COVID-19 pandemic.

Emergency remote teaching (ERT) has potential for transforming future instruction and learning across the K-12 educational domain. The study presented here evaluated empirical evidence from peer-reviewed literature pertaining to the challenges and opportunities experienced by teachers and students during the implementation of ERT prompted by the COVID-19 pandemic. To locate relevant reports and research, the authors explored three databases: Web of Science, ScienceDirect and Scopus. Based upon predefined selection criteria, they selected 51 studies for thematic and content analysis. Next, they developed a taxonomy which comprised three categories: (1) K-12 education responses to ERT; (2) educational inequality; and (3) learning outcomes. Using this taxonomy, the authors conducted a deep analysis and critical review to highlight multiple challenges and critical gaps in the literature surrounding ERT in K-12 education settings. Their review reveals innovative strategies for overcoming obstacles to technological readiness, online learning adaptation and teachers' and students' physical and mental health. This knowledge will be valuable to policymakers, researchers, practitioners and educational institutions in reducing the adverse effects of catastrophic situations on childhood education in the future.

The Influence of the Barriers of Hybrid Strategy on Strategic Competitive Priorities: Evidence from Oil Companies.

This study examines the impact of the barrier of adopting hybrid strategy on strategic performance using the oil sector in Iraq as a case. International oil companies consider various strategies in order to achieve superior performance. The procedure needs to overcome certain essential barriers for the adoption of the hybrid strategy that combines the cost leadership and differentiation strategy. The questionnaire was distributed online due to the COVID-19 pandemic that led to the closure of companies in the country. Out of the 537 questionnaires answered, 483 were used for further analysis which yielded usable response rate of 90%. The structural equation modeling results confirmed that the high costs of technologies, the priority of other external matters, inadequate industry regulation, insufficient supply, organizational capabilities, strategic capabilities, and financial capabilities are significantly related to strategic performance. The researchers recommend conducting an in-depth study of the phenomenon based on theoretical and empirical foundations, especially considering the relationship between the barriers of a hybrid strategy and strategic performance based on linear and non-compensatory relationships. This research sheds light on the barriers to adopting the hybrid strategy required by the oil sector as it relies on continuous production.

A systematic review of K–12 education responses to emergency remote teaching during the COVID-19 pandemic

Emergency remote teaching (ERT) has potential for transforming future instruction and learning across the K–12 educational domain. The study presented here evaluated empirical evidence from peer-reviewed literature pertaining to the challenges and opportunities experienced by teachers and students during the implementation of ERT prompted by the COVID-19 pandemic. To locate relevant reports and research, the authors explored three databases: Web of Science, ScienceDirect and Scopus. Based upon predefined selection criteria, they selected 51 studies for thematic and content analysis. Next, they developed a taxonomy which comprised three categories: (1) K–12 education responses to ERT;(2) educational inequality;and (3) learning outcomes. Using this taxonomy, the authors conducted a deep analysis and critical review to highlight multiple challenges and critical gaps in the literature surrounding ERT in K–12 education settings. Their review reveals innovative strategies for overcoming obstacles to technological readiness, online learning adaptation and teachers' and students' physical and mental health. This knowledge will be valuable to policymakers, researchers, practitioners and educational institutions in reducing the adverse effects of catastrophic situations on childhood education in the future.

The Influence of the Barriers of Hybrid Strategy on Strategic Competitive Priorities: Evidence from Oil Companies

This study examines the impact of the barrier of adopting hybrid strategy on strategic performance using the oil sector in Iraq as a case. International oil companies consider various strategies in order to achieve superior performance. The procedure needs to overcome certain essential barriers for the adoption of the hybrid strategy that combines the cost leadership and differentiation strategy. The questionnaire was distributed online due to the COVID-19 pandemic that led to the closure of companies in the country. Out of the 537 questionnaires answered, 483 were used for further analysis which yielded usable response rate of 90%. The structural equation modeling results confirmed that the high costs of technologies, the priority of other external matters, inadequate industry regulation, insufficient supply, organizational capabilities, strategic capabilities, and financial capabilities are significantly related to strategic performance. The researchers recommend conducting an in-depth study of the phenomenon based on theoretical and empirical foundations, especially considering the relationship between the barriers of a hybrid strategy and strategic performance based on linear and non-compensatory relationships. This research sheds light on the barriers to adopting the hybrid strategy required by the oil sector as it relies on continuous production.

Encountering Covid-19 and perceived stress and the role of a health climate among medical workers.

Due to the outbreak of Covid-19 epidemic, work stress among health sector employees has risen too high. The study aims at determining the effect of the current coronavirus epidemic in the form of stress perceived among the medical workers in Pakistan and to discover the moderating role played by a healthy climate in offsetting it. The data was collected from 255 medical workers through a self-administered online questionnaire. Multiple Hierarchical Regression was used as a tool to test the hypotheses of the study. The results obtained indicate a correlation between the pandemic and the stress caused by it among the health workers, whereas, the role of a wholesome climate in the reduction of stress among them was found lacking. Sub-hypotheses indicate that the healthy environment provided by supervisors is effective in reducing the impact of workers' handling of the Covid-19 epidemic and perceived stress, while the healthy environment provided by hospitals in general or by workgroups fails to cause such positive change. This revelation necessitates the adoption of compulsory precautionary measures on the part of relevant authorities, because increase in stress caused by the pandemic can prove more lethal than the pandemic itself. The threat of the coronavirus pandemic has emerged as a massive socio-economic challenge for the global community, especially for the developing countries like Pakistan which faces serious socio-economic challenges in the current scenario. On account of the similarity of situations, the results obtained through this study can be safely generalized to other developing countries, particularly from the South Asian region.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review.

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.

Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods

Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts’ perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results.

Rise of multiattribute decision‐making in combating COVID‐19: A systematic review of the state‐of‐the‐art literature

Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients.

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses.

Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase 'development' presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of 'recipients list' and 'COVID-19 distribution criteria' was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria.

Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients.

Introduction: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. Conclusion: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Understanding employees’ responses to the COVID-19 pandemic: The attractiveness of healthcare jobs

The purpose of this study was to examine the impact of COVID-19 on the attractiveness of work in the Iraqi health sector by looking at the mediating role of employees' attitudes. A questionnaire survey was used to collect data from 218 health sector employees. The results revealed that there is a significant impact of COVID-19 pandemic on employees' attitudes that influence their decision to quit the health sector and look for jobs in other sectors. As health sector employees are prone to suffer the most in the pandemic, this crisis significantly affected the attractiveness of jobs in the health sector, leading to an increase in employees' negative attitudes and their desire to leave work.