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The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.

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This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked through the multi-criteria decision methods DEMATEL and TOPSIS in order to determine the best solutions to the problems found and to apply these results in the academic context. The main contribution of this study was the categorization of OOP problems and solutions, as well as the proposal of strategies to improve the problem. Among the most relevant problems it was found: 1) difficulty in understanding, teaching and implementing object-orientation, 2) difficulties related to understanding classes and 3) difficulty in understanding object-oriented relationships. After doing the multicriteria analysis, it was found that the most important solutions to face the problems found in the teaching of OOP were: 1) use of active learning techniques and intrinsic rewards and 2) emphasize on basic programming concepts and introduce the object-oriented paradigm at an early point in the curriculum. As a conclusion, it was evidenced that there is coherence between the literary guarantee that gives support to the problems and solutions in the teaching of OOP presented in this study and the approaches that experts in the area of development highlight as relevant when they identify weaknesses in the process.

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The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.

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The classifier selection problem in Assistive Technology Adoption refers to selecting the classification algorithms that have the best performance in predicting the adoption of technology, and is often addressed through measuring different single performance indicators. Satisfactory classifier selection can help in reducing time and costs involved in the technology adoption process. As there are multiple criteria from different domains and several candidate classification algorithms, the classifier selection process is now a problem that can be addressed using Multiple-Criteria Decision-Making (MCDM) methods. This paper proposes a novel approach to address the classifier selection problem by integrating Intuitionistic Fuzzy Sets (IFS), Decision Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The step-by-step procedure behind this application is as follows. First, IF-DEMATEL was used for estimating the criteria and sub-criteria weights considering uncertainty. This method was also employed to evaluate the interrelations among classifier selection criteria. Finally, a modified TOPSIS was applied to generate an overall suitability index per classifier so that the most effective ones can be selected. The proposed approach was validated using a real-world case study concerning the adoption of a mobile-based reminding solution by People with Dementia (PwD). The outputs allow public health managers to accurately identify whether PwD can adopt an assistive technology which results in (i) reduced cost overruns due to wrong classification, (ii) improved quality of life of adopters, and (iii) rapid deployment of intervention alternatives for non-adopters.

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There is an increasing interest in product recovery, closed-loop supply chains, and reverse logistics (RL) for mitigating environmental impairment. Although RL is becoming a mandatory policy in developed countries, it is still in an embryonic stage in some industrial sectors of emerging economies. The purpose of this study is twofold: (1) identify the critical factors to the successful implementation of RL in the Brazilian pharmaceutical care process (PCP) and (2) determine the cause-and-effect relationships among them. We use snowball sampling to select the relevant RL studies and deductive reasoning and classification to identify the critical factors and a grey decision-making trial and evaluation laboratory (DEMATEL) to evaluate the cause-and-effect relationships among them. The study revealed management, collaboration, information technology, infrastructure, policy, financial and economic, end-of-life management practices, and logistic performance factors as the most relevant factors to the successful implementation of RL in the Brazilian PCP. The end-of-life management practices were identified as the most critical factor, and information technology was identified as the least critical factor. We further determined the end-of-life management practices and policy have the strongest casual relationship. The municipal PCP coordinators can use the findings of this study to formulate mitigating strategies to identify and eliminate barriers to the successful implementation of RL in the Brazilian PCP.

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