The analysis of risk assessment for the transmission of COVID-19 by using PROMETHEE and ELECTRE methods

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many prob-lems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the meth-ods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature.Cite this article as: Pekel ozmen E, Demir B. The analysis of risk assessment for the trans-mission of COVID-19 by using PROMETHEE and ELECTRE methods. Sigma J Eng Nat Sci 2023;41:2:232-242.

Measuring the economic performance of transition economies: DEA-bootstrapping approach

PurposeThe paper analyzes the relative technical efficiency of the transition economies of the Western Balkans in the period 2007-2021, in comparison with the former countries with a socialist state system, today members of the European Union (EU), based on selected macroeconomic indicators and panel data.Design/methodology/approachData envelopment analysis (DEA), i.e. its extension, DEA Window analysis, is applied. Total technical efficiency, as a prerequisite of economic efficiency, is decomposed into pure technical efficiency (PTE) and scale efficiency (SE). Bootstrapping method and Mann-Whitney U test were used to check the robustness of the obtained results, i.e. efficiency values.FindingsThe results show that in 2020, all observed countries recorded a significant drop in economic efficiency as a result of a general, disproportionate drop in the value of selected macroeconomic variables, which occurred due to the global economic crisis and the slowdown in economic activity caused by the COVID-19 pandemic. This drop in efficiency was significantly greater in the former socialist states, now members of the European Union, which showed their greater sensitivity to global crises. None of the observed economies in the observed period was relatively efficient, that is, at the level of best practice, which occurred primarily as a consequence of the inefficiency of business conditions expressed in the economies of scale.Research limitations/implicationsThe main limitation of this study stems from the very nature of the concept of DEA efficiency, which is relative in nature. Also, the results and their interpretation are also significantly influenced by the choice of model variables, as shown by Labaj et al. (2013), as well as a small number of decision-making units (DMUs). The mentioned limitations prevent unambiguous interpretation and generalization of the obtained results.Practical implicationsThe study may be of importance to economic policy makers in macroeconomic decision-making. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage on the international market.Originality/valueSince research of this type is very limited, the results of this study make a theoretical and empirical contribution to the literature, creating a basis for future research and reexamination. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage in the international market.

Real-time risk ranking of emerging epidemics based on optimized moving average prediction limit-taking the COVID-19 pandemic as an example.

BACKGROUND: Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time. METHODS: The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing. RESULTS: The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium-high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually. CONCLUSIONS: The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.

Identifying potential significant factors impacting zero-inflated proportion data.

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID-19 mortality dynamics.

An interpreter ranking index-based MCDM technique for COVID-19 treatments under a bipolar fuzzy environment

The entire world is currently fighting the severe and dangerous pandemic COVID-19, which is causing bodily suffering and mental distress due to the rapidly increasing number of infected patients and deaths worldwide. Many COVID-19 treatments are going on in India, and some treatments are under development for these patients. But, treatment selection for the COVID-19 patients is challenging in the present situation. Through the multi-criteria decision-making technique, they can select the COVID-19 treatments easily. Therefore, we have developed an MCDM technique to select COVID-19 treatments in India. This paper invented the value and ambiguity of bipolar fuzzy (BF) numbers. Additionally, some fundamental theorems and properties of BF-numbers are studied. A novel positive and negative interpreter ranking index of BF numbers has been introduced. In the present day, most human decision-making relies heavily on bipolar fuzzy information. Hence, we developed an MCDM technique with bipolar fuzzy details. A comprehensive range of human decisions for selecting COVID-19 treatments is based on positive and negative double-sided or bipolar judgemental thinking. To select COVID-19 treatments in India, we have applied the proposed MCDM technique with BTrF information. Moreover, to demonstrate the applicability of our proposed MCDM method, we have considered a numerical example with BF data. Finally, we give the comparison study to show the effectiveness of our proposed MCDM method with other existing decision-making methods.

The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.

A multifaceted graphical display, including treatment ranking, was developed to aid interpretation of network meta-analysis.

OBJECTIVES: Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed together, permitting intervention hierarchies to be created. Our aim was to develop a novel graphical display to aid interpretation of NMA to clinicians and decision-makers that incorporates ranking of interventions. STUDY DESIGN AND SETTING: Current literature was searched, scrutinized, and provided direction for developing the novel graphical display. Ranking results were often found to be misinterpreted when presented alone and, to aid interpretation and effective communication to inform optimal decision-making, need to be displayed alongside other important aspects of the analysis including the evidence networks and relative intervention effect estimates. RESULTS: Two new ranking visualizations were developed-the 'Litmus Rank-O-Gram' and the 'Radial SUCRA' plot-and embedded within a novel multipanel graphical display programmed within the MetaInsight application, with user feedback gained. CONCLUSION: This display was designed to improve the reporting, and facilitate a holistic understanding, of NMA results. We believe uptake of the display would lead to better understanding of complex results and improve future decision-making.

A Novel Hybrid Model for the Evaluation of Industry 4.0 Technologies' Applicability in Logistics Centers

The application of Industry 4.0 (I4.0) in the field of logistics leads to the emergence and development of the concept of logistics 4.0. Many I4.0 technologies have been applied in the field of logistics. The goal of this research is to analyze the applicability of nine key I4.0 technologies in logistics centers (LC). For this purpose, an integrated MEREC (MEthod based on the Removal Effects of Criteria)—fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) model was developed. The applicability of nine I4.0 technologies was evaluated based on 15 subcriteria within three main groups of criteria, namely, technological, social and political, and economic and operative. Using the MEREC method, the weight values of the criteria and subcriteria were determined, while the technologies were ranked using the fuzzy MARCOS method. Based on the results obtained by applying this integrated MCDM (multicriteria decision-making) model, CC was identified as the best alternative, i.e., the technology that is most applicable in logistics centers, followed by IoT and big data. An analysis of the sensitivity of the obtained results to the change in the importance of the criteria was carried out, which shows certain changes in the ranking when the importance of the most important criterion changes.

Career development of academic staff in the Russian Federation and the Czech Republic: From migration to integration

Higher education generates human capital for the economy. It is conceived as a source of competitive advantage for a state, financing its higher education institutions. This chapter evaluates the career development of university academics in Russia and Czechia, considering further globalization of education and academic labor markets processes. A special focus is placed on the opportunities and threats for staffing and stabilizing lecturers. The principal source of future academics in both countries is primarily PhD students and young researchers. Career development depends on the individual's performance and involvement in research projects. The growing emphasis is put on international collaboration, knowledge transfer, and distance education. Higher education institutions in the Czech Republic carry out habilitation procedures and professorship, guaranteeing employment for an indefinite period and higher salaries for academics. However, in the long run, their policies admit inbreeding. Due to the coronavirus disease 2019 (COVID-19), they implemented the massive transition to online education, not considering lecturers' digital well-being. The Russian Federation also lays emphasis on young scientists and international collaborations. Universities with a special status or universities with a technical bias receive more funding. The salary of a university teacher today can be considered above the national average. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Competition in american social work education during times of COVID-19: a midwestern case study

Competition to attract students for enrollment in American social work degree programs is intense. Program attributes (e.g. minimum grade point average, maximum transfer credits permitted, institutional tuition rate, and rankings) distinguish social work education programs in the United States. Determining which program attributes appeal to potential students could help increase a program's competitiveness in a crowded education marketplace. The COVID-19 pandemic is further intensifying recruitment of students to BSW programs. This research used a case study approach to compare admission attributes of 21 bachelor of social work programs (BSW) offered at 11 public and 10 private institutions located in one state that could be viewed as representative of American BSW programs. This paper compared attributes that differentiated these undergraduate social work programs, while exploring the potential impact of the pandemic on BSW student recruitment. Implications for social work education are discussed, including lessons learned that may be helpful to BSW faculty and staff responsible for student recruitment activities and related operations. Social work education program administrators and faculty could use this information to review recruitment and application processes and raise awareness of the burgeoning influence of reputational ranking services.

Personal qualities are malleable and fixed: Ambivalent mindset, capability ranking reinforcement, and parent-child relationship among Hong Kong Chinese parents

Past research showed that people may hold contradictory ideas about something or someone. Mindset ambivalence refers to the psychological state in which a person holds contradictory beliefs about the malleability of a valued attribute and spontaneously expresses agreement with both the fixed and growth mindsets. Our past findings showed that a sizable proportion of Hong Kong Chinese adults possess the ambivalent mindset. In the present study, 101 Hong Kong Chinese parents completed a survey during the COVID-19 pandemic. The findings provided further support for the prevalence of the ambivalent mindset. In addition, we found that parents with the ambivalent mindset tended to support several parental practices that would reinforce the relative ability rankings of their children. These practices included person praise, mobilization of effort to compensate for low ability, and lowering of expectation to avoid future failures. Finally, the use of these parental practices was accompanied by deterioration of parent-child relationship when children displayed undesirable self-regulatory behaviors. We discuss these findings' implications for growth mindset interventions in Chinese societies.

Prioritization of Awareness Campaigns for a Diseases through Fusion of Pentagonal Intuitionistic Fuzzy Number and Promethee

A novel method for assessing the effectiveness of enrichment evaluations PROMETHEE combining pentagonal intuitionistic fuzzy numbers (PIFNs) and preference rank-ing organization is presented in the present paper. PIFN suggests a new technique for multi -criteria group decision making (MCGDM) in which two characteristic values of membership and non-membership functions are involved. The key practicality of incorporating PIFN in decision -making is its effective capability of managing the vagueness and uncertainties of linguistic terms used during discussions. The designed algorithm is then applied to get an appropriate, cost-effective, and publicly accepted awareness campaign to be used to forewarn populaces about any virulent disease, which has not been studied before. Importantly, it is the only way to protect any huge population of a country from any fatal disease, i.e. to be timely aware of the disease's transmissibility, severity, and precautionary measures through any effectively ap-proachable source. Here, we consider alternative sources of campaigns, such as commercial advertisement on television, on social media, on bills /other government circulars, billboards, and door-to-door volunteering for guidance. These alternative campaigns are based on five generalized criteria, where the weight of each criterion is evaluated via the fuzzy analytical hier-archy process (F-AHP). After using the F-AHP for complex decisions based on acceptance and effectiveness, the F-PROMETHEE algorithm is applied to achieve the closest ideal alternative.

Teaching Adults: Learning Languages in the Pandemic Span

All over the world the COVID-19 pandemic has led to the urgent transfer to distance education or blended learning in various educational establishments. As a result of such consequences of the governments' requirements to prevent the virus spreading those of isolation, social distancing and quarantine, both students and academicians unexpectedly and instantly were forced to switch to distance education within days. Based on the changes, the present study is to analyze the challenges adult learners face and to offer ways to meet them, as well as to devise and develop measures providing language promotion sustainability in the pandemic. These measures are referred to in the present study as a cushion grid could serve as an imperative of the pandemic span style (a teaching style which is seen by the authors as an umbrella term for methods and approaches used for distant teaching during the COVID-19 pandemic) in teaching adult learners. The study is based on analysing the variables producing e-learning satisfaction among the adult learners of the UNICO Language Centre of Siberian Federal University. The Centre was established on the TEMPUS (Trans-European Mobility Programme for University Studies) programme which encourages higher education institutions in the EU Member States and partner countries to engage in structured cooperation to implement Joint European Projects (JEPs) with a clear set of objectives. On completion of the project activities the UNICO (University-Career-Opportunities) Centre was transformed into a sustainable educational establishment where the adult learners could take a non-formal course of studies. The study group included 92 learners of the UNICO Language Centre of Siberian Federal University. The results demonstrated two learning imperatives: those of the social and the linguistic one. © 2022, Springer Nature Switzerland AG.

Linguistic Resources Construction for Food and Beverages in Thai Texts for Product Storytelling Text Generation

Due to the impact of COVID-19, most people have to quit their jobs. As a result of the epidemic, many people are turning to open online stores on e-commerce platforms, and people new to this market are inexperienced in competing with other stores. One of the critical points that can help them is to present their stories through merchandising. The presentation of a story through sales can be like telling a story about their store or the product. However, writing a good story is not easy. So, this research aims to help the sellers by constructing linguistic resources that sellers can use for writing a story about their products. In this paper, 3378 token product description texts were collected and ranked by the Text Ranking method to determine the frequency of frequently-used words. The 1,853 words from the Text Ranking were applied for text modeling using Latent Dirichlet Allocation (LDA). The result shows that these words can be categorized into seven food and beveragerelated topics. Moreover, sellers can use these words for writing product descriptions. © 2022 IEEE.

Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers' own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria;2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score;3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Artificial Neural Network Dual Hesitant Fermatean Fuzzy Implementation in Transportation of COVID-19 Vaccine

This article, in order to address impreciseness, initiated the notion of dual hesitant fermatean fuzzy sets (DHFFSs), as a generalization of the combination of dual hesitant fuzzy set (DHFS), dual hesitant Pythagorean fuzzy set (DHPFS) and Fermatean fuzzy set (FFS). The authors defined the fundamental set of operations for DHFFS. Additionally, the authors have also proposed two ranking functions and an accuracy function for the ordering of this novel set. In order to facilitate the pragmatic implementation of DHFFS in optimization, the authors formulated three types of transportation problem with dual hesitant Fermatean fuzzy (DHFF) parameters. To optimize the DHFF-TP, an algorithm was proposed with the help of one of the proposed ranking functions. Artificial neural network is also applied to the transportation problems in DHFF environment. A numerical example based on the transportation of COVID-19 vaccine with DHFF cost has also been carried out to validate out to validate our technique.

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.

A computational model for assessing experts' trustworthiness

The algorithmic detection of disinformation online is currently based on two strategies: on the one hand, research focuses on automated fact-checking;on the other hand, models are being developed to assess the trustworthiness of information sources, including both empirical and theoretical research on credibility and content quality. For debates among experts, in particular, it might be hard to discern (less) reliable information, as all actors by definition are qualified. In these cases, the use of trustworthiness metrics on sources is a useful proxy for establishing the truthfulness of contents. We introduce an algorithmic model for automatically generating a dynamic trustworthiness hierarchy among information sources based on several parameters, including fact-checking. The method is novel and significant, especially in two respects: first, the generated hierarchy represents a helpful tool for laypeople to navigate experts' debates;second, it also allows to identify and overcome biases generated by intuitive rankings held by agents at the beginning of the debates. We provide an experimental analysis of our algorithmic model applied to the debate on the SARS-CoV-2 virus, which took place among Italian medical specialists between 2020 and 2021. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

The effects of the Covid-19 pandemic on the performance of academic journals: The case of Turkey

Since the beginning of 2020, "Covid-19” has affected the whole world in an unprecedented way in modern times. It is inevitable that this pandemic, which has negatively affected many fields, will also have an impact on academic journals. The aim of this study is to determine the effect of the Covid-19 pandemic on the performance of academic journals. In our study, a "Data Envelopment Analysis” methodology with 3 inputs and 3 outputs was used to determine the relative "performance of the journals”. Within the scope of the study, 109 journals published in "Turkey” and scanned in "Web of Science” indexes were examined. Results show that eleven journals were efficient in 2019, while in 2020 this number decreased to seven. Four fields have been positively affected by the pandemic and journals publishing in these fields have increased their efficiencies. Eighteen fields were adversely affected by the pandemic and the efficiency of journal publishing in these fields decreased. Eleven fields and journals publishing in these fields maintained their efficiency both before and during the pandemic. As the Covid-19 pandemic is not over yet, our data is limited. In the coming years, more detailed and comprehensive studies can be carried out with more extensive data and a further number of journals from different countries. © 2022 Pontificia Universidade Catolica de Campinas. All rights reserved.