Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport

Globally, the transportation and logistics sector is facing economic disruptions owing to geopolitical tensions and post-COVID-19 global economic downturns. This disruption places more pressure on transportation companies to review their work methods and processes. Coupling data and model-driven approaches is essential for developing effective and efficient resilience strategies. To address this issue, this study provides an overview of the appearance of simulation in business analytics. However, a thorough review of the literature based on the PRISMA search process allowed us to identify that none of the previous studies could highlight the role or evaluate the hybridization between business analytics and simulation and their joint use in freight transportation. Moreover, this study proposes a collaborative framework based on the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique to select a business analytics-enabled simulation architecture. This study contributes to the freight transport sector by setting up an updated list of criteria and sub-criteria necessary for business analytics evaluation and enriches the literature by applying the IF-AHP technique to a concrete case of implementing data analytics and simulation. This study also suggests future directions to enrich the academic literature and offers insights to improve the framework for other use cases. © 2023 Elsevier Ltd

Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

Purpose: Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians' knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts' knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods: Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts' knowledge. In the proposed model, we applied clustering methods to patients' data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient's data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results: According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%. Conclusion: The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well. Highlights: • A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts' knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. • Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage. • Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data;• According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets. • To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on th performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers. Graphical : [Figure not available: see fulltext.] © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Exploring the Acceptance of Smart Grid Technology in India: Role of Government Policies and Regulatory Framework

In the last few decades, technological advancements in the power sector have accelerated the evolution of the smart grid to make the grid more efficient, reliable, and secure. Being a consumer-centric technology, a lack of knowledge and awareness in consumers may lead to consumer opposition, which could imperil the grid modification process. This research aims to identify and prioritize the factors that can be considered barriers to technology acceptance for smart grid development in India. This study follows an integrated approach of literature review, AHP, and FERA. In the present work, 17 barriers have been identified and ranked on the basis of the social, technical, and economic paradigm. This study finds the impact of government policies and stakeholders' involvement in consumers' acceptance of smart grid technology and its importance towards improving the quality of life of Indians. The government should play as the main proponent. The present work will contribute to developing and upgrading the basic framework for the smart grid in a developing country like India. Copyright © 2022, IGI Global.

Logistics System Risk Analysis Using FMEA-AHP Methodology: During and after the COVID-19 pandemic

The COVID-19 pandemic broke out, and the global logistics industry suffered severe losses, therefore, the FMEA-AHP (Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an improvement on the basis of the traditional FMEA method: The AHP is integrated into the FMEA algorithm (referred to as RPWN (risk priority weighted number) in this article). In this algorithm, the AHP is to determine the weights of risk indicators. Meanwhile, in this article, we also consider about the new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. 12 failures have been identified, and corresponding preventive and corrective measures have been suggested to cut off the path of failure propagation and reduce the impact of failures. © 2022 ACM.

Fuzzy AHP approach to evaluate incentive factors of high-tech talent agglomeration

To compete with the speedy revolution of high technological innovation and restarted economy for the post-COVID-19 period in China, governments and organizations should be active in attracting high-tech talent to enhance independent and indigenous R&D capability. Talent agglomeration effectiveness is the strongest endogenous force pushing competitiveness for regional economy and industrial development. Due to the complexity of high-tech talent agglomeration, there are still considerable gaps to evaluate the incentive factors. This study evaluates the influential indicator system by using a hybrid fuzzy set theory extended Analytic Hierarchy Process (AHP) approach for proximity to reality from individual, organizational and environmental dimensions. The statistical analysis is adopted to verify the results of fuzzy AHP analysis. This research explores the founding that individual incentives are more important than environmental factors, and environmental incentives are more influential than organizational incentives. Job satisfaction, welfare system, and geographical location are the highest ranking factors. High-tech start-ups should give priority to combine geographical location with political support to reserve site selection or firm relocation for a great effectiveness of high-tech talent agglomeration. © 2022 Elsevier Ltd

New Approach of Covid-19 Prevention by Implemented Combination of Decision Support System Algorithm

Indonesia and Malaysia from 2020 to 2021 were exposed to COVID-19 pandemic. Both countries implemented a policy of restricting entry areas based on almost the same criteria, In Indonesia namely as PPKM which applying some level of exposure to those infected with covid-19. The determination of this level was all based on the growth in numbers exposed to covid-19, but on pandemic cases, the number of people who do not suffer from COVID-19 disease but have the same symptoms as the symptoms of COVID-19 also need to be considered as the pandemic agent to their environment. We named it as Precaution Covid-19 Pandemic (PCP) Level. The current level of the COVID-19 pandemic has not been fully determined by this idea. So, the idea of this research is to determine the pre-pandemic or precaution level of covid-19 in an area interfere by surrounding area. PCP level was not based on the growth of those infected with the covid-19 disease, but influenced by the number of patients whose have the symptoms similar to the dominant symptoms of the covid-19. The PCP Level determination can be used for precaution policy and support the previous Level Pandemic Methods. To accomplish this idea, three algorithms are used, they are K-Mean algorithm as a pattern clustering and the AHP algorithm as a level determination of the Covid-19 pandemic, While the relationship of candidate symptom pairs to Covid-19 transmission is carried out using the Naïve Bayes algorithm. The results of this study show that the combination of the three proposed algorithms provides and using data symptoms closely to dominant covid-19 symptoms can give an alternative for precaution level of covid-19 pandemic. The model for determining Covid-19 transmission based on four candidate symptoms has 89% precision and 85% accuracy. © 2022 IEEE.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Attitudes wards Online Learning ring COVID-19: A Cluster and Sentiment Analyses

This paper investigated the attitudes of 702 college students toward the implementation of fully online learning during the COVID-19 pandemic. Toward this goal, responses of the students were collected and analyzed through hierarchical cluster and sentiment analyses using the R software. Hierarchical cluster analysis revealed hopeful and apprehensive attitudes toward online learning. Advantages of online learning emerged as positive sentiments while challenges and their impact on mental health emerged as negative sentiments. It is concluded that online learning is a promising platform of learning provided that its shortcomings are addressed. Implications to teaching are offered. © 2022 The authors and IOS Press.

Dynamic and Fuzzy Multi-Criteria Decision-Making for Regional Safety Assessment Ranking

This paper proposes augmenting the Multi-Criteria Decision-Making (MCDM) hybrid methodology of AHP-TOPSIS with dynamic-case handling (DCH) calculations and fuzzy logic. This method is evaluated with an illustrative example of three interrelated scenarios that rank 20 countries based on regional safety assessment related to the COVID-19 pandemic. The proposed method is compared to related work in the field. Additionally, sensitivity analysis is performed to evaluate the robustness of the proposed methodology. Empirical results demonstrate that the AHP-TOPSIS method coupled with fuzzy logic and DCH calculations is a realistic decision-making approach. © 2022 IEEE.

A Decision-Making Model for Selecting the Best Digital Marketing Strategy for Chinese Long Stayers in Chiang Mai, Thailand

Along with the digital economy's growth, digital marketing significantly impacts how businesses and customers interact. It steadily permeates the Internet environment and emphasizes the importance of all types of marketing operations. Since 2020, the effect of the Covid-19 epidemic has accelerated the deep integration of social media and the home economy, and corporate marketing methods have changed from traditional marketing to digital marketing. However, digital marketing is in its infancy for many traditional industries, and marketing effectiveness needs to be improved. There is still a long way to go to complete this transformation. Therefore, this paper uses the Central Festival catering business in Chiang Mai as a case study and follows a knowledge management process, aiming to produce a multiple criteria decision-making model based on the 4C theory. To select digital marketing strategies for the Central Festival catering business in Chiang Mai to recruit and retain long-stay Chinese customers, improve market competitiveness, and increase brand influence in the face of the Covid-19 epidemic. This study is based on the knowledge management process and used qualitative and quantitative methods to investigate the research questions, conducted online surveys and in-depth interviews to collect individual and collective data. AHP and TOPSIS analyses were also used in this work. In the AHP section, each influencing factor's importance was counted by calculating the weights of the four criteria and 15 sub-criteria based on the weight of each influencing factor, thus providing a more precise measure of the importance of the six alternatives. The result was made that social media marketing is the most suitable digital marketing strategy for Central Festival. The result will help the CF restaurant industry better understand the long-stay Chinese consumer value proposition in Chiang Mai and thus build more robust decision-making models. © 2022 ACM.

Post-Covid-19 maritime transport risks management using an AMDEC-AHP combined strategy: A Tunisian case study

In this paper, we focus on the management of risks in the maritime transport sector after the spread of the pandemic Covid-19. We propose an integrated strategy to determine the best action decision to manage the most important risks. This strategy is based on two stages: the first stage consists on detecting the most serious risks using the AMDEC method and proposing a set of corrective actions for each of these risks;the second stage is responsible to determine the most appropriate action about the set of proposed alternatives using the AHP multi-criteria decision-making approach. Finally, to validate the proposed strategy, real data are collected from a Tunisian maritime transport company and the obtained results show the effectiveness of the proposed integrated method. © 2022 IEEE.

MOTIVATIONAL DIFFERENCES AMONG SOFTWARE PROFESSIONALS

[Context] The motivation and well-being of software professionals are challenged. COVID-19 pandemic shifted the work landscape, making hybrid and remote workplace settings the standard and putting previously established motivation management tools at risk. Increasing autonomous motivation of software professionals and optimizing multitasking to remain within preferred IT roles might be one approach to overcoming the new obstacles. [Method] Using a quantitative approach, the present study examined the proposed nomological network of software engineering roles, motivation, and personality traits. A conveniently sampled quantitative survey was employed in eight IT companies and two professional IT forums. It produced a considerable (N = 243) data corpus. Based on the state-of-the-art research, hypotheses were formulated, and their statistical counterparts tested by suitable statistical methods, such as the Kruskal-Wallis test. In addition, hierarchical cluster analysis was employed to meaningfully characterize personal differences among software professionals. Finally, correlation analysis was used to derive the strengths of the causal relationships. [Result] Software professionals in this study were of four distinct personality types with varying motivational levels. The openness/intellect dimension was found to significantly nurture motivation in project manager, developer, and analytical roles. In contrast, neuroticism was detrimental to motivation in all roles. The results and future study recommendations were discussed. © 2022 IDIMT. All rights reserved.

Mid-semester Pandemic-driven Course Rescheduling with Integer Programming

As universities begin the return to in-person course work, uncertainty remains about the future of SARS-CoV-2 virus and its variants. In the years to come, other novel pathogens may emerge. Pandemic-driven social distancing requirements reduce the number of students in classrooms, and when these requirements are instituted mid-semester, universities must make quick changes to classroom assignments and course delivery mode. In this work, we introduce two integer programs to optimize mid-semester changes: (i) a conflict-matrix-based model that determines new classroom capacities and designs the corresponding seat map, and (ii) a hierarchical model that optimizes room assignment and course delivery mode according to prioritized objectives. We test our methods with University of Michigan's engineering course schedule for Fall 2021, under a hypothetical 3-foot social distancing requirement. We compare the performance of the models under different hierarchical objectives and room assignment assumptions and discuss the managerial implications of our results. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

A Hybrid Supplier Evaluation Model for Personal Protective Equipment Procurement to Healthcare Employees

Healthcare workers face the risk and the danger of contracting several infectious diseases. In order to prevent from the risk of being infected by blood, contact, respiratory droplets, etc., healthcare workers need to use Personal Protective Equipment (PPE). Because of the increasing demand for quality PPE after COVID-19 pandemic, it is important for healthcare institutions to determine the best supplier. Evaluation of suppliers is a complex decision, which contains a number of alternative suppliers and conflicting criteria. The conflicts between supplier evaluation factors require institutions to make a compromise choice among alternative suppliers. Therefore, the main aim of this study is to develop an analytic supplier evaluation model for PPE procurement to healthcare institutions. To that end, a hybrid multi-criteria decision making (MCDM) model based on Analytic Hierarchy Process (AHP) and VIse KriterijumsaOptimiz acija I Kompromisno Resenje (VIKOR) is proposed. A case study for surgical mask procurement to a hospital is presented to demonstrate the applicability of the proposed model. The application results show that the proposed model is a useful decision support tool for PPE procurements to policy-makers of healthcare institutions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation. © 2022 Tech Science Press. All rights reserved.

The Post-pandemic Era Study on the Design of High-speed Railway Seats

The COVID-19 has led to people’s increased concern about health issues. In this paper, we investigate the needs of users traveling by high-speed rail in the post-pandemic era and optimize the design of high-speed rail seats, and evaluate the feasibility. Methodology: Using INPD combined with AHP and QFD to guide the design of high-speed railway seats, we use INPD as the main line of research and SET factor analysis to find the product opportunity gaps;using questionnaires and user interviews to research different high-speed railway travelers and derive various needs of users for high-speed railway seats;AHP was used to calculate and prioritize the target user requirements, and then QFD was used to determine the weights of each design requirement point. Conclusion: This paper aims to provide design ideas and future development trends for the design of high-speed railway seats in the post-pandemic era by using INPD, AHP and QFD methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluation of emergency management ability of COVID-19 vaccine safety emergencies based on AHP-TOPSIS

Large-scale injections of COVID-19 vaccine and formation of herd immunity are currently the most effective way to combat COVID-19 epidemic in the world. During the vaccination process, unexpected security incidents are likely to occur, and the government and relevant emergency departments have the responsibility to deal with these emergent security incidents. This paper proposes an AHP-TOPSIS method, establishes the evaluation index system, determines the index weight combined with AHP, and uses TOPSIS to rank the evaluation objects, so as to evaluate the emergency management ability of the government and relevant departments on the emergency safety events of COVID-19 vaccine. This is of great positive significance for continuously strengthening the emergency management capacity of the government and relevant departments and ensuring people's life, health and safety. © 2022 SPIE

An Analytical Study of Cross-Cultural Design Factors Affecting Virtual Reality Teaching Experience

Objectives: With the COVID-19 epidemic, more and more schools are choosing online education and electronic devices for learning. Due to the uniqueness of the VR model, many teachers are introducing the use of VR in educational teaching activities. Generally speaking, virtual reality is widely used in the field of education and training because of its potential to promote interactivity and motivation. It also offers a new approach to teaching and learning due to the increasing number of online learners sharing and presenting educational content, and the technological possibilities of spreading knowledge over the global web and allowing students to participate in educational courses remotely. Therefore, this study focuses on relevant design factors through the study of teaching scenarios such as VR experience teaching training in the field of product design. In the preliminary questionnaire survey, we concluded that the three key words that students care most about VR teaching tools are user interface, usability and interaction design, but from the questionnaire survey we cannot accurately understand the specific preferences of students for these three factors. Therefore, we want to understand specifically whether students care more about user interface, usability, or interaction design for VR teaching tools, that is, which of these three factors will be more preferred and valued by students, in order to consider how to weigh the three factors in the VR design and production process. By studying the design factors of VR teaching and learning experiences, VR educational content developers can better understand the factors to be considered in this field. It can also guide VR content producers to produce more content that meets students’ needs. Methods: In this study, firstly, 80 questionnaires were mainly used to organize and collect the key words of relevant factors, and after the key words were obtained, the AHP tool was used to obtain the hierarchical model, and then according to the 1–9 scale method, 10 experts in the design field and 10 students were invited to score the three design factors of VR teaching tools in a two-by-two comparison, and finally the weight values were analyzed and organized according to the AHP calculation formula. Finally, the weight values were analyzed and sorted according to the AHP formula to determine the ranking of the weight values of the design factors of VR teaching tools. Results: The AHP method is used to study the factors that influence students in the design and production of VR courses and software, and to form a hierarchy in which different factors are sequentially generated. Designers and content producers can determine the relative importance of each factor in the hierarchy through pairwise comparisons. Based on the results, we can find that user interface is the most important VR design factor that students are concerned about, followed by interactivity. The findings can be used as a framework tool to design VR content according to students’ needs and make the product a better experience. Conclusions: According to the results, we can find that the user interface is the VR design factor that students are most concerned about, followed by interactivity, which also gives us a hint that when we are making VR teaching tools, we should pay more attention to the design and presentation of VR content, choosing appropriate VR materials, materials that are closer to the real world, and at the same time designing more beautiful, simpler, and more obvious buttons or The interface should be designed with more beautiful, simple and obvious buttons or prompts. The user interface is also important, when students enter the virtual reality, they want to be as in the real world in general scenes, rather than poor quality 3D production of animation graphics, virtual reality to provide students with a realistic reproduction of the world, where they can operate, learn, practice and even experiment, and designers to do is to provide them with as much as possible to meet the needs of the VR tools. Similarly, interactivity is also important in this process. If you can only watch, but not effectively interact with VR as if it were reality, then VR is obviously inappropriate. Therefore, the weighting of these three factors will hopefully provide some meaningful inspiration to the designers of VR teaching tools. When students use VR educational products, it is very important for VR content providers and VR designers to improve the actual value of the product content, as it can help them design VR educational products that better meet the market demand. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

I-Valued Neutrosophic AHP:An Application To Assess Airline Service Quality After Covid-19 Pandemy

This study proposes the I-valued Neutrosophic AHP technique to evaluate airline service quality by determining importance priorities for passengers and generating recommendations to managers to allocate the most appropriate resource for increasing service quality and customer satisfaction. We also provide a list of what airline managers need to improve in resource allocation to increase service quality by taking customer satisfaction into account. This technique can be adapted for any industry where service quality depends on multiple attributes. © 2022. All Rights Reserved.

Study on Urban Travel Volume During the Outbreak of COVID-19

The distribution and change of travel intensity reflect the pattern of the city and the activity of trip population. It is important to understand the pattern of the city and the activity of trip flow for urban planning and government decision-making. This paper constructs a Bayesian hierarchical spatiotemporal model with three effects: space, time, and space-time, which uses the travel intensity data during the outbreak of the novel coronavirus (COVID-19) in Hubei province (2020.01.01–2020.05.02). With the help of Markoff’s Monte Carlo method, this paper analyzes the distribution and fluctuation of traffic flow in each city of Hubei province. The results show that the space-time model does not deteriorate compared with the main space model. The study found that nearly 41% of cities with a spatial effect higher than 1 were active during the epidemic in Hubei province and the time effect of travel intensity in Hubei province dropped rapidly from 2 to 0.5 after cities in Hubei province issued measures to close the cities one after another, which lasted nearly a month. Strict social distance intervention is one of the important reasons for Hubei province to control the epidemic effectively in a few months. At the same time, in the stability analysis of the city, we found that Wuhan belongs to an unstable area, which is unfavorable to the control of COVID-19. The research results provide a certain perspective for COVID-19 prevention and control: when there are confirmed patients in the province, we believe that the government should first pay attention to those cities with high spatial effect and instability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.