Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

The Influence of Social Media and Sustainability Advocacy on the Purchase Intention of Filipino Consumers in Fast Fashion

The large consumption of fast fashion brings many negative environmental impacts. Filipino consumers love and buy fast fashion because it is relatively cheap but trendy, and there are lots of fashionable designs to choose from. Despite the shortage in water supply and disposal issues of fast fashion, people still continue to purchase. The lack of awareness of consumers on sustainable fashion consumption led the researchers to conduct a study that aims to identify factors affecting Filipino consumers' buying decisions on fast fashion using the combined theory of planned behavior, elaboration likelihood model, and hedonic motivation. A total of 407 participants were gathered through a convenience sampling approach, and the data collected were analyzed using structural equation modeling (SEM). The result shows that attitude towards fast fashion is the highest contributing factor to purchase intention. While social media positively affects purchase intention, sustainability advocacy negatively impacts the consumers' intention to buy fast fashion. The awareness of sustainability leads to consumption reduction of fast fashion garments. Surprisingly, perceived product price and quality do not show a significant influence on purchase intention. Incorporating sustainability advocacy on social media may be a great strategy to encourage the sustainable consumption of fashion garments. The findings of this study could be a great tool to influence fashion companies and government institutions to promote sustainability awareness and transition marketing strategies to the sustainable consumption of fashion.

Rationality in social science: Foundations, norms, and prosociality

The concept of rationality and its significance for theory and empirical research in social science are key topics of scholarly discussion. In the tradition of an analytical as well as empirical approach in social science, this volume assembles novel contributions on methodological foundations and basic assumptions of theories of rational choice. The volume highlights the use of rational choice assumptions for research on fundamental problems in social theory such as the emergence, dynamics, and effects of social norms and the conditions for cooperation and prosociality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021.

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

PurposeThe COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.Design/methodology/approachBased on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.FindingsThe research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government's punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.Practical implicationsThe study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.Originality/valueTo the best of the authors' knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

MADM for Assessment the Nurses Knowledge and their Attitudes During Covid-19 Spread in Mosul City in the Perspective of Neutrosophic Environment

Coronavirus remains an important public health issue both nationally and globally, so all healthcare professionals including nurses should have a good knowledge and attitudes for educating their patients about Coronavirus and provide appropriate referral and support mechanisms to minimize the complication of disease [1]. COVID-19 is an emerging, rapidly changing global health challenge affecting all sectors [2, 3]. The Health Care Workers (i.e. HCWs) are not only at the forefront of the fight against this highly infectious disease but are also directly or indirectly affected by it and the likelihood of acquiring this disease is higher among HCWs compared to the general population [4]. Therefore, it is importance that HCWs across the world have adequate knowledge and good attitudes about all aspects of the disease from clinical manifestation, diagnosis, proposed treatment and established prevention strategies. In this manuscript, a descriptive design study was conducted from 1st April to June 2021. The study samples consisting of 90 nurses were purposively selected in three hospitals (Al-Khansa Teaching Hospital, Ibn Sina Teaching Hospital, and Telafer General Hospital) in Mosul city The objectives of this study are to assess the knowledge and attitudes of nurses about the Covid19 using the multi attribute decision making technique where the data have been adapted and reconstructed to be as triangular single-valued neutrosophic numbers (TSVNN) and tackled these (TSVNN) into the neutrosophic structured element (NSE). It is well known that the neutrosophic theory has flexible tools to analyze data utilized in dozens of fields of science such as but not limited to medicine, engineering, economics, healthcare, physics…etc. In this manuscript, the authors were very felicitous to choose an uncertain mathematical environment named neutrosophic theory to use it as a strong method in decision making technique to measure the performance of the nurses and their attitude in three Iraqi hospitals during a specific period of time where Covid19 has spread and was in its peak. The decision-making with multi-attribute criteria containing truth membership, indeterminate membership, and falsity membership is regarded as the core of the neutrosophic decisions. The neutrosophic theory used to handle uncertain, vague, incomplete, and inconsistent data or information which already exist in our daily life © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Meta-analysis of Perceptions of Occupational Risks in the COVID-19 Era

Received: 06-Feb-2023, Manuscript No. iphsj-23-13598;Editor assigned: 09-Feb-2023, Pre-QC No. iphsj-23-13598 (PQ);Reviewed: 30-Mar-2023, QC No. iphsj-23-13598;Revised: 04Apr-2023, Manuscript No. iphsj-23-13598 (R);Published: 11-Apr-2023, DOI: 10.36648/1791809X.17.4.1012 Introduction Until November 2022, the pandemic has claimed the lives of five million, although international health systems such as the World Health Organization and the Pan American Health Organization recognize the underreporting of community transmission. [...]the mitigation and containment policies of the pandemic through the implementation of distancing, confinement and immunization strategies limit the workplace and reorient it towards biosafety guidelines [9] In this situation, the theory of prospective decisions explains the relationships between leaders and talents in the face of contingent events [10] The theoretical approach raises differences between those who make decisions and those who abide by them [11]. Risks are also the result of their determinants, as is the case with the perception of control The emergence of self-control is the product of a high expectation of risk, but also of experiences of control that guide the individual to assume self-efficacy in health care. [...]the modeling of perception or expectation biases in the face of a risk such as infection, disease or death from COVID-19 has not been clarified.

Emergency plan selection for epidemic prevention and control based on cumulative prospect theory and hybrid-information MADM

PurposeDecision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.Design/methodology/approachIn this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.FindingsThe validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.Originality/valueAn emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.HighlightsThis paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

Three-way investment decisions during the epidemic with Choquet-based bi-projection method

The outbreak of epidemic has had a big impact on the investment market of China. Facing the turbulence in the investment market, many enterprises find it difficult to judge the development prospects of investment projects and make the right investment decisions. The three-way decisions offer a novel study perspective to solve this problem. Then the developed model is applied to select the investment projects. Firstly, some relevant attributes of the project are described with the double hierarchy hesitant fuzzy linguistic term sets. And a double hierarchy hesitant fuzzy linguistic information system is constructed for each project. Secondly, the weights of attributes are determined with the Choquet integral method. And the closeness degree calculated by Choquet-based bi-projection method is taken as the conditional probability that the project will be profitable. Next, considering the influence of the bounded rationality of decision makers, the threshold parameters are calculated based on prospect theory. Finally, the decision results about investment projects during four stages are deduced based on the principle of maximum-utility, which demonstrates the practicability and effectiveness of the proposed model.

Decision-Making in Mask Disposal Techniques Using Soft Set Theory

In 2003, Maji, Biswas, and Roy developed a method for applying soft set theory to a decision-making problem using Pawlak's rough set approach. Further, research proved that Maji's soft set reductions were inaccurate in 2005, leading to the development of a new method by Chen et al. This article applies soft theory to waste management and disposal decision-making problems. The excessive masks discarded during the COVID-19 era, in particular, must be managed effectively, and the current paper provides a method for better decision-making of the same. The algorithms used are first to compute the reductions and then the reduct soft set is used to choose the ideal objects for decision problems, and then the choice value is calculated. Predefined parameters are sometimes not enough to make precise decisions to solve general or real-time issues. Therefore, additional parameters are added into the existing set, either as a new parameter or generated by the handling of existing ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

The missing link in disruption management research: coping

The COVID-19 pandemic made it clear that the impact of supply chain disruptions on different organizations may vary widely. Even if different levels of capabilities (agility, adaptability, etc.) may have contributed to the differential in outcomes, organizations need to learn how to harness their capabilities effectively in the face of disruptions. Although there is vast literature on supply chain disruption management, risk management, and resilience, we are not aware of any theory that comprehensively explains the decision-making process for managing disruptions. We argue that coping theory can explain how organizations may channelize resources based on two stages of appraisal to handle long- and short-term disruptions. Borrowing from psychology, we adapt coping theory to disruption management for any organization in any industry. In this paper, we demonstrate how supply chain coping strategies may explain outcomes of several organizations from different industries during the COVID-19 pandemic. We argue that organizations may sustain and even thrive if they adopt the right coping strategies in their context. We present our thesis using the following three themes: (1) We first identify potential demand trajectories organizations may follow during and after the pandemic, (2) We explain how coping strategies adopted by organizations may impact these trajectories, and (3) We present a framework to help the decision makers understand potential positive impact the coping strategies may bring to their organizations in future crises.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The impact of global risks on supply chain performance. An empirical study on construction sector in the COVID-19 pandemic

PurposeThis paper aims to identify and assess global risks in the supply chain performance.Design/methodology/approachFirst, global risks are identified and classified according to three criteria: content, probability and context. A set of supply chain performance indicators are then defined by the theory of resource-based view and balanced scorecard. Structural equation modeling is adopted to access risks in the global supply chain.FindingsThis article contributes to the supply chain risk management literature by providing a detailed operationalization of global supply chain risk constructs, e.g. natural disasters, war and terrorism, fire accidents, economic and political instability, social and cultural grievances, decease. Empirical results reveal that the supply chain is predominantly regarded as being vulnerable as the proposed model of risks can explain up to 12.6% variance of supplier performance, 25.2% innovation and learning, 23% internal business, 40.6% customer service and 32.4% finance.Research limitations/implicationsThese risks are relevant contextual variables in strategic supply chain decisions. Supply chain managers should keep in mind acceptable cost/benefit tradeoffs in their firms' mitigation efforts associated with major contingency risks. This research advocates the allocation of scarce resources to adopt the supply chain strategies of avoidance, speculative and postponement.Originality/valueThe application of the strategic content/process/context to explain global supply chain performance is an interesting approach. Moreover, globalization trends and the COVID-19 perspectives are considered to be the main reasons for increasing such complex factors. Data on validating research models collected during the COVID-19 pandemic reflect the topicality of this study.

An integrated interval type-2 fuzzy rough technique for emergency decision making

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM, this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights;(ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM;(iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM. © 2023 Elsevier B.V.

Exploring key factors sustaining micro and small food, wine and hospitality firms through the COVID-19 crisis

PurposeThe purpose of this study is to contribute to the understanding of how micro and small firm owners/managers cope with an extreme event, as this has implications on how firms make decisions. The study considers self-efficacy and stakeholder theory as tools to gain more in-depth knowledge.Design/methodology/approachThe perspectives of owners/managers of 308 micro and small firms operating in the food, wine and hospitality industries in Italy, one of the most affected nations, were drawn through an online questionnaire.FindingsThe importance of determination, passion, family support and a sense of responsibility towards internal and external stakeholders emerged as fundamental factors helping firms confront the crisis. Five theoretical dimensions that help explain how firm owners/managers make decisions to safeguard their firms during the COVID-19 crisis are identified. Three of these, "motivational”, "stepping up” and "firm-based”, are directly associated with tenets of self-efficacy theory, and two, "human-moral” and "entity-based”, with stakeholder theory. Further complementing this second contribution, a theoretical framework underlining conceptual and practical implications is proposed.Originality/valueThe study delves into the challenges and survival of a key group of firms facing an extreme crisis. The identified dimensions provide useful conceptual depth and practical insights that, together, form part of a proposed framework. For instance, the "human-moral” dimension reflects upon aspects that have wider implications, notably, for firms' employees and the wider society.

Avoiding the Worst Decisions: A Simulation and Experiment

Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.

Managerial decision-making during the COVID-19 pandemic and its impact on the sustainability initiatives of Canadian foodservice businesses

COVID-19 had a major impact on the Canadian foodservice sector. Like most countries, the pandemic in Canada resulted in various periods of lockdown. The pandemic placed great strain on many establishments and had a major impact on the pre-COVID-19 sustainability initiatives of the Canadian foodservice sector. The purpose of this study was to observe managerial decision-making in Canadian foodservice businesses during lockdown and reopening, focusing on the impact of those decisions on pre-COVID-19 sustainability initiatives. We linked the outcomes to the theory of decision-making by objection during times of crises. This study used semi-structured interviews over a two-month period in mid-2020 with three Canadian foodservice establishments. Our results showed that decision-making impacted the environmental sustainability initiatives in foodservice establishments by imposing a throwaway culture for food and personal protective equipment. The pandemic also impacted social and economic initiatives, created higher operation costs, a complexity of government intervention and the managing of mental health. This study showed that the COVID-19 pandemic provided an opportunity to develop theories of managerial decisions during crises and disasters that are natural, versus human-based crises, with pandemics situated between those two concepts. Future research could investigate the impact of decision-making on other initiatives within foodservice businesses.

Supporting Health and Medical Decision Making: Findings and Insights from Fuzzy-Trace Theory.

HIGHLIGHTS: Fuzzy-trace theory (FTT) supports practical approaches to improving health and medicine.FTT differs in important respects from other theories of decision making, which has implications for how to help patients, providers, and health communicators.Gist mental representations emphasize categorical distinctions, reflect understanding in context, and help cue values relevant to health and patient care.Understanding the science behind theory is crucial for evidence-based medicine.

Classifications of COVID-19 Variants Using Rough Set Theory

Since January 2020, the corona epidemic has created havoc worldwide. Although this virus has been mutated many times, the recent variant is more fatal for humans. Increasing active and death cases in the globe as well as in our country affect the psychological well-being of the people. India has experienced all variants including Alpha variant (B.1.1.7), Delta variant (B.1.617.2), and Omicron variant (B.1.1.529). All variants have some common symptoms along with extended symptoms. In this paper, we propose a rule base to classify and predict the variants of COVID-19 using a rough set approach. Our approach works for the elimination of redundant symptoms to create effective reduct, core, and selection of important symptoms to maintain the accuracy in a rule base. Our rules are validated to computer-generated data with 90% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.