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The globalization of business and the consequent exposure to global competition, besides the economic and social changes caused by the COVID-19 pandemic made the Training & Development (T&D) sector increasingly important for professionals in the corporate environment. In this sense, managing stakeholders and a portfolio of clients, as well as analyzing the relationship between customer and service, are necessary and strategic for the success of professional training organizations. This paper aims to support the strategic process of portfolio formation of T&D courses offered by a company in the Information Technology (IT) training sector in Brazil, through the application of the ELECTRE-MOr multicriteria sorting method. We have obtained a categorization of several courses, aiming to define which ones should be prioritized, maintained, or discarded by the company’s management. The results showed that, among the analyzed courses, only 17% should be prioritized, 61% maintained, and 22% discarded by the company.

In group decision making (GDM) processes, prior to the selection of the best alternative(s), it would be desirable that experts achieve a high degree of consensus or agreement between them. Due to the complexity of most decision making problems, individuals' preferences may not satisfy formal properties. ‘Consistency’ is one of such properties, and it is associated with the transitivity property. Obviously, when carrying out a rational decision making, consistent information, i.e. information which does not imply any kind of contradiction, is more appropriate than information containing some contradictions. Therefore, in a GDM process, consistency should also be sought after. In this paper we present a consensus model for GDM problems that proceeds from consistency to consensus. This model integrates a novel consistency reaching module based on consistency measures. In particular, the model generates advice on how experts should change their preferences in order to increase their consistency. Also, the consensus model is considered adaptive because the search for consensus is adapted to the level of agreement achieved at each consensus round.

The price risk of fresh agricultural products has been a significant topic in recent years. Taking the two-level fresh agricultural product supply chain as the research object, this paper studies the optimal ordering and coordination of supply chain based on two-period price, wholesale price and option contract. The optimal order decision of the retailer at the single period price and the optimal decision corresponding to the supplier are obtained when the output of the supplier is uncertain under decentralized decision-making. The range of penalty cost parameter that avoids supplier default is also obtained. The effect of two-period price on the optimal order decision and supply chain profits is discussed when the production yield of the supplier is fixed. Cost-sharing contract is introduced to increase the order quantity and achieve coordination because the option contract cannot completely make the supply chain coordination with two-period price. This paper provides a low-cost approach that can be applied in fresh agricultural supply chain to solve financing and order problems.

Zadeh introduced the concept of Z-numbers in 2011 to deal with imprecise information. In this regard, many research works have been published in an attempt to introduce some basic theoretical concepts of Z-numbers to model real-world problems. To understand the current challenges when dealing with Z-numbers and the feasibility of using Z-number in solving real-world problems, a comprehensive review of the existing work on Z-number is paramount. This paper consists of an overview of existing literature on Z-number and identifies some of the key areas that are required for further improvement.

Requirement change impact analysis has been acknowledged as one of the crucial steps in product design. In this paper, we propose a network-based method to analyze change impact. By defining interconnections among parts, we build a directed weighted complex product network model to represent the product structure under given requirements. Then, we discuss two requirement change cases and develop corresponding modification policies. To specify indirect impacts, we propose a change propagation searching model in light of Matthew Effect theory. To measure the degree of change impacts, we propose two criteria (network variation scale and extra network change cost), both of which can provide a systemic assessment of impacts. Finally, a case of clutch is presented to illustrate the proposed approach. The results can provide way of measuring overall change impacts on the product, which can support decision-makers to respond that the change request can be fulfilled or not.

The problem of the accumulation of experience and the use of decision-making in the previously observed situations is researched. The main objective of the research is development of the data model that provides the upgrade of reliability of decision-making on the basis of experience. The concept of the image of the situation, which has not clearly defined center and a neighborhood, is introduced. The main thing is not trajectories in feature space but the admissible transformations of situations and solutions.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance.

Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.

Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking.

Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results.

Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.

Multi-risk assessment involves the inclusion of hazard and risk interactions within the modeling of the disaster risk chain. These interactions include more than one disastrous event at the same time, cascading events, and how changes in exposure and vulnerability arise over time, including as a result of previous events. At a first glance, multi-risk assessment appears to be a better means of approaching disaster risk reduction actions. However, it is hindered by a lack of knowledge about the fundamental physical processes involved, difficulties in comparing hazards and risks of different types and, especially, the topic of this chapter, barriers within risk governance for the successful implementation of necessary risk mitigation actions. Such barriers include a lack of standardization in terminology, a deficiency in expertise in the range of disciplines that are relevant to multi-risk reduction planning, inadequate resources, and biases and barriers in communication between the relevant public and private actors, as well as between researchers and policy-makers. This chapter details some of the social, institutional and scientific barriers that are associated with the full consideration of multi-risk governance, and provides some suggestions as to how these may be overcome.

This paper first explores the decision-making process in agile teams using scrum practices and second identifies factors that influence the decision-making process during the Sprint Planning and Daily Scrum Meetings. We conducted 34 semi-structured interviews and 18 observations across four agile teams. Our findings show that a rational decision-making process is sometimes followed in the Sprint Planning and Daily Scrum Meetings and that three factors can influence the rational decision-making process: sprint duration, experience and resource availability. Additionally, decisions are not always made in a collaborative manner by team members. This research contributes to the decision-making literature and project management literature by highlighting difficulties pertinent to decision making in agile teams.

The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.

With the purpose of understanding the extent of superfluous work and, thereby, suggesting managerial opportunities for reducing superfluous work, this paper focuses on decision-making processes at the shop floor level in digitalized manufacturing companies. Superfluous work is a kind of hidden waste and comprises the gap between necessary work and the work that is actually carried out, either on handling daily tasks at the shop floor, accomplishing decision-making processes, or implementing workarounds. By using an abductive approach, the research systematically combines a theoretical conceptualization of shop floor decision-making processes in smart-manufacturing with an empirical enquiry into a highly digitalized manufacturing company. The paper reveals superfluous work if the decision-making process involves collaboration across disciplines and/or organizational boundaries. Superfluous work occurs because of a lack of data and information to guide reflective thinking and knowledge sharing. In relation to highly complex decision-making, the ongoing implementation of workarounds also causes superfluous work. Prerequisites for reducing superfluous work are enhancing the accessibility of applicable data to guide reflective thinking and knowledge sharing at the shop floor level.

This article uses theoretical approaches from cognitive psychology to examine the basis for entrepreneurial alertness and to connect it to existing theories of attention in strategic management and decision-making. It thereby provides a theoretical basis for understanding how entrepreneurial alertness leads the individual to pay attention to new opportunities. A model is developed to show how attention and entrepreneurial alertness work together to support the recognition or creation of opportunities. Entrepreneurial alertness is believed to be a manifestation of differences in the schemata and cognitive frameworks that individuals use to make sense of changes in the environment. This suggests that entrepreneurial alertness mediates the impact of observed phenomena upon the situated attention of individual decision-makers.

The use of web-based education and e-learning environments has increased with the developments in educational technology. Schools, universities, public institutions, and other private sector companies started deploying these systems to train their students, members, and employees. Exams are carried out during the evaluation process of these trainings. Web-based tests are sometimes used for these exams. When there are so many questions about the same topic, it is a time-consuming and difficult problem to prepare these exams in terms of the best quality, quickly and effectively. In order to overcome this issue, artificial intelligence techniques are utilized as well as conventional methods for producing test papers. In this study, an Intelligent Question Evaluation and Selection Software (I-QUESS), that enables the selection of questions according to desired preferences by using Fuzzy Analytic Hierarchy Process (FAHP) and genetic algorithm (GA) as hybrid, was developed. This proposed hybrid system was used in a case study to create test sheet for web-based environments.

The distance measure as an information measure helps in processing incomplete and confusing data to arrive at a conclusion by assessing the degree of difference between pairs of variables. Reviewing distance measures for Intuitionistic Fuzzy Sets (IFSs), we have pointed out several drawbacks of the existing measures. To overcome these, this paper presents a new distance measure between IFSs based on the probabilistic divergence measure. Several mathematical properties of the proposed metric are established and validated via numerical examples. This proposed definition is further used to devise several similarity measures. Applicability and consistency of the introduced measures have been corroborated by various examples. In addition to that, rationality of the proposed metric is established by applying it to pattern recognition applications, Multi-Attribute-Decision-Making (MADM) problems and medical & pathological diagnoses. Analysis of the results establishes that the suggested measure overcomes shortcomings associated with existing measures and thereby authenticates the superiority of the proposed measure.

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain–computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects’ recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77s, when compared with 79.2% accuracy and a decision time of 3.01s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

Product packaging has a great influence on customers’ decision-making and shapes purchase intentions. The graphic message is the crucial component of this impact. Digital presentations of goods are ubiquitous, therefore understanding how graphical features influence customer decisions is of enormous theoretical and practical importance. Despite the interest, the role of specific factors and their combinations is still unclear, especially if medium-involvement products are concerned. Since only a few studies have considered this context, this research examines how eight variants of a digital presentation of cordless kettle packaging influence purchase willingness, which was derived from pairwise comparisons using eigenvectors. The experimental conditions differed in three factors: the existence of a product graphical context, a brief or extended product description, and white or black packaging background color. Results of analyses of variance and conjoint analyses revealed a significant role of all examined effects, with the background color being the least influential. The best-rated designs included graphical context and extended textual information. There were also some meaningful gender-related differences revealed by conjoint analyses. The black background color was much more important for females than males. The outcomes broaden our knowledge on people’s perception of packaging design graphical factors, and their impact on purchase decisions.

Sundry complex real-world problems involving decision-making have been resolved using the idea of distance measures between intuitionistic fuzzy sets (IFSs). Several distance measuring techniques between IFSs have been developed, but it is only the work of Xie et al. that considered the tendency coefficients of the intuitionistic fuzzy parameters (IFPs), namely, membership grade, nonmembership grade and hesitation grade. Albeit, Xie et al.’s technique uses assumed tendency coefficients for IFPs, which is defective for a reliable result. Sequel to this setback, we develop a new distance measure between IFSs which includes tendency coefficients of the IFPs, where the tendency coefficients are computed from the intuitionistic fuzzy values to enhance reliable results. In addition, the new distance measure between IFSs is applied to discuss students’ admission process to ascertain the most eligible candidate based on academic performance in an entry examination. The application is carried out using two techniques, namely, the recognition principle and the multiple criteria decision-making approach, respectively. Finally, the superiority of the newly developed distance measure between IFSs is shown comparatively with respect to the existing approaches between IFSs. This new distance measure can be applied to clustering analysis, multiple attributes decision-making (MADM), a technique for order preference by similarity to ideal solution (TOPSIS), etc. in future research.

Innovative and astonishing developments in the field of spine analysis can commence with this manuscript. The lumbar disks ($L1-L2$ to $L5-S1$) are most commonly impaired by degeneration due to their long-standing degeneration and associated strain. We investigate the indications, purposes, risk factors, and therapies of lumbar degenerated disc disease (L-DDD). We assume that the degeneration of five discs creates many effects, making it difficult to differentiate between the different types of degenerated discs and their seriousness. Since the indeterminacy and falsity portions of science or clinical diagnosis are often ignored. Due to this complexity, the reliability of the patient’s progress report cannot be calculated, nor can the period of therapy be measured. The revolutionary concept of interval-valued m-polar neutrosophic Choquet integral aggregation operator (IVmPNCIAO) is proposed to eliminate these problems. We associate generalized interval-valued m-polar neutrosophic Choquet integral aggregation operator (GIVmPNCIAO) with the statistical formulation of $L^p$-spaces and use it to identify the actual kind of degenerative disc in the lumbar spine. For the classification of interval-valued m-polar neutrosophic numbers (IVMPNNs), we set the ranking index and score function. These concepts are appropriate and necessary in order to better diagnose degeneration by associating it with mathematical modeling. We construct a pre-diagnosis map based on the fuzzy interval $[0,1]$ to classify the types of degenerative discs. We develop an algorithm by using GIVmPNCIAO based on interval-valued m-polar neutrosophic sets (IVMPNNs) to identify the degenerative disc appropriately and to choose the most exquisite treatment for the corresponding degeneration of every patient. Furthermore, we discuss the sensitivity analysis with parameter $p$ in GIVmPNCIAO to investigate the patient’s improvement record.

Entrepreneurs are a product of their social environment. The manner by which they perceive opportunities; access or process information; and make decisions is, influenced by both social interaction, and their social background. Using insights from Socially Situated Cognition (SSC) theory, that posits one’s social environment can have a normative or informative effect on decision-making process we consider proximal social factors influencing the decision-making processes of student entrepreneurs. We propose that entrepreneurial education, networking, and incubation spaces provide direct information to students to aid entrepreneurial decision-making, and indirect informational cues that are situational, synergistic and omnipresent. Noting the multi-faceted and dynamic nature of the entrepreneurial journey of the student, we explore the potential effect of each of these factors on the student decision-making process. We discuss the implications of this inquiry from a researcher and educator perspective, and note the current challenges faced by student entrepreneurs in a socially distanced educational and entrepreneurial context. It is envisaged that this paper will serve as the basis for further thought and empiricism.

Downtime of rotating equipment in large petrochemical plants often led to serious or even disastrous safety and environmental accidents, which generally stem from inadequate maintenance or incapability of failure prediction. In order to allocate maintenance resources rationally and improve the reliability, availability and safety of equipment, a kind of risk- and condition-based maintenance decision-making and task optimizing system for rotating equipment in large petrochemical plants is established in this paper. Using real-time database, web service and service-oriented architecture (SOA), a risk- and condition-based maintenance decision-making system architecture is developed to provide a unified data structure and man–machine interface, which integrates reliability-centered maintenance (RCM), condition monitoring system (CMS) and manufacturing executive system (MES) together. Risk assessment and condition monitoring technology is applied to form maintenance decision making, such as to determine the priority maintenance level, to optimize maintenance content, and to determine the right maintenance time. Based on the decision-making system, the risk rank and degradation trend of failure characteristics are used to support the decision making and to optimize maintenance tasks. The result of an engineering case shows that the maintenance decision-making based on the risk assessment and condition monitoring can lower the operational risk while enhancing the reliability, availability and safety.