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The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

A numerical study of natural convection of air in a vertical annulus has been conducted, where the inner wall is heated with constant heat flux at its inner side, the outer wall of the annulus being maintained at constant temperature, and the top and bottom plates are assumed to be insulated. The cases of radius ratio K = 3, aspect ratio A = 10∼30, and Ra* = 103∼1.7 × 107 have been simulated. Both axial conduction and surface radiation are taken into account to reveal their effects on the distributions of inner wall temperature and local Nusselt number. Emphasis is on the comparison between the numerical results and the relevant experimental data, and the comparison between numerical solutions with and without considering the surface radiation. The numerical results of heat transfer are found to be in good agreement with the corresponding experimental results in the literature. The dependence of average relative conductivity on aspect ratio and the effect of imperfection in top and bottom insulation on the inner wall temperature are also discussed.

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Greater parental monitoring is commonly associated with reduced delinquent behavior in adolescents, yet less is known about the extent to which parental monitoring behavior changes after a child is arrested for the first time. The present study examines the extent to which mothers’ monitoring behaviors (i.e., parental monitoring knowledge and effort) change in association with juvenile recidivism after their sons’ first arrest, operationalized through both youth-reported recidivism and official re-arrest records. Mother–son dyads (total N = 634) across three states were interviewed in two waves over 30 months following the youth’s first arrest. Mothers who reported both more monitoring knowledge and effort at Wave 1 had sons who self-reported less recidivism and were less likely to be re-arrested at Wave 2. Repeated sons’ re-arrests were associated with a change in mothers’ monitoring behavior, as both parental knowledge and parental effort significantly increased from Wave 1 to Wave 2 when youth have been re-arrested more than once, relative to youth who had never been re-arrested. No change in monitoring behaviors were observed in association with youth-reported recidivism, and mothers who stated an intention to change their monitoring habits at Wave 1 did not necessarily do so by Wave 2. The findings point to the ability of parents to modulate their monitoring behavior to respond to chronic juvenile offending. This provide an opportunity for practitioners to work with parents to improve their monitoring skills, to ensure rehabilitative gains that result from justice system intervention are maintained in the home via parental monitoring.

The purpose of this paper is to analyze the impacts of differences in sanitary and phytosanitary measures as non-tariff measures (NTMs) in the tea trade between importing and exporting countries. With the progress of trade liberalization, there has been a shift of focus to NTMs as alternative or potential trade barriers.

In order to quantify an NTM on tea trade and implement its empirical application, this study designed an index of differences in maximum residue levels (MRLs) for the pesticide endosulfan and introduced it into a gravity trade model. The estimation challenges in the presence of heteroscedasticity and many zero-trade flows are resolved by taking the Heckman and Poisson pseudo-maximum likelihood estimators.

This study found that differences in MRLs, arising from the stricter standards in importing countries lead to a significant decrease in tea trade value. This negative impact of differences in MRLs is found to be slightly less than that of tariffs, implying that in this case, the NTM acts as a policy substitute for import tariffs in the global tea trade.

The main contribution of this study is to suggest and quantify the differences in MRLs across countries as a substantial NTM on the global tea trade and provide its empirical application.

The very contextual nature of most mitigating evidence runs counter to America’s individualistic culture. Prior research has found that capital jurors are unreceptive to most mitigating circumstances, but no research has examined the capital sentencing decisions of trial judges. This study fills that gap through a content analysis of eight judicial sentencing opinions from Delaware. The findings indicate that judges typically dismiss contextualizing evidence in their sentencing opinions and instead focus predominately on the defendant’s culpability. This finding calls into question the ability of guided discretion statutes to ensure the consideration of mitigation and limit arbitrariness in the death penalty.

The purpose of this paper is to develop a multi-criterion group decision-making (MCGDM) method by combining the regret theory and the Choquet integral under 2-tuple linguistic environment and apply the proposed method to deal with the supplier selection problem.

When making a decision, the decision-maker is more willing to choose the alternative(s) which is preferred by the experts so as to avoid the regret. At the same time, the correlative relationships among the criterion set can be sufficiently described by the fuzzy measures, later the evaluations of a group of criteria can be aggregated by means of the Choquet integral. Hence, the authors cope with the MCGDM problems by combining the regret theory and the Choquet integral, where the fuzzy measures of criteria are partly known or completely unknown and the evaluations are expressed by 2-tuples. The vertical and the horizontal regret-rejoice functions are defined at first. Then, a model aiming to determine the missing fuzzy measures is constructed. Based on which, an MCGDM method is proposed. The proposed method is applied to tackle a practical decision-making problem to verify its feasibility and the effectiveness.

The vertical and the horizontal regret-rejoice functions are defined. The relationships of the fuzzy measures are expressed by the sets. A model is built for determining the fuzzy measures. Based on which, an MCGDM method is proposed. The results show that the proposed method can solve the MCGDM problems within the context of 2-tuple, where the decision-maker avoids the regret and the criteria are correlative.

The paper proposes an MCGDM method by combining the regret theory and the Choquet integral, which is suitable for dealing with a variety of decision-making problems.

The “Internet of Things (IoT)” is a platform for involving smart devices via the Internet at a worldwide scale. It supports the “supply chain (SC)” and “information and communication technology (ICT)” infrastructure to be well integrated into an organization and externally with customers and suppliers. The “sustainable supply chain (SSC)” is currently unavoidable if a company seeks to satisfy the aggressive change in its customers' requirements. Numerous studies have confirmed that manufacturing firms have to accelerate the shift of their focus toward sustainability and the implementation of novel technologies, such as IoT, to accomplish their organizational goals most effectively. Although the literature consists of many theoretical approaches to IoT and numerous studies that have extremely concentrated upon the IoT technology and its potential applications, it lacks research with a focus on the challenges that arise when applying IoT to the “sustainable supply chain management (SSCM)”.

The present study proposes an integrated framework using the “Criteria Importance Through Intercriteria Correlation (CRITIC)” and “VlseKriterijumska optimizcija I kaompromisno resenje in Serbian (VIKOR)” models and employs to evaluate the IoT challenges to implement the SSCM. For estimating the criteria weights, the CRITIC tool is utilized. The organization's prioritization is obtained by the VIKOR procedure, which delivers simple mathematical procedures with precise and consistent outcomes.

To exhibit the practicality of the introduced model, a case study is taken to evaluate the IoT challenges to implement the SSCM within the “q-Rung Orthopair Fuzzy Sets (q-ROFSs)” environment. Moreover, the authors exhibit a sensitivity investigation over given parameter values, examining the stability of developed approach. Finally, the authors draw attention to a comparison between developed q-ROF-CRITIC-VIKOR decision-making approach with an existing q-ROF-TOPSIS method to show its superiority and potency.

The outcome of the study lies in observing the top benefits of individual businesses, and their entire SSCs can be found by implementing IoT. This paper investigates the most important challenges that individual firms and entire SSCs might while applying IoT. It provides a deep insight regarding the effects of IoT upon SSCM and the issues every firm need to contemplate when it is to apply IoT solutions.

Sequel to the results of the preceding chapter that depicted positive associations of credit with the indicators of growth and development, the present chapter aims at investigating the interrelationships of credit with GDP and HDI separately in a bivariate framework for the selected countries for the period 1990–2019. For this purpose, this chapter first develops a theoretical model in line with the Barro (1991) model where bank credit is introduced as a good institutional component of endogenous growth. Then, it goes for a time series exercise to establish the long-run relations and short-run dynamics for the pairs of variables, credit-GDP and credit-HDI, to justify the linkages between the financial sector and the real sector. The study arrives at mixed results across the countries. In many cases, credit has been identified to be strongly related to income and development indicators in the long run through cointegrated stable relationships. Furthermore, credit makes a causal influence on GDP and HDI in some developed countries whereas GDP becomes a causal factor to credit in some developing countries. It is thus recommended for further aggravation of the two sectors’ linkages under the patronisations of the governments and the monetary authorities of the countries to have high growth of income and development so that a part of the sustainable development goal can be achieved through the financial sector.

The aim of this research is to establish a new type of aggregation operator based on Hamacher operational law of spherical uncertain linguistic numbers (SULNs).

First, the authors define spherical uncertain linguistic sets and develop some operational laws of SULNs. Furthermore, the authors extended these operational laws to the aggregation operator and developed spherical uncertain linguistic Hamacher averaging and geometric aggregation operators.

The authors were limited in achieving a consistent opinion on the fusion in group decision-making problem with the SULN information.

In order to give an application of the introduced operators, the authors first constrict a system of multi-attribute decision-making algorithm.