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Although teams of negotiators are widely assumed to be better at unlocking integrative solutions compared to individual negotiators, the interteam negotiation context is characterized by unique challenges that can make effective collaboration between teams difficult. We extend our theoretical understanding of interteam negotiations by offering novel insights about when and why teams realize their potential in integrative negotiations. Specifically, we propose a theoretical model that explains how hierarchical team structures reduce information elaboration within teams, which reinforces "fixed-pie" assumptions that prompt the reliance on value-claiming behaviors between teams and lower high-quality outcomes such as the joint gain achieved. Across four studies, each involving interactive team-on-team negotiations, we provide support for the hypothesized effects of formal intrateam hierarchies on joint gain, and test a useful intervention to mitigate the harmful effects of hierarchically structured teams at the negotiation table. Contributions to the literatures on team negotiations, interteam collaboration, and hierarchical differences within teams are discussed. [ABSTRACT FROM AUTHOR]

As a generalization of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets, and their combinations have been developed to represent uncertain, imprecise, incomplete, and inconsistent information existing in the real world. A neutrosophic set has the potential to be a general framework for uncertainty analysis in various spectra. On the other hand, the idea of Fermatean neutrosophic sets (FNSs) is the hybrid model of Fermatean fuzzy sets and neutrosophic sets, developed to enable the analytical management of ambiguous data from relatively typical real-world decision-making scenarios. In this work, we develop an algorithm to compare the Fermatean neutrosophic set and the neutrosophic set, Fermatean Neutrosophic Weighted Arithmetic Mean (FNWAM) and Fermatean Neutrosophic Weighted Geometric Mean (FNWGM) operators as an accuracy function in addition to the conventional aggregating operators are designed. Further, the CODAS technique for Multiple Attribute Group Decision Making (MAGDM) problems according to the defined operators is proposed. Additionally, to make a clear understanding of the presented study, a university faculty selection problem is studied to illustrate the proposed methodology. Finally, the results of the above two sets prove the authenticity of this study. [ABSTRACT FROM AUTHOR]

In the process of multi-attribute group decision-making (MAGDM), the cubic q-rung orthopair fuzzy sets (Cuq-ROFSs) are utilized to express membership and non-membership degrees in the form of interval values to efficiently cope with decision makers' (DMs') complex assessment values. To more efficiently capture DM evaluation results in the MAGDM procedure, we offer a novel tool called 2-tuple linguistic cubic q-rung orthopair fuzzy set (2TLCuq-ROFS), which extends Cuq-ROFS by using 2-tuple linguistic (2TL) terms. 2TLCuq-ROFS effectively incorporates the advantages of 2TL and Cuq-ROFS, making them attractive and versatile for depicting attribute values in an uncertain and complex decision-making environment. To effectively aggregate the attribute values in the form of 2-tuple linguistic cubic q-rung orthopair fuzzy numbers (2TLCuq-ROFNs), some Maclaurin symmetric mean (MSM) operators and their weighted forms are presented in this paper. The weight information for attributes is unknown. Therefore, the criteria importance through inter-criteria correlation (CRITIC) method is employed to determine the objective weight information. The purpose of this study is to incorporate a conventional multi-attributive border approximation area comparison (MABAC) framework based on 2TLCuq-ROFNs because it addresses problematic and imprecise decision-making problems by calculating the distance among each alternative and the border approximation area by using 2TLCuq-ROFNs and MSM aggregation operators. First, some basic concepts associated with 2TLCuq-ROFNs and the CRITIC-MABAC procedure are briefly explained. Moreover, an evaluation framework based on the improved CRITIC-MABAC method is established. An explanatory case study related to the risk investment problem in Belt and Road is used to verify the validity and practicality of the designed evaluation framework. In conclusion, by utilizing the CRITIC-MABAC methodology based on proposed operators, we find that Λ 7 is the optimal alternative for risk investment. Furthermore, comparison analysis emphasizes the integrity and prominent features of the proposed methodology and provides various complementary perspectives for investors. [ABSTRACT FROM AUTHOR]

In real-world complex group decision-making problems, preference inconsistency and opinion conflict are common and crucial challenges that need to be tackled. To promote consensus reaching, a novel group consensus reaching model is constructed considering individual satisfaction and group fairness. This study focuses on managing the group consensus reaching process based on flexible and adaptable information, modelled as distributed linguistic preference relations (DLPRs). First, a building process for DLPRs is discussed by integrating cumulative distribution functions converted from single linguistic term sets, hesitant fuzzy linguistic term sets, and comparative linguistic expressions. Furthermore, a two-stage consistency improvement method is proposed, which makes a trade-off between the frequency and magnitude of adjustments. Finally, we establish an improved group consensus model to balance individual satisfaction and group fairness, where individual satisfaction is measured by the deviation between the original and revised preferences and group fairness is measured by the deviation between individual satisfactions. The emergency response plan selection is conducted to show the validity and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members' influence and leader impact. In this paper, a model has been proposed to compute members' influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders' judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms. [ABSTRACT FROM AUTHOR]

This study investigated the myriad risk factors contributing to cost overruns in healthcare construction projects. It determined the impact of managerial, financial, workerpower, and environmental considerations, focusing on the intricacies of healthcare facilities. By employing a modified fuzzy group decision-making approach (MFGDMA), the research methodically quantified the subjective perceptions of diverse stakeholders involved in project execution. Results reveal that labor-related issues—such as shortages, productivity, and skill deficits—along with managerial delays, particularly in decision-making and change orders, significantly contribute to cost escalations. The study's novelty lies in its integration of fuzzy logic to defuzzify risk perceptions, providing a clear hierarchy of risk factors, which assists in the strategic prioritization of risk mitigation efforts. Additionally, it demonstrates a risk response plan considering the fuzzy-based risk assessment findings, which can serve as a hands-on tool guiding healthcare project practitioners to develop their risk management plans and monitor cost overrun risks throughout the execution phases of a project. The application of the research findings suggests policy implications for improved risk management frameworks toward the development of more-resilient and -efficient strategies for managing healthcare construction projects without prohibitive cost overruns. [ABSTRACT FROM AUTHOR]

In linguistic decision theory, multi-attribute group decision-making (MAGDM) of linguistic neutrosophic numbers (LNNs) is one of the crucial research topics. Existing LNN aggregation algorithms do not consider trigonometric periodicity. In this case, they cannot perform MAGDM problems including periodic/multitemporal applications in LNN scenarios. To fill this gap, this article aims to present a MAGDM technique using the LNN tangent Dombi aggregation operators (TDAOs) for addressing MAGDM issues including multitemporal/periodic applications in LNN scenarios. First, we present the linguistic tangent Dombi t-norm and t-conorm and the LNN tangent Dombi operation laws. Second, we propose the LNN tangent Dombi weighted average and geometric operators and investigate their properties. Third, a MAGDM technique is built based on the two presented operators to tackle MAGDM problems in a LNN scenario. Lastly, the built MAGDM technique is applied to a choice of new energy design schemes in Ningbo City, China, and then its efficiency and appropriateness are verified by comparing with the existing MAGDM technique in the LNN scenario. [ABSTRACT FROM AUTHOR]

Multi-attribute group decision-making is an important research field in decision science, and its theories and methods have been widely applied to engineering, economics and management. However, as the information embedded volume and complexity of decision-making expand, the diversity and heterogeneity of decision-making groups present significant challenges to the decision-making process. In order to effectively address these challenges, this paper defines the concept of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making model in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency in decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual regret, and balances the gains and losses of various options in the decision-making process. Finally, in response to the 3R principle, the model is employed to address the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the model is robust in the context of varying parameter configurations. Moreover, the correlation coefficients between its ranking outcomes and those of alternative methodologies are all above 0.77, and its average superiority degree is 1.121, which is considerably higher than that of other methods. Consequently, the model's effectiveness and superiority are substantiated. [ABSTRACT FROM AUTHOR]

In many aspects of life on earth, individuals may engage in cooperation with others to contribute towards a goal they may share, which can also ensure self-preservation. In evolutionary game theory, the act of cooperation can be modelled as an altruistic act of an individual producing some form of benefit that can be utilised by others they are associated with at some personal cost. In various scenarios, individuals make use of information that they can perceive within a group to aid with their decision-making regarding who they should associate and cooperate with. However, cooperative individuals can be taken advantage of by opportunistic defectors, which can cause significant disruption to the population. We study a model where the decision to establish interactions with potential partners is based on the opportune integration of the individual's private ability to perceive the intentions of others (private information) and the network position (ranking) of potential partners (public information). We find that there are rankings, such as degree and eigenvector, which can lead to a significant increase to the prosperity of the network, but this greatly increases the likelihood of a network succumbing to cheater invasion. Other rankings, such as betweeness, can instead lead to more stable resilient networks whilst also cultivating some degree of payoff. Our results highlight how commonly used network rankings can be utilised to aid with connection formation within networks and in turn can be utilised to improve the well-being of these networks, helping with stability and allowing for individuals to cultivate cooperation amongst each other. Private information should also continue to be considered when examining the dynamics of these networks as it appears to be a primary driver of encouraging individual agency. [ABSTRACT FROM AUTHOR]

The overlap function, a continuous aggregation function, is widely used in classification, decision-making, image processing, etc. Compared to applications, overlap functions have also achieved fruitful results in theory, such as studies on the fundamental properties of overlap functions, various generalizations of the concept of overlap functions, and the construction of additive and multiplicative generators based on overlap functions. However, most of the research studies on the overlap functions mentioned above contain commutativity and continuity, which can limit their practical applications. In this paper, we remove the symmetry and continuity from overlap functions and define discrete pseudo-quasi overlap functions on finite chains. Meanwhile, we also discuss their related properties. Then, we introduce pseudo-quasi overlap functions on sub-chains and construct discrete pseudo-quasi overlap functions on finite chains using pseudo-quasi overlap functions on these sub-chain functions. Unlike quasi-overlap functions on finite chains generated by the ordinal sum, discrete pseudo-quasi overlap functions on finite chains constructed through pseudo-quasi overlap functions on different sub-chains are dissimilar. Eventually, we remove the continuity from pseudo-automorphisms and propose the concept of pseudo-quasi-automorphisms. Based on this, we utilize pseudo-overlap functions, pseudo-quasi-automorphisms, and integral functions to obtain discrete pseudo-quasi overlap functions on finite chains, moreover, we apply them to fuzzy multi-attribute group decision-making. The results indicate that compared to overlap functions and pseudo-overlap functions, discrete pseudo-quasi overlap functions on finite chains have stronger flexibility and a wider range of practical applications. [ABSTRACT FROM AUTHOR]

The household waste (HW) disposal and recycling have become a significant challenge due to increasing quantities of generated household wastes and increased levels of urbanization. Selecting locations/sites for building new HW recycling plant comprises numerous sustainability dimensions, thus, this work aims to develop new decision-making model for evaluating and prioritizing the HW recycling plant locations. This paper is categorized into three phases. First, we propose new improved score function to compare the Fermatean fuzzy numbers. Moreover, an example is presented to validate the effectiveness of proposed score function over the extant ones. Second, we introduce new distance measure to estimate the discrimination degree between Fermatean fuzzy sets (FFSs) and further discuss its advantages over the prior developed Fermatean fuzzy distance measures. Third, we introduce an integrated methodology by combining the method with the removal effects of criteria (MEREC), the stepwise weight assessment ratio analysis (SWARA) and the measurement alternatives and the ranking according to compromise solution (MARCOS) approaches with Fermatean fuzzy (FF) information, and named as the "FF-MEREC-SWARA-MARCOS" framework. In this method, the FF-distance measure is used to find the weights of involved decision-making experts. Moreover, an integrated criteria weighting method is presented with the combination of MEREC and SWARA models under the context of FFSs, while the combined FF-MEREC-SWARA-MARCOS model is applied to evaluate and prioritize the locations for HW recycling plant development, which illustrates its feasibility of the developed framework. Comparative study and sensitivity assessment are conducted to validate the obtained outcomes. This work provides a hybrid decision analysis approach, which marks a significant impact to the HW recycling plant location selection process with uncertain information. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts. [ABSTRACT FROM AUTHOR]

Some new techniques of aggregating hesitant fuzzy numbers (HFNs) by using monotonic dependent OWA (MDOWA) operators are investigated. By utilizing the score value of HFN, the concepts of hesitant fuzzy configuration vector and hesitant fuzzy hybrid configuration vector are proposed. Then, some methods of calculating variable weights related to the MDOWA operators under hesitant fuzzy environments are presented. Further, some operators, including hesitant fuzzy monotonic dependent OWA (HFMDOWA) operators and hesitant fuzzy hybrid monotonic dependent OWA (HFHMDOWA) operators, are developed, such as balanced HFMDOWA operators, rewarded HFMDOWA operators, balanced HFHMDOWA operators, rewarded HFHMDOWA operators, and so on. These developed operators are applied to multiple criteria group decision making (MCGDM), and a novel MCGDM algorithm is presented. By using the presented operators and algorithm, we can obtain symmetric decision-making results. Finally, an application example is provided to demonstrate the effectiveness of the developed MCGDM techniques. [ABSTRACT FROM AUTHOR]

Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and the trapezium cloud model are powerful tools for expressing uncertainty and randomness. This paper aims to combine these methodologies. First, we review relevant concepts and operators, introducing a novel combination of IVLNSs and Z-numbers, which establishes a new form of expression. Subsequently, we propose the Z-interval-valued linguistic neutrosophic set-trapezium–trapezium cloud (Z-IVLNS-TTC) model, designed to minimize information loss and distortion during quantification. A novel method for calculating the objective weight vector is then developed using multi-objective programming (MOP). Drawing inspiration from the TOPSIS method, we propose a new approach for calculating the distance between Z-IVLNS-TTCs based on the p-norm. Finally, a group decision-making problem is presented to demonstrate the practical application of the proposed method. To validate the effectiveness and feasibility of the method, sensitivity analysis and comparisons with existing approaches are conducted. [ABSTRACT FROM AUTHOR]

Circular economy and sustainable growth are closely linked as they both aim to reconcile economic development with environmental considerations. The circular economy provides a framework and set of principles for achieving sustainable growth. By adopting circular practices, such as resource efficiency, recycling, and product life extension, economic activities can become more sustainable and contribute to long-term growth. In today's world, consumers have high expectations for companies to be accountable for the environmental and social impact of their products and services. The implementation of Total Quality Management (TQM) in manufacturing can contribute to sustainable growth by enhancing quality, reducing waste, and increasing efficiency. This study proposes a novel methodological framework to establish a comprehensive association between Juran's ten quality principles using a mixed-method sequential approach with an integrated Machine Learning Group Decision-Making (MLGDM) and Interpretive Structural Modeling (ISM)-DEMATEL approach. The framework involves using the MLGDM approach to select the optimal number of experts to develop contextual relationships among the principles. This framework is designed to address the challenge of determining the appropriate number of experts to involve in the decision-making process. Involving too few experts can limit the generalizability of the results, while having too many experts can lead to a high degree of inconsistency and make it challenging to reach a consensus. The MLGDM portion of our framework provides a systematic approach to overcome this challenge and helps supply chain managers and academicians implement quality practices in their organizations. Moreover, although several studies have explored the implementation of TQM practices, there is still a lack of a systematic framework that can fully incorporate Juran's quality principles. To fill this gap, the ISM-DEMATEL approach was then used to explore the causal relationships between these principles. Practitioners from the industry were asked to identify contextual associations among variables, which facilitated a better understanding of these principles. Our results suggest that "Build awareness," "Set goals for improvement," and "Provide training" are strategic requirements for successful TQM implementation, while "Carry out projects to solve problems," "Organize to reach the goals," and "Keep score" are tactical requirements. Furthermore, "Communicate results," "Report progress," "Give recognition," and "Maintain momentum" are operational necessities for TQM implementation. The present study represents a significant step forward in giving a new direction to Juran's ten quality principles and providing a holistic picture to decision-makers. [ABSTRACT FROM AUTHOR]

In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry. [ABSTRACT FROM AUTHOR]

The process of selecting contractors for megaprojects involves inherent uncertainty in judgment and preferences, especially under multiple criteria group decision-making (MCGDM) scenarios. This uncertainty often stems from the ambiguity and hesitation when decision makers (DM) evaluate and prioritize different criteria, causing challenges for the evaluation of contractors' technical capabilities. This study aims to enhance a generalized comparison table (GCT) by applying hesitant fuzzy soft sets to capture this uncertainty. Through the operation of a criteria adjustment algorithm for group consensus, experts-trust network, and the rule of maximum deviation, the GCT is improved by the ability to incorporate objective weights of subjective criteria. The improved GCT provides a transparent evaluation tool and has been tested on contractors' assessments for a mega tunneling project in China. Its practicality is demonstrated by matching the method-assisted decision with the final choice of the project manager. The key research findings highlight the importance of incorporating project-specific criteria, the criteria adjustment algorithm, and the objective weights of criteria to refine MCGDM. The main contributions of this study include (1) a novel framework of the improved GCT for MCGDM, (2) a new criteria system that includes common categories and a project-specific criteria category, (3) a criteria adjustment algorithm to attain group consensus decisions, and (4) objective weights of criteria and DM based on the experts-trust network and maximum deviation model. Practical Applications: Selecting the right contractor for megaprojects can be a daunting task filled with uncertainty. This uncertainty primarily arises from the ambiguity and hesitation when decision-makers evaluate various technical capabilities of different contractors. This study, therefore, aims to improve a method to make the decision-making process more efficient and dependable, particularly in scenarios involving multiple decision-makers and criteria. The improved method incorporates various factors, from technical ability to project-specific criteria, ensuring a comprehensive evaluation of potential contractors. For practicality, it utilizes a fuzzy evaluation language to capture the decision-makers' uncertainty during the evaluation process. Additionally, the study uses a specific algorithm method to generate a group consensus result. The improved method also applies objective weights of both criteria and decision-makers to help reduce subjective evaluation. Through a case study, it is further found that the improved method is beneficial for inviting disciplinary decision-makers to join the megaproject evaluation process. The improved method is recommended for evaluating tenderer megaprojects, especially when decision-makers' backgrounds are diverse and the accurate assessment of technical capabilities is critical. For practitioners interested in applying this method to their own projects, the programming code of this study is available for reference and use. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

Individuals' emotions, such as hesitation and unwavering confidence, can influence the ability of decision-makers (DMs) to make rational judgments. The emotion is always hidden in individual preference series, which is referred to as emotion soft factors, It is a prerequisite for avoiding unfavorable impacts on consensus reaching process. This study focuses on structuring a consensus model with emotion soft factors in linguistic preference time sequence. Specifically, a personalized individual semantics (PIS) learning process is implemented to obtain the personalized numerical scales of DMs' linguistic terms. Subsequently, we propose a consensus model incorporating the consensus measurement and feedback modification phase. In the process, a grey clustering scheme is devised to mine emotion soft factors from DMs' preference sequences and manage individuals in different grey classes. Finally, numerical examples, simulation analysis, and comparison study are presented to illustrate the influence of different parameters and justify the validity of the proposed model. [ABSTRACT FROM AUTHOR]

In China, communities function as grassroots self-governing bodies, and the enhancement of public participation in community governance has remained a central focus of study. This paper applies the Large-Scale Group Decision-Making (LSGDM) method to the process of community self-governance and proposes a dual feedback group consensus decision-making model that takes into account the unique social relations among residents. Firstly, the concept of the Organizational Invisible Field—formed in communities by intangible social capital such as positional power and interpersonal relationships within the organization—is introduced. The definition of Invisible Field Force is utilized to measure the influence of these forms of capital on social relationships. Subsequently, drawing on field dynamic theory, the process by which residents' preferences within the organization are shaped by the action of Invisible Field Force is explored. Secondly, acknowledging that invisible relationships can be affected by dynamic interactions during the decision-making process, the Invisible Field Force change model is constructed. Building on this, a dual feedback consensus coordination mechanism—encompassing both in-organization members and all residents—is designed. Finally, the validity and utility of the model are verified through case studies and sensitivity analyses. [ABSTRACT FROM AUTHOR]

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics. [ABSTRACT FROM AUTHOR]

The fractional orthotriple fuzzy set (FOFS) model is a recently created extension of fuzzy sets (FS) for coping with ambiguity in DM. The purpose of this study is to define new exponential and Einstein exponential operational (EO) laws for fractional orthotriple fuzzy sets and the aggregation procedures that accompany them. We present the operational laws for exponential and Einstein exponential FOFSs which have crisp numbers as base values and fractional orthotriple fuzzy numbers as exponents (weights). The proposed operations' qualities and characteristics are then explored. Based on the defined operation laws regulations, various new FOFS aggregation operators, named as fractional orthotriple fuzzy weighted exponential averaging (FOFWEA), fractional orthotriple fuzzy ordered weighted exponential averaging (FOFOWEA), fractional orthotriple fuzzy hybrid weighted averaging (FOFHWEA), fractional orthotriple fuzzy Einstein weighted exponential averaging (FOFEWEA), fractional orthotriple fuzzy Einstein ordered weighted exponential averaging (FOFEOWEA), and fractional orthotriple fuzzy Einstein hybrid weighted exponential averaging (FOFEHWEA) operators are presented. A decision-making algorithm based on the newly defined aggregation operators is proposed and applied to a multicriteria group decision-making (MCGDM) problem related to bank security. Finally, we compare our proposed method with other existing methods. [ABSTRACT FROM AUTHOR]

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes. [ABSTRACT FROM AUTHOR]

Simple Summary: An increase in game meat consumption has been observed worldwide. This is the first study to assess the amino acid composition and the protein quality of the muscles of deer from organic feeding grounds. This study assessed the nutritional value of the protein of the muscles of deer originating from forest, organic, and conventional feeding grounds. The meat of deer from the forest and the conventional feeding grounds exhibited higher exogenous and endogenous amino acid contents than that of deer from the organic feeding ground. However, the meat of deer from the organic farm feeding ground is characterized by the highest protein content and ensures optimal satisfaction of the demand for essential amino acids. The highest lysine content was found in the muscles of the deer from the forest feeding ground. This knowledge can help nutritionists develop diets that meet the nutritional recommendations for different consumer groups and breeders to make decisions about farm location and deer feeding strategies. This study aimed to analyze the amino acid profile, with a particular focus on the nutritional value of the protein of the longissimus lumborum (LL) and the semimembranosus (SM) muscles of deer originating from three feeding grounds: forest (FFG); conventional (CFG) grounds; organic farm (OFG). This is the first time that deer from an organic farm feeding ground have been included in this study. The muscles were collected from 36 deer carcasses with equal proportions of sex and 31 months of age. This study demonstrated significantly higher essential amino acid (EAA) and non-essential amino acid (NEAA) contents in the muscles of deer from the FFG and CFG compared to the OFG. However, the EAA-to-NEAA ratio was significantly higher for the muscles of deer from the OFG. The muscles of the FFG and CFG deer were characterized by a higher concentration of lysine as well as acidic and tasty amino acids compared to the OFG deer, with the muscles of the latter exhibiting a higher percentage of branched-chain amino acids (BCAA). The results obtained can be used professionally by nutrition specialists in preventive and therapeutic diets and breeders to make decisions about farm location and deer feeding strategies. [ABSTRACT FROM AUTHOR]

Online and offline integration is an increasingly popular method of performing modern medical services. To provide suggestions for physician selection by patients with full use of previous online and offline patient evaluations, this paper investigates online and offline social network group decision making (OAOSNGDM) in depth. With the aim of inferring the indirect trust relationships of online and offline patients, we first construct a q-rung orthopair fuzzy dual trust propagation operator based on the q-rung orthopair fuzzy trust function, which can effectively deal with inconsistency in trust functions among patients. Considering patient inconsistency in online and offline scenarios, which can increase the uncertainty of the trust relationship in cross-network propagation, we propose a q-rung orthopair fuzzy dual trust cross-network propagation operator by introducing cross-network propagation efficiency. Considering the signal-to-noise ratio, we calculate the trust propagation efficiency and introduce it into the trust propagation operators. To aggregate the trust information of multiple trust paths among patients, we introduce the Dempster rule from evidence theory which can handle the uncertainty of trust functions. In addition, to accurately determine the patient weights according to online and offline social networks, we integrate the ranking results of patients in terms of degree centrality, neighbor importance and betweenness centrality by developing an improved linear assignment method. We then propose a novel decision-making method for OAOSNGDM and design a complete decision-making process for the evaluation of physicians. Finally, we verify the effectiveness of our proposed method for the evaluation of physicians in an online and offline scenario. [ABSTRACT FROM AUTHOR]

The consensus reaching process (CRP) is often utilized to mitigate conflicts among decision makers and help them reach an agreement. Almost all existing CRP models focused on obtaining a consensual complete ranking of alternatives. However, in many situations, it may be sufficient to classify alternatives into a number of ordinal classes rather than a complete ranking. To this end, this study proposes an ordinal classification-based CRP (OCCRP) with the aim to generate consensual ordinal classes of alternatives. Further, this study investigates the OCCRP in the large-scale multi-attribute group decision making context, in which flexible linguistic expression is used to model uncertain opinions of decision makers. In the OCCRP, an approach is proposed to manage flexible linguistic expression by transforming it into linguistic distribution assessment. Then, an opinion clustering and aggregation approach is used to deal with the assessment information of the large group. Next, an approach-based technique for order performance by similarity to ideal solution is used to obtain the ordinal classification of alternatives. Following this, a feedback adjustment mechanism is presented to help decision makers improve their consensus level on the ordinal classification of alternatives. Finally, a numerical example about grape wine grading problem and a comparative analysis are presented to demonstrate the validity of the proposal. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a mathematical programming model that calculates the relative efficiency of homogenous Decision Making Units (DMUs). The conventional DEA models used to calculate the efficiency require the exact amount of inputs and outputs; in real business situations, however, it is often impossible to determine the exact numeral value of some inputs and outputs. At the same time the Common Set of Weights (CSW) overcomes the weakness of DEA models for assessment under same conditions. On the other hands, it is important to considering the balance in evaluation and calculation of indicators. This study develops a new model to calculate the CSW in fuzzy environments, considering the balanced environment using the Balanced Scorecard (BSC). Our proposed model is linear for fairly and equitably evaluating the DMUs on the same scale, also enables us to deal with fuzzy environment and greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in fuzzy DEA models. From a managerial point of view, this paper aims to provide an integrated framework to form a better strategic decision-making process about organization performance, which ultimately leads to the competitive advantages and success of the organization in the long run. Finally, in the field of performance management, the proposed model was applied to evaluate the performances of ten manufacturing enterprises in to confirm the validity and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Decision-making plays a crucial role in achieving success across various scenarios, especially when confronted with complex issues inundated with abundant facts and information. Employing multicriteria decision-making (MCDM) methods and techniques becomes particularly indispensable in tackling such formidable challenges. This study introduces novel neutrosophic SWGM and SWAM accuracy functions, which enhance traditional aggregation operators. Furthermore, it introduces the CODAS technique tailored for addressing Multiple Attribute Group Decision Making problems utilizing the newly defined operators. To exemplify the proposed methodology, a supplier selection problem is examined. [ABSTRACT FROM AUTHOR]

In this paper, we show that it is possible to obtain an optimal configuration variant of Modular Strongrooms (MSR) that satisfies the individual requirements of customers and is most economically advantageous for manufacturers. A model of the automatic configuration system for configuring MSR type MODULPRIM was developed, integrating the procedures for generating product variants, choosing the optimal configuration, and designing detailed products and technological processes. The developed model's importance lies in its ability to automatically select the optimal configuration from a set of possible configurations on a multidisciplinary basis. The problem of choosing was solved by integrating the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods from the group of Multi-Attribute Decision Making (MADM). Validation of the proposed model was performed on eight examples of Modular Strongrooms type MODULPRIM 5 and showed great opportunities to improve efficiency and effectiveness in the process of innovative product development, as well as to obtain a product configuration with significantly improved quality. The proposed model has a high degree of flexibility and universality; thus, it can be further upgraded and integrated into a company's business system. [ABSTRACT FROM AUTHOR]

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Identifying the most optimal wearable health technology devices for hospitals is a crucial step in emergency decision-making. The multi-attribute group decision-making method is a widely used and practical approach for selecting wearable health technology devices. However, because of the various factors that must be considered when selecting devices in emergencies, decision-makers often struggle to create a comprehensive assessment method. This study introduced a novel decision-making method that took into account various factors of decision-makers and has the potential to be applied in various other areas of research. First, we introduced a list of aggregation operators based on Pythagorean hesitant fuzzy rough sets, and a detailed description of the desired characteristics of the operators under investigation were provided. The proposed operators were validated by a newly defined score and accuracy function. Second, this paper used the proposed approach to demonstrate the Pythagorean hesitant fuzzy rough technique for order of preference by similarity to ideal solution (TOPSIS) model for multiple attribute decision-making and its stepwise algorithm. We developed a numerical example based on suggested operators for the evaluation framework to tackle the multiple-attribute decision-making problems while evaluating the performance of wearable health technology devices. In the end, the sensitivity analysis has confirmed the performance and reliability of the proposed framework. The findings indicated that the models being examined demonstrated greater reliability and efficacy compared to existing methodologies. [ABSTRACT FROM AUTHOR]

Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods. [ABSTRACT FROM AUTHOR]

Cognitive differences can catalyze social learning through the process of one-to-one social influence. Yet the learning benefits of exposure to the ideas of cognitively dissimilar others often fail to materialize. Why do cognitive differences produce learning from interpersonal influence in some contexts but not in others? To answer this question, we distinguish between cognition that is expressed—one's public stance on an issue and the way in which supporting arguments are framed—and cognition that is latent—the semantic associations that underpin these expressions. We theorize that, when latent cognition is obscured, one is more likely to be influenced to change one's mind on an issue when exposed to the opposing ideas of cognitively dissimilar, rather than similar, others. When latent cognition is instead observable, a subtle similarity-attraction response tends to counteract the potency of cognitive differences—even when social identity cues and other categorical distinctions are inaccessible. To evaluate these ideas, we introduce a novel experimental paradigm in which participants (a) respond to a polarizing scenario; (b) view an opposing argument by another whose latent cognition is either similar to or different from their own and is either observable or obscured; and (c) have an opportunity to respond again to the scenario. A preregistered study (n = 1,000) finds support for our theory. A supplemental study (n = 200) suggests that the social influence of latent cognitive differences operates through the mechanism of argument novelty. We discuss implications of these findings for research on social influence, collective intelligence, and cognitive diversity in groups. This paper was accepted by David Simchi-Levi, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00895. [ABSTRACT FROM AUTHOR]

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Under the background of big data, the evaluation of college students' satisfaction with online ideological and political education (IAPE) is primarily achieved through data mining and analysis techniques, allowing for a more comprehensive and accurate reflection of students' attitudes and feedback. By collecting data through online surveys, social media interactions, and other channels, educators can adjust the content and methods of teaching in real-time to better meet students' needs and improve the effectiveness and satisfaction of IAPE. The satisfaction evaluation of college students' online IAPE in the context of big data is a multiattribute group decision-making (MAGDM) problem. Recently, VIKOR method have been applied to address MAGDM challenges. Single-valued neutrosophic sets (SVNSs) are employed as a tool to represent uncertain data in the satisfaction evaluation of college students' online IAPE within the big data context. In this paper, we propose the single-valued neutrosophic number VIKOR (SVNN-VIKOR) method to solve MAGDM problems under SVNSs. Finally, a numerical case study is presented to validate the effectiveness of the proposed method in evaluating the satisfaction of college students' online IAPE in the context of big data. [ABSTRACT FROM AUTHOR]

Computer network security (CNS) evaluation is crucial for protecting information assets and maintaining the stable operation of network systems. Through this evaluation, potential threats and vulnerabilities within the network can be systematically identified, effectively preventing possible cyber-attacks and data breaches. Additionally, network security evaluation helps enterprises and organizations allocate security resources appropriately, enhancing their ability to respond to emergencies and ensuring business continuity and data integrity. In summary, network security evaluation is one of the foundational tasks for achieving continuous, secure, and stable network operations. The CNS evaluation is multiple-attribute group decision making (MAGDM). Then, the generalized TODIM(GTODIM) approach has been developed to deal with MAGDM. The interval neutrosophic sets (INSs) are defined as an effective tool for representing uncertain data during the CNS evaluation. In this manuscript, the interval neutrosophic number generalized TODIM (INN-GTODIM) approach is proposed to put forward the MAGDM under INSs. Finally, numerical example study for CNS evaluation is introduced to validate the INN-GTODIM approach. The primary contributions of this study are outlined: (1) Deriving weight information through the application of average method, enhancing the robustness of the decision-making process; (1) Implementing the INN-GTODIM approach for MAGDM scenarios that utilize INSs, showcasing its adaptability to complex decision-making environments; (1) Applying the INN-GTODIM approach specifically to the domain of CNS evaluation, and comparing its performance against existing approaches to highlight its strengths and improvements; (4) Providing a detailed comparative analysis, which clearly demonstrates that the INN-GTODIM approach significantly enhances the effectiveness of CNS evaluations. [ABSTRACT FROM AUTHOR]

Choosing building materials suppliers is crucial as it directly affects the quality, cost, and timeline of a project. Quality suppliers ensure the reliability and consistency of materials, reducing construction risks. The right suppliers offer competitive pricing and flexible delivery schedules, helping control budgets and optimize resource use. Additionally, suppliers' professional support and technical services can enhance construction efficiency and quality. By carefully selecting suppliers, project teams can establish long-term partnerships, improving overall construction standards and market competitiveness. Thus, supplier selection is a key factor in the success of construction projects. The process of selecting and applying building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) method. This study constructs the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) approach, which integrates the grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). To validate the method, a numerical example for building material supplier selection is provided, accompanied by comparative studies to highlight the effectiveness of the 2TLNN-CGRA approach. The key contributions of this research include (1) the utilization of the 2TLNN-CGRA approach to address MAGDM under 2TLNSs conditions; (2) the incorporation of both 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) within the 2TLNSs framework in the 2TLNN-CGRA approach; (3) the demonstration of the method through a numerical example for supplier selection; and (4) a series of efficient comparisons with other existing decision-making approaches. [ABSTRACT FROM AUTHOR]

The significance of evaluating the design quality of Maximum Power Point Tracking (MPPT) controllers in photovoltaic systems lies in ensuring efficient and stable operation under various environmental conditions. By assessing the controller's tracking efficiency, dynamic response speed, steady-state error, and disturbance rejection capability, the system's energy output can be optimized, equipment lifespan extended, operating costs reduced, and overall system reliability and performance improved, thereby maximizing solar energy utilization efficiency. The quality evaluation of MPPT controller design for photovoltaic systems is framed as a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) approach has been introduced to address this type of decision-making. Single-valued neutrosophic sets (SVNSs) are employed as a decision-making tool to represent fuzzy data during the quality evaluation process of MPPT controller design in photovoltaic systems. In this study, a method combining the single- valued neutrosophic number and Exponential TODIM (SVNN-Com-ExpTODIM), based on the SVNN Hamming distance (SVNNHD) and SVNN Euclidean distance (SVNNED), is proposed to solve the MAGDM problem under SVNSs. Finally, a numerical study is conducted to demonstrate the effectiveness of the SVNN-Com-ExpTODIM approach for the quality evaluation of MPPT controller design in photovoltaic systems, supported by comparative analysis. [ABSTRACT FROM AUTHOR]

University educational management quality evaluation is a systematic assessment of various elements involved in the educational management process of higher education institutions. Its goal is to analyze aspects such as management systems, resource allocation, faculty quality, student services, and curriculum design, measuring the impact of management practices on teaching outcomes to ensure educational objectives are met. This evaluation system helps improve the operational efficiency and teaching quality of universities, enhancing their reputation and aligning talent cultivation with societal needs. The quality evaluation of university educational management is a task well-suited for multi-attribute group decision-making (MAGDM). Recently, the Exponential TODIM (ExpTODIM) and VIKOR methods have been introduced to address MAGDM challenges. For handling uncertain data during this evaluation, the 2-tuple linguistic neutrosophic sets (2TLNSs) have been introduced as an effective tool. In this research, the 2-tuple linguistic neutrosophic number ExpTODIM-VIKOR (2TLNN-ExpTODIM-VIKOR) method is introduced to tackle MAGDM challenges using 2TLNSs. Furthermore, a numerical study focusing on the quality evaluation of university educational management is conducted to validate the effectiveness of the 2TLNN-ExpTODIM-VIKOR approach. [ABSTRACT FROM AUTHOR]

The evaluation of public opinion management capability for public crises in universities assesses the ability of universities to guide and handle public opinion during crises. The evaluation covers areas such as opinion monitoring, crisis response, information dissemination, and media communication. Through a systematic assessment, universities can improve their crisis management capabilities, reduce the spread of negative public opinion, and maintain campus stability and reputation. The public opinion management capability evaluation for public crises in universities is multiple-attribute group decision-making (MAGDM). Recently, the Logarithmic TODIM (LogTODIM) technique was interpreted to cope with MAGDM. The single-valued neutrosophic sets (SVNSs) are interpreted as decision tools for characterizing fuzzy data during the public opinion management capability evaluation for public crises in universities. In this study, the single-valued neutrosophic number combined Logarithmic TODIM (SVNN-Com-LogTODIM) technique based on the SVNN Hamming distance (SVNNHD) and SVNN Euclidean distance (SVNNED) is interpreted to solve the MAGDM under SVNSs. Conclusively, numerical study for public opinion management capability evaluation for public crises in universities is interpreted to elucidate the SVNN-Com-LogTODIM technique through comparative analysis. [ABSTRACT FROM AUTHOR]

College English holds great importance in Chinese universities as one of the largest, most influential, and highly regarded foundational courses. Despite significant progress in reforming college English teaching (CET), its effectiveness has faced widespread criticism. To ensure and enhance the quality of CET, it is essential to explore the establishment of a quality evaluation mechanism. One such mechanism is the multiple-attribute group decision-making (MAGDM) approach. Recently, the Exponential TODIM (ExpTODIM) and TOPSIS techniques have been employed to address MAGDM challenges. To capture uncertain data during the evaluation process, interval neutrosophic sets (INSs) are administrated as a valuable tool. This study introduces the interval neutrosophic number Exponential TODIM-TOPSIS (INN-ExpTODIM-TOPSIS) approach to address MAGDM using INSs. Eventually, anumerical study for CET quality evaluation is administrated to validate the INN-ExpTODIM-TOPSIS approach for evaluating the quality of CET. [ABSTRACT FROM AUTHOR]

The evaluation of university physical education teaching quality helps measure the actual effectiveness of the courses and ensures the achievement of teaching objectives. Through evaluation, schools can identify deficiencies in teaching and make improvements, enhancing students' physical fitness and health. Additionally, evaluation provides data support for teaching reforms, aiding in curriculum optimization and strengthening faculty, thereby promoting the comprehensive development of physical education and fostering students' lifelong awareness of fitness and healthy lifestyles. The teaching quality evaluation of university physical education adopts the multiple- attribute group decision-making (MAGDM) approach. Currently, cross-entropy (CE) model was constructed to handle the MAGDM. To capture uncertain data for the teaching quality evaluation of university physical education, 2-tuple linguistic neutrosophic numbers (2TLNNs) are administrated as valuable decision tool. This study constructs the 2TLNN cross-entropy (2TLNN-CE) technique, which combines traditional cross-entropy and 2TLNSs. The 2TLNN-CE technique is applied to MAGDM in the context of teaching quality evaluation. Furthermore, numerical example for the teaching quality evaluation of university physical education is presented, and comparative analysis is administrated to validate the advantages of 2TLNN-CE technique. The contributions of this study are summarized:(1) Extension of cross-entropy technique to 2TLNSs, enabling its application in uncertain decision scenarios; (2) Introduction of 2TLNN-CE technique for MAGDM, specifically tailored for teaching quality evaluation; (3) Illustration of 2TLNN-CE technique through numerical example of teaching quality evaluation of university physical education accompanied by comparative analysis to highlight its benefits. [ABSTRACT FROM AUTHOR]

With the ongoing development of vocational education in our country, the employment quality of vocational college graduates has emerged as a crucial metric for assessing the overall strength and effectiveness of these institutions. Establishing a robust employment quality evaluation system for vocational colleges not only complements and enhances their employment-related efforts, but also represents a critical step toward improving the broader educational and teaching practices within these institutions. Currently, the construction of such evaluation systems tends to focus primarily on professional education and teaching. While this approach has undoubtedly contributed to improving the quality of vocational education, it often overlooks the dimensions of development, innovation, and entrepreneurship education, thereby limiting its potential to produce optimal educational outcomes. In light of this, vocational colleges must proactively design and implement an employment quality evaluation system that incorporates the principles of entrepreneurship and innovation, fostering the healthy and sustainable growth of vocational education. The employment quality assessment of vocational college students, within the framework of innovation and entrepreneurship (often referred to as "double innovation"), constitutes a multiple-attribute decision-making (MADM) problem. To address such complex issues, methods like Logarithmic TODIM (LogTODIM) and TOPSIS have been proposed for MADM scenarios. In this context, Type-2 neutrosophic numbers (T2NNs) are utilized to represent and manage the inherent uncertainty and vagueness in the employment quality evaluation process for vocational college students under the double innovation framework. This study introduces a novel method--the Type-2 neutrosophic number Logarithmic TODIM-TOPSIS (T2NN-LogTODIM-TOPSIS) approaches a solution for multiattribute group decision-making (MAGDM) under T2NN conditions. Finally, a numerical case study is conducted to illustrate the application and effectiveness of the T2NNLogTODIM-TOPSIS approach in evaluating the employment quality of vocational college students within the context of innovation and entrepreneurship. [ABSTRACT FROM AUTHOR]

Abstract Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals’ cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals’ metacognition and recording and utilizing users’ confidence in their answers?

Aim: This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual‐pathway mediating effect of AI self‐efficacy and AI anxiety and organizational ethical climate as the moderating effect. Background: To address the practical application of medical AI technology, alleviate the scarcity of medical resources and fulfil the medical and health demands of the public, it is crucial to improve the innovation behaviour of nurses through the use of medical XAI. Methods: A cross‐sectional survey was conducted involving 368 Chinese nurses working at tertiary and secondary hospitals in Anhui Province, Jiangsu Province, Zhejiang Province and Shanghai. Results: Implementing medical XAI significantly enhanced the innovation behaviour of nurses. Anxiety and self‐efficacy regarding AI mediated the connection between medical XAI and the innovation behaviour of nurses. Furthermore, the organizational ethical climate positively moderated the relationship between medical XAI and AI self‐efficacy. Conclusion: Medical XAI helps to enhance nurses' AI self‐efficacy and reduce AI anxiety, thereby enhancing nurses' innovation behaviour. An organizational ethical climate enhances the positive relationship between medical XAI and AI self‐efficacy. Implications for Nursing Management: Organizations and technology developers must augment the study about XAI and the system design of human‐centred AI technology. The organizations aim to enhance the education and training of nurses in AI, specifically focussing on boosting nurses' self‐efficacy in utilizing AI technology. Moreover, they want to alleviate nurses' fear of new technological advancements. Hospital administrators and leaders develop strategies to address the ethical atmosphere inside their organization. [ABSTRACT FROM AUTHOR]

Voluntary behaviors such as sniffing, moving, and eating require decision-making accompanied by intentional respiration. Based on the study of respirationcoherent activity of rodent olfactory networks, we infer that during the inhalation phase of respiration, olfactory cortical areas process environmental odor information and transmit it to the higher multisensory cognitive areas via feedforward pathways to comprehensively evaluate the surrounding situation. We also infer that during the exhalation phase, the higher multisensory areas generate cognitive-signals and transmit them not only to the behavioral output system but also back to the olfactory cortical areas. We presume that the cortical mechanism couples the intentional respiration with the voluntary behaviors. Thus, in one respiratory cycle, the mammalian brain may transmit and process sensory information to cognize and evaluate the multisensory image of the external world, leading to one behavioral decision and one emotional expression. In this perspective article, we propose that one respiratory cycle provides a minimum time unit for decision making during wakefulness. [ABSTRACT FROM AUTHOR]

Background: Improving the uptake of relevant and reliable research is an important priority in long-term care to achieve sustainable and high-quality services for the increasingly older population. Aim: The purpose was to assess the effectiveness of a tailored, adaptive and a multifaceted KT capacity program, relative to usual practice, on the implementation of National Early Warning Score 2 (NEWS2). Methods: This study was carried out as a pragmatic cluster-randomized controlled trial. The capacity program consisted of an educational part to address implementation capacity gaps and a facilitation-upon-implementation part to address a relevant knowledge gap in nursing homes. A collective decision was made to address the challenge of early detection of clinical deterioration among nursing home residents, by implementing the (NEWS2) as clinical innovation. Public nursing homes in a Norwegian municipality (n = 21) with a total of 1 466 beds were eligible for inclusion. The study-period spanned over a 22-month period, including a 12-month follow-up. Data was extracted from the Electronic Patient Journal system and analyzed using multilevel growth model analysis. Results: The intervention had a large effect on the use of NEWS2 among care staff in intervention nursing homes, compared to the control group (standardized mean difference, d = 2.42). During the final month of the implementation period, residents in the intervention group was assessed with NEWS2 1.44 times (95% CI: 1.23, 1.64) per month, which is almost four times more often than in the control group (mean = 0.38, 95% CI: 0.19, 0.57). During the follow-up period, the effect of the intervention was not only sustained in the intervention group but there was a substantial increase in the use of NEWS2 in both the intervention (mean = 1.75, 95% CI: 1.55, 1.96) and control groups (mean = 1.45, 95% CI: 1.27, 1.65). Conclusions: This tailored implementation strategy had a large effect on the use of NEWS2 among care staff, demonstrating that integrated knowledge translation strategies can be a promising strategy to achieve evidence-based care in the nursing home sector. Trial registration: ISRCTN12437773. Registered 19/3 2020, retrospectively. [ABSTRACT FROM AUTHOR]

Humans tend to give more weight to information confirming their beliefs than to information that disconfirms them. Nevertheless, this apparent irrationality has been shown to improve individual decision-making under uncertainty. However, little is known about this bias' impact on decision-making in a social context. Here, we investigate the conditions under which confirmation bias is beneficial or detrimental to decision-making under social influence. To do so, we develop a Collective Asymmetric Reinforcement Learning (CARL) model in which artificial agents observe others' actions and rewards, and update this information asymmetrically. We use agent-based simulations to study how confirmation bias affects collective performance on a two-armed bandit task, and how resource scarcity, group size and bias strength modulate this effect. We find that a confirmation bias benefits group learning across a wide range of resource-scarcity conditions. Moreover, we discover that, past a critical bias strength, resource abundance favors the emergence of two different performance regimes, one of which is suboptimal. In addition, we find that this regime bifurcation comes with polarization in small groups of agents. Overall, our results suggest the existence of an optimal, moderate level of confirmation bias for decision-making in a social context. Author summary: When we give more weight to information that confirms our existing beliefs, it typically has a negative impact on learning and decision-making. However, our study shows that a moderate confirmation bias can actually improve decision-making when multiple reinforcement learning agents learn together in a social context. This finding has important implications for policymakers who engage in fighting against societal polarization and the spreading of misinformation. It can also inspire the development of artificial, distributed learning algorithms. Based on our research, we recommend not directly targeting confirmation bias but instead focusing on its underlying factors, such as group size, individual incentives, and the interactions between bias and the environment (such as filter bubbles). [ABSTRACT FROM AUTHOR]