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The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that 'Big Data and Analytics' and 'Cloud Computing' are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing 'Responsible consumption and production' and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs. [ABSTRACT FROM AUTHOR]

Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously. [ABSTRACT FROM AUTHOR]

Numerous domains, including all branches of science and technology, database theory, data mining, neural networks, expert systems, cluster analysis, control theory, and image capture, have employed graph theory, which can be used to represent interactions among multiple individuals. An extension of fuzzy sets, specifically intuitionistic fuzzy sets, is the concept of single-valued neutrosophic sets. We extend to graphs the notion of single-valued neutrosophic sets, which is an instance of neutrosophic sets. Neutrosophic fuzzy graphs are generalizations of fuzzy graphs, intuitionistic fuzzy graphs, and interval-valued fuzzy graphs. When in the case of fuzzy or intuitionistic fuzzy graphs relation between atoms in problems is indeterminate, then the neutrosophic fuzzy graph serves as a potent tool for dealing with partial, ambiguous, and inconsistent information in the actual world. In single-valued neutrosophic fuzzy graphs, each vertex is allocated a triplet in which the first element represents membership value, second and third elements as indeterminacy and non-membership value, respectively. In this work, we generalize the conclusions relating crisp graphs and fuzzy graphs by applying graph theory to single-valued neutrosophic sets and exploring a novel type of graph structure known as single-valued neutrosophic graphs. Sombor numbers are an effective and efficient tool in describing the topology of the graph in a numerical value. The main aim of this article is to define the first six Sombor numbers for single-valued neutrosophic fuzzy graphs. Then, degree of vertices of different graph families is determined in single-valued neutrosophic fuzzy framework and calculate the six Sombor numbers for these fundamental graph families for single-valued neutrosophic fuzzy graphs. In the end, an application to show the trade flows between different countries via sea route is presented to prove that single-valued neutrosophic fuzzy graphs are more generalized and efficient. First, we calculated the six Sombor numbers for crisp graph and then in SVNS fuzzy framework. Illustrated cases are then provided to show the applicability, viability, efficacy, and benefits of the suggested ways. [ABSTRACT FROM AUTHOR]

Picture fuzzy sets (PFSs), as a generalization of traditional fuzzy sets and intuitionistic fuzzy sets (IFSs), offer a powerful framework for modeling and dealing with imprecise and uncertain information and have been widely used in various fields. Nevertheless, how to effectively measure the differences or similarities between PFSs is still a challenging and urgent problem. Despite that some distance measures for PFSs have been developed, some of them have counterintuitive and unreasonable results and even partially fail to satisfy the axiomatic definition of distance measures. To handle these limitations, in this paper, we propose two distance measures of PFSs motivated by the merits of Jensen–Shannon divergence. Specifically, the first distance measure of the PFSs considers the positive degree, neutral degree and negative degree. The second distance measure introduces the refusal degree on this basis. Moreover, the proposed distance measures not only satisfy the axiomatic definition but also overcome the counterintuitive results of existing distance measures. Finally, two kinds of applications related to pattern recognition and medical diagnosis are used to verify the performance of the proposed distance measures. [ABSTRACT FROM AUTHOR]

The practical implementation of logistics center 4.0 (LC4.0) is progressively becoming crucial to logistics operations. The appropriate LC4.0 implementation can be controlled by properly analysing certain key success factors (KSFs), a complex Multi-Criteria Decision-Making problem in uncertain environments. Therefore, it is crucial to analyse the KSFs in implementing LC4.0. Previous research works barely investigated the impact on the interdependencies between KSFs of LC4.0. We improvize the Pythagorean fuzzy Decision-Making Trial and Evaluation Laboratory (PF-DEMATEL) method for GDM under uncertain and incomplete information. Our proposed research framework includes the strengths of trapezoidal pythagorean fuzzy numbers for handling fuzziness and D-Numbers for achieving a better and more accurate decision from a heterogeneous group of experts with their linguistic preferences. Moreover, it is also intelligent and adaptable in handling the inherent uncertainty due to subjective and vague information. The proposed D-number-based PF-DEMATEL model is applied to analyse the KSFs in the execution of LC4.0 and accurately represents the total impacts on each other. The validity and efficiency of the model are examined by applying it to the data obtained from the experts, followed by a comparative analysis. The result analysis will be helpful in industrial practice and academic professionals in the field and future recommendations. [ABSTRACT FROM AUTHOR]

Failures in the integration of solar energy into smart grids can have significant implications for energy reliability and environmental sustainability, resulting in a greater dependence on conventional energy sources and increased carbon emissions. These failures can impact system functionality, efficiency, and long-term cost savings. Therefore, failure analysis plays a crucial role in identifying the underlying causes, devising appropriate solutions, and enhancing the performance of solar integration within smart grid systems. The conventional method of failure mode and effects analysis (FMEA) is widely utilized to identify failure modes in various processes. However, the Risk Priority Number (RPN) scoring system employed in FMEA has faced criticism due to its limitations. To overcome this challenge, our hybrid FMEA approach integrates cost and time considerations into the RPN calculation, thereby enhancing the assessment of failure factors. In the second step of our methodology, we utilize the Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (SF-SWARA) technique and expert insights to determine the weightage of the five underlying factors. Lastly, in the third phase, we propose the Spherical Fuzzy Weighted Aggregated Sum Product Assessment (SF-WASPAS) method to prioritize risks based on the outcomes of the previous phases, while taking into account the uncertainty in the determinants and assigning varying weights to them. According to SF-WASPAS, the highest-rated failure is connectivity and cybersecurity, underscoring the critical importance of ensuring secure and reliable connections in solar systems. Additionally, the FMEA results indicate that overheating or fire ranks as the most significant risk, emphasizing the need for effective fire prevention and mitigation strategies. [ABSTRACT FROM AUTHOR]

Nowadays, to achieve sustainable development and avoid devastating impacts on the environment, India is making rapid and broad changes to green energy technology. In order to provide long-term energy security with lower emissions, renewable energy sources are essential. It is well known that renewable energy technologies, or RETs, have the capacity to significantly meet the demand for electricity while lowering pollution levels. The nation has set up an ecologically friendly energy route in recent years. In this paper, we apply multi-criteria decision making (MCDM) models to determine the best renewable energy technology for India. We use the MULTIMOORA model to identify the best technology under the pythagorean neutrosophic fuzzy set (PNFS) and the FUCOM to obtain the criteria weights. In this study, we investigate sources of renewable energy using the newly developed idea of the PNFS and recommend the best renewable energy source for India. [ABSTRACT FROM AUTHOR]

This paper is dedicated to defining and studying for the first time a two-fold algebra over the neutrosophic real number ring by merging the fuzzy set mapping with the algebraic operations of the neutrosophic real number ring. Also, we study the elementary algebraic properties of the defined two-fold algebra through its algebraic operations and substructures such as homomorphisms and ideals. [ABSTRACT FROM AUTHOR]

This paper is dedicated to defining and studying for the first time the concept of fuzzy metric spaces based on two-fold fuzzy algebras, where the elementary properties of this new concept will be studied and presented by many theorems and related examples that explain the validity of our work. Also, many different types of open and closed balls will be discussed, as well as the relationships between these metric substructures. Keywords: fuzzy metric space, two-fold algebra, open ball, closed ball, torus Introduction and basic concepts The applications of neutrosophic sets and fuzzy sets are very wide and open research areas. In the literature, we can find many neutrosophic and fuzzy algebraic structures with deep connection with applied mathematics and number theory [5-11]. The concept of two-fold algebra was presented by Smarandache in [4], where many suggestions for the algebraic structure related to this algebra were defined and presented. This new idea has been used in [1] to study the two-fold algebra based on the standard fuzzy number theoretical system [3]. In [2], Hatip et.al. proposed the two-fold vector space and two-fold algebraic module based on fuzzy mappings, where they have studied the elementary properties of these new generalizations with many interesting examples. This work is motivated by the modern idea of two-fold algebra, and metric spaces, where we can combine those to different structures in one algebraic structure called two-fold. [ABSTRACT FROM AUTHOR]

Given the complexity of today's world, we might need to work with numbers requiring multi-attribute functions, such as those having positive, neutral, and negative membership and those having truth, indeterminacy, and falsity membership. Adding these numbers together to get a single real number is the most important factor. In situations like this, decision-makers have more difficult choices and are unable to apply the single attribute function of the soft set theory. To address this constraint, the hyper-soft set theory with multi-attribute functions is introduced. We combine the notion of neutrosophic hypersoft set with picture fuzzy hypersoft set to form a single theory of neutrosophic picture hypersoft set in this study. We introduce the notions of correlation coefficient and weighted correlation coefficient and demonstrate its fundamental properties for neutrosophic picture hypersoft set. Then, we introduce the notions of a weighted average operator and a weighted geometric operator for neutrosophic picture hypersoft set by using the various aggregation operators with a suitable example. Making decisions based on several factors and choosing the best option is multi-criteria decision-making or MCDM. When ranking and choosing options based on a distance metric, one essential and useful strategy is the Technique of order preference by similarity to an ideal solution (TOPSIS). We demonstrate the accuracy of the fuzzy TOPSIS methodology by extending it to neutrosophic fuzzy TOPSIS and using neutrosophic picture hypersoft set theory to describe the MCDM problem in this study. We provide a generalized neutrosophic TOPSIS approach to demonstrate correlation coefficients and the effectiveness of this approach with an appropriate example. Finally, we offer a comparison to prior studies to demonstrate the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain. [ABSTRACT FROM AUTHOR]

The transportation problem is one of the most significant mathematical programming applications that appears in various real-world decision-making problems. In an actual scenario, the supply, demand, and cost parameters of a transportation problem cannot be exactly quantified due to market instability. To deal with such types of impreciseness, the researchers have widely used fuzzy numbers and their extensions. Pythagorean fuzzy set theory is a prominent tool for handling uncertain and vague information in complex decision-making situations. This paper aims to develop a solution approach to solve the transportation problem with uncertainty in input parameters by incorporating Pythagorean fuzzy numbers. To do so, first, a new score function is proposed to rank Pythagorean fuzzy numbers more efficiently. A comparative study highlights some flaws in existing score functions, which depicts the advantages of the proposed score function over existing ones. Afterward, we solve the Pythagorean fuzzy transportation problem using the proposed score function. The solution technique is demonstrated with the help of some numerical examples. In addition, a comparative study is also included to show the efficacy of the proposed approach over existing ones. [ABSTRACT FROM AUTHOR]

This study analyzed data from the Korea Welfare Panel Study to identify changing patterns in social exclusion among South Korean youth and determine the influencing factors. The study examined 345 youth from 2012 to 2021. A fuzzy set theory and growth mixture model was used to measure social exclusion and identify patterns and the associated factors during this timeframe. The main findings are as follows. First, the social exclusion gap among South Korean youth that emerged a decade ago has continued, with high, middle, and low levels of exclusion identified. Second, the analysis revealed that looking at the demographics, being female, having less education and lower personal and household income increased the likelihood of falling into a relatively higher level of social exclusion. Based on the results, policy recommendations are suggested to mitigate the social exclusion gap among Korean youth. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth exploration of bipolar vague relations (BPVRs) and their fundamental properties. We delve into the theoretical underpinnings of BPVRs and demonstrate their application to real-world problems, highlighting their potential to enhance decision-making processes. A novel approach to composite relations in decisionmaking is introduced, showcasing how the concept of bipolar vague composite relations can be effectively utilized in practical scenarios. To illustrate the practical utility of BPVRs, we provide a comprehensive numerical example that guides students in making informed career choices based on their performance across various skill assessments. This example underscores the dual capability of BPVRs to independently evaluate the intervals of satisfaction and dissatisfaction for each option, offering a balanced perspective that is crucial for informed decision-making. Our findings reveal the significant potential of bipolar vague relations in refining and improving decision-making processes, particularly in contexts characterized by uncertainty and complexity. The application of BPVRs can lead to more accurate, nuanced, and reliable solutions, making them a valuable tool in various domains. [ABSTRACT FROM AUTHOR]

In the era of big data, there exist complex structure between different classes labels. Hierarchical structure, among others, has become a representative one, which is mathematically depicted as a tree-like structure or directed acyclic graph. Most studies in the literature focus on static feature selection in hierarchical information system. In this study, in order to solve the incremental feature selection problem of hierarchical classification in a dynamic environment, we develop two incremental algorithms for this purpose (IHFSGR-1 and IHFSGR-2 for short). As a preliminary step, we propose a new uncertainty measure to quantify the amount of information contained in the hierarchical classification system, and based on this, we develop a non-incremental hierarchical feature selection algorithm. Next, we investigate the updating mechanism of this uncertainty measure upon the arrival of samples, and propose two strategies for adding and deleting features, leading to the development of two incremental algorithms. Finally, we conduct some comparative experiments with several non-incremental algorithms. The experimental results suggest that compared with several non-incremental algorithms, our incremental algorithms can achieve better performance in terms of the classification accuracy and two hierarchical evaluation metrics, and can significantly accelerate the fuzzy rough set-based hierarchical feature selection. [ABSTRACT FROM AUTHOR]

Evaluation and prioritization are indispensable for selecting the appropriate FWTT (food waste treatment technique). Previous research shows that the decision methods are suitable for selecting FWTTs. Nevertheless, current literature on choosing FWTTs seldom considers the impact of experts' bounded rational behavior and interactive criteria, especially with uncertain information. This article proposes a q-ROFS (q-rung orthopair fuzzy set)-generalized TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making method)-based hybrid framework for coping with the selection problem of FWTTs. In the information fusion process, the fuzziness and ambiguity of information are expressed by the q-ROFS. Then, the q-ROFS-CRITIC (criteria importance through inter-criteria correlation) method is introduced to calculate the criteria weights considering the interactive criteria. Next, the q-ROFS-generalized TODIM framework is presented to rank the alternative techniques considering the experts' bounded rational behavior. Finally, a real case of selecting FWTTs with q-ROFSs is performed to examine the applicability and feasibility of this framework. The result shows that the alternative f 2 (anaerobic digestion) has the highest priority for food waste treatment with the largest overall dominance degree value. After that, a sensitivity study of different parameters and comparative analysis with similar selection methods are organized to elaborate on the reasonability of the provided framework. [ABSTRACT FROM AUTHOR]

The role of transportation in international trade cannot be overlooked; hence, the need for regular upgrades of roads and marketplaces. The Karakorum Highway (KKH), a vital part of the China–Pakistan economic corridor that connects China with Arabian waters, has not received significant attention from the National Highway Authority (NHA) due to uncertainties in many regions. To address this issue, the study employs Dombi operations using Polytopic fuzzy sets to explore uncertainty in decision-making. The Dombi t-norm and t-co-norm can capture inconsistencies, making it an effective tool in the decision-making process. The study applies the Polytopic fuzzy Dombi-weighted averaging (PF-DWA) operator, the Polytopic fuzzy Dombi-ordered weighted averaging (PF-DOWA) operator, and the Polytopic fuzzy Dombi hybrid-weighted averaging (PF-DHWA) operator to demonstrate how the model can help the NHA open bidding for interested companies to repair damaged areas, bridges, and side barriers affected by floods. The study reveals that infrastructure is essential for the development of any country, and the most suitable choice for reconstruction can be made using the proposed methods. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets have been widely studied and applied as an important means of dealing with information uncertainty. However, the existing intuitionistic fuzzy sets and their extension methods are limited and single in their fuzzy spatial representation of information. Under this environment, this paper proposes a new generalized fuzzy set, called qth Rung Root Orthopair Fuzzy Sets (q-RROFS). Since the q-RROFS can adjust the range of fuzzy space expression by the parameter q, it is superior to intuitionistic fuzzy sets, SR-fuzzy sets, and CR-fuzzy sets. We give some definitions and properties of q-RROFS and give their proofs. Under the q-RROFS, we give its operations and properties and introduce four new weighted aggregation operators, namely, qth Rung Root Orthopair Fuzzy-weighted average operator (q-RROFWA), qth Rung Root Orthopair Fuzzy-weighted geometric operator (q-RROFWG), qth Rung Root Orthopair Fuzzy-weighted power average operator (q-RROFWPA), and qth Rung Root Orthopair Fuzzy-weighted power geometric operator (q-RROFWPG). We discuss the properties of these operators in detail and follow the proof procedure. Then, we give a Multi-criteria decision-making approach under q-RROFS. Finally, we illustrate the effectiveness and applicability of the proposed methodology through practical application examples and comparisons with other methods. [ABSTRACT FROM AUTHOR]

In this study, an extended Interval-Valued Picture q-rung Orthopair Fuzzy Set (IVPq-ROFS) is developed, which indicates a trio-different combination of multi-granulated fuzzy numbers, and their basic operation and score function are discussed. An IVPq-ROFS was utilized for sustainability-focused transportation-sectorized electric vehicle battery charging technology selection problem among seven attributes and three alternatives. The application parameters are prioritized through innovative Integrated Objective and Subjective Weighted Approach (IOSWA) for attribute importance identification and Measurement of Alternatives and Ranking according to Compromise solution (MARCOS), which comes under Multi-Attribute Decision Making (MADM) model. This research paper elucidates efficiency of the proposed MADM approach under IVPq-ROFS environment and suggesting alternative battery swapping is best option for electric vehicle charging technology for more adaptation in society for future green mobility. The proposed computational decision algorithm results are validated through nine comparative and sensitivity cases, which proves the reliability and higher effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For decision makers in various fields, the best way to deal with complicated decision-making problems quickly and efficiently is to simplify the complicated problems as much as possible. Inspired by this kind of social management demand, this paper proposes a novel attribute reduction method using evidence theory in hesitant fuzzy data sets. The method ensures belief and plausibility sum and considers internal and external significance measures. It extends classical evidence theory to hesitant fuzzy data sets and introduces internal and external belief significance measures and internal and external plausibility significance measures based on belief and plausibility functions. The properties of belief and plausibility reduction in hesitant fuzzy data sets are studied, along with the relationship between internal and external significance measures. The attribute reduction method based on significance measure has significant effects on complex information systems and can improve the execution efficiency of decision-makers. And the impressive application value of these theories is demonstrated. [ABSTRACT FROM AUTHOR]

As a powerful tool for depicting uncertain information, hesitant fuzzy elements (HFEs) have been favored by many experts and scholars. Consequently, the aggregation of HFEs plays an imperative role in both theory and practice. Although there exist many kinds of hesitant fuzzy aggregation operators nowadays, limitations and irrationality still exist because they cannot satisfy some basic properties of a valid aggregation operator, such as idempotency and boundedness. Motivated by this case, this article aims to develop some novel hesitant fuzzy aggregation operators for handling HFEs that can satisfy three basic properties of a reliable aggregation operator. We first define two normalized operations on HFEs that avoid crossover operation. Furthermore, we propose some normalized aggregation operators from the perspective of arithmetic aggregation and geometric aggregation respectively. Additionally, we establish a decision-making method based on the proposed aggregation operators. Finally, the feasibility and reliability of the method is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

Fuzzy ranking plays a vital role in decision-making problems and various fuzzy applications. There are plenty of ranking methods that are used to rank fuzzy numbers. However, they fail to give satisfactory results in certain situations due to the complexity of the problem. In this present study, an attempt has been made to introduce four types of ranking methods in the field of intuitionistic dense fuzzy (IDF) depending on centroid and graded mean ranking. Also, arithmetic operations based on λ 1 , λ 2 -cuts, fuzzy numbers, and extension principles are defined for IDF environment. A model is framed to rank MCDM problems, which are aggregated using a weighted aggregation operator and ordered using the proposed and extended ranking methods. To illustrate the proposed MCDM model under the field of IDF, the problem of robot selection is taken for war fighter robots and exoskeleton robots to help and replace humans in war and help assistive walking patients with spinal cord injuries. The result reveals that the Ripsaw and Rewalk emerge as preferable options for substituting humans in the contexts of war fighters and exoskeleton robots, respectively. To analyze the effectiveness of the ranking results, comparative and sensitivity analyses are examined. Thus, the results provide a satisfactory output. [ABSTRACT FROM AUTHOR]

Zadeh introduced fuzzy sets to study imprecision in real life after which many variants of fuzzy sets have been developed in literature. In this paper, new variants of fuzzy sets named Hybrid fuzzy sets and Hybrid intuitionistic fuzzy sets have been defined based on some real life scenarios where conventional fuzzy sets are not applicable for dealing Multi-Criteria Decision Making (MCDM) problems. Further, total ordering algorithms on those proposed sets based on scoring functions are defined and applied in MCDM problems where decision matrix is incomplete with respect to some criteria. The major drawback of MCDM problems is non-availability / missing of data values for some alternatives with respect to some criteria while collecting data from the experts. To overcome this, researchers infuse the missing data for one expert from the corresponding data collected from the other expert or deleting that criteria to form a single complete MCDM table. But there are situations in which decisions should be taken with single MCDM matrix with missing data points. In this paper, we have proposed a new method in which we find the unknown data from known values / other entries of the table using relative performances of the alternatives with respect to other criteria through which we attain a ranking of the alternatives of MCDM using total ordering defined on the hybrid intuitionistic fuzzy sets. Further, we proposed another method in which we obtain the score of missing values based on the scores defined on the hybrid intuitionistic fuzzy sets through which final decision / ranking is made. Finally, the applicabilities of new types of fuzzy sets and total ordering methods are established in the field of Fuzzy decision making by comparing with existing imputation methods in the literature. [ABSTRACT FROM AUTHOR]

Real-world multi-criteria problems can be solved using multi-criteria decision-making (MCDM) techniques that allow a variety of qualitative and quantitative aspects to be taken into account simultaneously. There have been an increasing number of theories and approaches created for solving distinct MCDM situations with proportionate and conflicting aspects. Judgments about alternatives considered in MCDM methods may change according to the circumstances and conditions that may occur in the future. Many existing decision-making approaches do not take this situation into account, which may cause wrong decisions. Therefore, it is necessary to create adaptable decision models that make use of both current and future information in order to handle a dynamic decision system. The novelty of this study is to develop a novel MCDM method and its intuitionistic fuzzy set extension, Multi-Period Dynamic ASsessment (MP-DAS) and Intuitionistic Fuzzy Multi-Period Dynamic ASsessment (IF MP-DAS), to provide a dynamic decision approach based on present and future information simultaneously. The objective is to present a dynamic-based MCDM method with as many future time points as experts prefer and a stronger multi-measurement system. By concentrating on medium- and long-term judgments that take into account potential variations of the addressed components, the established method enables more informed decisions to be made for complicated problems. In the study, a multi-measurement system is developed by using both Euclidean distance and distance from average solution. The proposed dynamic decision method can be applied by managers and decision-makers as a decision support system. To show the efficiency and applicability of the developed method, a renewable energy source selection problem is handled, where the assessments include possible variations for both developed methods. A comprehensive sensitivity analysis is conducted to confirm the effectiveness and stability of the method for the developed methods. The results of the comparison analysis with distance-based MCDM and intuitionistic fuzzy distance-based MCDM methods showed that the developed method is superior to other MCDM methods. [ABSTRACT FROM AUTHOR]

Derived from an effective strategy - direct and multiple-input multiple-output strategy, a modular neural network based on a bi-level particle swarm optimization algorithm (BLPSO-MNN) is proposed in the present study to improve the accuracy for multi-step time series prediction. While a binary particle swarm optimization algorithm is designed for the external layer to optimize the task division of prediction horizons, a multi-objective particle swarm optimization algorithm is designed for the internal layer to trade off between the prediction accuracy and structural complexity for each subnetwork in modular neural network. Besides, a set of fuzzy If-Then rules is proposed to determine the historical information to be input to subnetworks. Thus, the structure of BLPSO-MNN, including the number of modules as well as the subnetwork structure, is self-determined accordingly. Numerous experiments are conducted for 18-step-ahead time series prediction to evaluate the performance of BLPSO-MNN. Experimental results show that, although the prediction accuracy decreases when the prediction horizon is large, the overall performance of BLPSO-MNN is superior over all comparative models with greater improvement for larger horizons, indicating it is suitable for a long-term prediction. Besides, the set of fuzzy rules balances the prediction accuracy against the structural complexity caused by the subnetwork inputs. [ABSTRACT FROM AUTHOR]

The optimization of the initial design or the development of repair and replacement strategies for pipes within the water distribution network (WDN) during its operational phase relies on the utilization of crisp values. Some input variables such as nodes demand, pipe's roughness coefficient and reservoir water level have the uncertain nature. Alterations in input parameter values during the operational period, attributed to uncertainty, induce shifts in the behavior and performance of the WDN compared to scenarios with crisp input parameters. Recognizing and analyzing these variations are imperative for making informed decisions to address their ramifications and mitigate problems within the water distribution network. This research analyzes the uncertainty surrounding WDN node pressures, post-implementation of optimal instructions for pipe repair and replacement. The input parameters considered for this analysis include the uncertainty associated with the pipe's roughness coefficient and the nodes demand within the network. To achieve this objective, a combined approach utilizing both a simulation model (EPANET) and the fuzzy α-cut methodology is employed. Triangular fuzzy membership functions (MF) are chosen for both the input and output variables in the analysis. The extreme value combinations of the two uncertain input variables, at each level of uncertainty, are formulated into four distinct scenarios, serving as the input fuzzy set for the simulation model. Among the research scenarios, the second scenario, characterized by the combination of the minimum pipe's roughness coefficient and the maximum demand, is designated as the critical scenario. The findings indicate that in the critical scenario, under the highest level of uncertainty, the WDN reliability index diminishes significantly, ranging from approximately 30–40%. This decrease is primarily attributed to the inadequate supply of required pressure to most nodes within the network. At lower levels of uncertainty, the reliability index of the network surpasses 75%, indicating a relatively acceptable performance. Specifically, in the first and fourth scenarios, the network reliability index consistently exceeds 72%. Notably, in the third scenario, the network reliability index remains consistently above 90% across all uncertainty levels. [ABSTRACT FROM AUTHOR]

Chatbots offer customers access to personalised services and reduce costs for organisations. While some customers initially resisted interacting with chatbots, the COVID‐19 outbreak caused them to reconsider. Motivated by this observation, we explore how disruptive situations, such as the COVID‐19 outbreak, stimulate customers' willingness to interact with chatbots. Drawing on the theory of consumption values, we employed interviews to identify emotional, epistemic, functional, and social values that potentially shape willingness to interact with chatbots. Findings point to six values and suggest that disruptive situations stimulate how the values influence WTI with chatbots. Following theoretical insights that values collectively contribute to behaviour, we set up a scenario‐based study and employed a fuzzy set qualitative comparative analysis. We show that customers who experience all values are willing to interact with chatbots, and those who experience none are not, irrespective of disruptive situations. We show that disruptive situations stimulate the willingness to interact with chatbots among customers with configurations of values that would otherwise not have been sufficient. We complement the picture of relevant values for technology interaction by highlighting the epistemic value of curiosity as an important driver of willingness to interact with chatbots. In doing so, we offer a configurational perspective that explains how disruptive situations stimulate technology interaction. [ABSTRACT FROM AUTHOR]

Crowdfunding platforms have emerged as a promising contemporary means for mobilising collective civic actions to address local or social issues, improve community cohesion and develop the public good. This empirical study taps into the understudied civic crowdfunding platforms (CCP) developed to facilitate such actions, proposing, supporting and funding public‐interest projects through crowdsourcing and microfinancing. Previous studies have shown that individuals' characteristics affect their level of civic engagement with social issues. Considering the diversity of contributor motivations, we aim to shed light on the dynamics of emergent subpopulations of citizens who participate in CCPs. To this end, we use a sequential mixed‐methods approach to integrate our fuzzy set Qualitative Comparative Analysis (fsQCA) findings with the results of an in‐depth qualitative study, to gain rich and robust inferences and meta‐inferences. In Study 1 (n = 316), we used fsQCA to explore five distinctive configural profiles that display the heterogeneity of civic backers' motivations, including civic champions, prosocial advocates, normative supporters, reward seekers and regret‐averse contributors. In Study 2, we corroborated and complemented our fsQCA inferences through an extreme‐case study and identified four boundary conditions. Taken together, our inferences and meta‐inferences address the heterogeneity of motivations for participating in CCPs, by understanding and theorising about diverse profiles of citizen backers. Finally, we offer practical implications for successful civic crowdfunding initiatives. [ABSTRACT FROM AUTHOR]

From a resource action perspective, a configuration analysis was performed on 219 cases of new retailers using fuzzy set qualitative comparative analysis (fsQCA). The major study findings are as follows: (1) Digital transformation capacity and resource management behavior are essential conditions for high performance of new retailers. (2) Seven configurations drive high performance of new retailers. (3) Under a specific configuration, there are potential substitution relationships between digital culture and corporate reputation, "digital culture + human resources" and basic resources. Accordingly, new retailers should take measures such as establishing a three‐layer conditional combination chain to promote sustainable development. [ABSTRACT FROM AUTHOR]

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system's procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation. [ABSTRACT FROM AUTHOR]

Internal marketing has been developed in various forms in all types of organizations. Mainly, internal marketing has been studied as a motivational strategy in service companies. Goods manufacturing industries have not benefited much from the use of internal marketing in strengthening employees’ extra-role behaviors. Customer orientation is one of the extra-role behaviors of employees, which in turn is the source of value for internal and external customers. When we follow the internal process segmentation approach, the organization is divided into a set of internal suppliers and internal customers. In this case, it becomes important to study the customer-oriented behavior of employees in the exchanges within the organization. To ensure the implementation of internal marketing in manufacturing organizations, in a way that best meets the needs of these organizations, it was necessary to conduct a study specific to manufacturing industries; a study that included the identification of internal marketing components with a customeroriented approach. The purpose of the current research was to identify and introduce the dimensions of internal marketing with a customer-oriented approach of employees in manufacturing industries. This is sequential mixed research in the inductive paradigm that was conducted in qualitative-quantitative phases. Its purpose is practical and its nature is exploratory. The data collection of the qualitative phase was carried out during semi-structured interviews that continued until theoretical saturation, and finally 15 experts and managers of manufacturing industries and university professors in the field of management participated in it. Qualitative phase analysis was done with them analysis approach using MAXQDA software. In the quantitative part, the fuzzy Delphi questionnaire was used to reach the consensus of the expert group on the dimensions identified in the qualitative phase. The results indicate the extraction of internal marketing dimensions with a customer-oriented approach in comprehensive themes (individual factors of employees and managers, organizational factors, motivational factors) and in 9 organizational themes, 1- Demographic characteristics 2- Organizational citizenship behavior of employees and managers 3- Managers’ support 4- Internal communication 5- participatory management 6- Promotion of customer-oriented culture 7- Employee job security 8- Job satisfaction 9- Appreciation and 44 basic themes. [ABSTRACT FROM AUTHOR]

The risk control model of the power supply chain system is established. A fault information identification method based on fuzzy clustering is proposed. This method fully considers the power grid's characteristics and uses terrible data. A risk assessment model based on fuzzy set theory is established by the COWA operator weight method and grey cluster evaluation method. The security risk identification model of power grid enterprises uses insufficient data. The security risk identification data are normalized and classified. Empirical analysis determines various risk factors that may appear in power projects. The applicability and feasibility of the index system and evaluation model are verified. [ABSTRACT FROM AUTHOR]

This paper aims to determine the resilience scores of suppliers by considering the importance of knowledge sharing and incorporating a set of criteria that affect the supply chain's resilience, along with knowledge sharing. In order to achieve this goal, we propose a new multi-criteria decision-making (MCDM) method named MULTIMABAC to evaluate suppliers' resilience. This method is based on the multi-attributive border approximation area comparison (MABAC) method. The new method consists of criteria weighting, expert weighting, and supplier evaluation. In the criteria weighting section, we extend the BWM (Best–Worst method) using Pythagorean fuzzy sets (PFSs). The weight of experts is determined by incorporating the average concept into the MABAC method. The resilience score of suppliers is calculated using an extended MABAC method with ideal positive and negative solutions. All indices are aggregated using the ordinal priority approach (OPA) to obtain a unique ranking. Additionally, we extend the MULTIMABAC method using PFSs to address uncertainty in supply chain decision-making. Results and sensitivity analysis demonstrate that the proposed method effectively facilitates knowledge sharing in selecting resilient suppliers. Therefore, organizations can use the proposed method to evaluate and choose resilient suppliers. Moreover, the MULTIMABAC method demonstrates robustness to variations in the weights of its constituent parameters. [ABSTRACT FROM AUTHOR]

The Muirhead Mean (MM) tools are more powerful and well-known operators utilized to express interrelationships among any input arguments considering different variables. Multi-attribute decision-making (MADM) technique is used to evaluate a reliable optimal option based on some realistic characteristics or criteria. The aggregation operators (AOs) play a crucial role in the aggregating and decision-making (DM) processes. In this article, we generalize the theory of intuitionistic fuzzy sets (IFSs) with Frank t-norm and t-conorm. Some robust operational laws of Frank t-norms and t-conorms are also expressed. By inspiring the significance and advantages of the MM operators, we derive some robust mathematical approaches, including intuitionistic fuzzy Frank Muirhead mean (IFFMM) and intuitionistic fuzzy Frank weighted Muirhead mean (IFFWMM) operators. By generalizing the concepts of Dual MM (DMM) operators, we establish a list of new methodologies such as intuitionistic fuzzy Frank Dual Muirhead mean (IFFDMM) and intuitionistic fuzzy Frank weighted Dual Muirhead mean (IFFWDMM). Some prominent characteristics and exceptional cases are discussed in detail. Furthermore, an algorithm is established to evaluate a MADM problem based on derived mathematical approaches. To examine the credibility and effectiveness of diagnosed approaches, we illustrate an experimental case study to assess a suitable optimal option from a group of options. To show the intensity and effectiveness of our derived approaches, a brief discussion about a comparative study is also presented, in which we compare the results of existing approaches with diagnosed mathematical approaches. [ABSTRACT FROM AUTHOR]

Divergence measure between intuitionistic fuzzy sets (IFSs) is important due to its wide range of applications in various fields including pattern recognition, image segmentation, decision-making and clustering. This paper introduces the characterization of belief and plausible intuitionistic fuzzy sets (BP-IFSs) to explore novel logarithmic and non-logarithmic divergence measures between two BP-IFSs. These measures are regarded as highly useful approaches to express ambiguous information within the framework of Dempster–Shafer Theory (DST). An axiomatic definition based on proposed divergence measures is also stated within a frame work of newly established theory. Furthermore, the proposed divergence measures are utilized in three different applications: (i) an example related to the recognition of BP-IFS patterns is provided to demonstrate the practicality of the proposed method in pattern recognition. (ii) An example of Hierarchical agglomerative clustering is also provided. (iii) Introduces an innovative Belief and Plausible Combinative Distance-based Assessment (BP-CODAS) method based on proposed measures for resolving Multicriteria Decision Making (MCDM) problems connected to child labor in under developed countries. The examples provided in these different directions are sufficient to demonstrate the effectiveness, applicability and viability of the suggested methods within the framework of generalized DST. [ABSTRACT FROM AUTHOR]

Rough sets (RSs) and fuzzy sets (FSs) are designed to tackle the uncertainty in the data. By taking into account the control or reference parameters, the linear Diophantine fuzzy set (LD-FS) is a novel approach to decision making (DM), broadens the previously dominant theories of the intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), and q-rung orthopair fuzzy set (q-ROFS), and allows for a more flexible representation of uncertain data. A promising avenue for RS theory is to investigate RSs within the context of LD-FS, where LD-FSs are approximated by an intuitionistic fuzzy relation (IFR). The major goal of this article is to create a novel method of roughness for LD-FSs employing an IFR over dual universes. The notions of lower and upper approximations of an LD-FS are established by using an IFR, and some axiomatic systems are carefully investigated in detail. Moreover, a link between LD-FRSs and linear Diophantine fuzzy topology (LDF-topology) has been established. Eventually, based on lower and upper approximations of an LD-FS, several similarity relations are investigated. Meanwhile, we apply the recommended model of LD-FRSs over dual universes for solving the DM problem. Furthermore, a real-life case study is given to demonstrate the practicality and feasibility of our designed approach. Finally, we conduct a detailed comparative analysis with certain existing methods to explore the effectiveness and superiority of the established technique. [ABSTRACT FROM AUTHOR]

Since the concept of energy justice emerged, studies have conceptually, but rarely empirically, explored energy justice. In addition, studies on energy justice have mainly focused on anthropocentric values toward energy justice. This study aims to categorize Organization for Economic Cooperation and Development (OECD) countries' energy justice using synthetic energy justice via two aspects: eco-centrism and anthropocentrism. Each aspect of energy justice is sub-divided into distributive, substantive, and procedural justices. Using a fuzzy set qualitative comparative analysis methodology via a statistical program, we suggest typologies of 35 OECD countries for each aspect. Eight types of energy justice for each aspect are extracted, and four types of synthetic energy justice groups are identified among OECD countries. Austria, Iceland, and Luxembourg show the best performance in synthetic energy justice, but do not reach a complete contestable status from an eco-centric perspective. This study provides practical policy implications for OECD countries to self-evaluate each country's macroscopic policy direction in the energy sector, and to ultimately pursue synthetic energy justice with a balanced perspective for our environment and us. [ABSTRACT FROM AUTHOR]

This paper describes a generalized Diophantine fuzzy sets, which can be seen as a generalization of both Diophantine fuzzy sets and Pythagorean fuzzy sets. We define the basic properties of generalized Diophantine fuzzy set, as well as their relationships and distances. We compare Diophantine fuzzy sets with other Diophantine Pythagorean fuzzy sets to demonstrate their importance in the literature. We introduce new operators including necessity, possibility, accuracy function and score function. Furthermore, we discuss the new distance between normalized Euclidean distance and normalized Hamming distance. For a generalized Diophantine fuzzy relation, image and inverse image functions are defined. Numerous real-world applications can be found for the prevalent ideas of intuitionistic fuzzy sets, Pythagorean fuzzy sets, Diophantine fuzzy sets and q-rung orthopair fuzzy sets. Regretfully, these theories about the membership and non-membership grades have their own limits. We provide a new idea the generalized Diophantine fuzzy set that eliminates these limitations by including reference parameters. Compared to other kinds of fuzzy sets, there are more applications for generalized Diophantine fuzzy sets. We offer practical examples that show how different enhanced distances might be used in everyday situations. Additionally, to demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and used to a numerical example. The outcomes are assessed for various parameter values. Furthermore, a comparative analysis developed to demonstrate the superiority of the suggested technique over current methodologies. [ABSTRACT FROM AUTHOR]

In this paper, first the fuzzy set and i-(m,n)-near ring then fuzzy subgroups, fuzzy (m,n)-subnear ring and finally fuzzy ideals are defined. We state and prove theorems in these cases. Also in the next chapter, normal fuzzy subnear ring expressed and theorems about normal fuzzy subnear rings is expressed and proved. [ABSTRACT FROM AUTHOR]

An AG-group is an algebraic structure that satisfies the left invertive law and includes its left identity and inverse elements. This framework is naturally suited for representing and manipulating various real-life applications in civil engineering, information engineering, artificial intelligence, and more. In this paper, we present the structural application of AG-groups in data encryption and in the rotational transformation of water molecules. Additionally, numerous authors have explored fuzzy set theory over AG-group systems, yielding many valuable results. Spherical fuzzy set theory, an extension of fuzzy set theory, allows for the representation of uncertainty in decision-making, where there may be multiple possible choices or uncertainty about the degree of membership of an element in a set. However, no prior work has combined spherical fuzzy set theory with AG-groups, which motivates our study. In this paper, we apply spherical fuzzy set theory to AG-groups and introduce the concept of spherical fuzzy AG-subgroupoids. We provide the characterization of spherical fuzzy AG-subgroupoids over AG-groupoids and examine their related properties. As an application of spherical fuzzy sets over AG-groups, we develop the concept of normal spherical fuzzy groups over AG-groups and investigate their various properties. [ABSTRACT FROM AUTHOR]

This longitudinal study explored the impact of COVID‐19 on individuals in romantic relationships. The sample spans three waves: first confinement (n = 52), deconfinement (n = 49), and second confinement (n = 26). The study tested sociodemographic factors, psychological adjustment (anxiety, depression, stress, well‐being), COVID‐19 threat perception, dyadic coping, and relationship quality. Results from repeated measures ANOVA and fuzzy set qualitative comparative analysis (fsQCA) models revealed a decline in anxiety, depression, and stress symptoms, coupled with improved relationship quality over time. Well‐being and dyadic coping remained stable, while COVID‐19 threat perception increased. QCA models emphasized the predictive power of initial mental states (anxiety, depression, stress, and well‐being at W1) on subsequent adjustment. Notably, shorter relationship duration, healthcare worker status, and not having children, when combined with prior mental states, explained increased symptoms and diminished well‐being. The study underscores the significance of addressing these factors in individuals navigating romantic relationships during the pandemic. [ABSTRACT FROM AUTHOR]

From the perspective of configuration, this paper takes the region of manufacturing efficiency as the explanatory variable, selects eight antecedent conditions, and applies fuzzy set qualitative comparative analysis (fsQCA) to study the paths and methods of improving manufacturing emission efficiency. The results of the study show that there are two configuration paths of carbon emission efficiency in manufacturing industry, namely, research frontier and technological innovation level and labour force structure, R&D investment, science and technology innovation level, manufacturing output value, and environmental regulation synergistic path. [ABSTRACT FROM AUTHOR]

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term "dynamic" frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques. [ABSTRACT FROM AUTHOR]

This study, grounded in the cognitive evaluation theory of psychology, examines how perceived crowding affects tourists' time displacement behavior in crowded destinations, drawing on data from 494 analyzed questionnaires at Quanzhou West Street. With using regression analysis, it emphasizes that tourists' perceived crowding primarily drives time displacement behavior, which is mediated by heightened negative emotions. Negative emotions emerge as the most significant outcome of perceived crowding. Furthermore, perceived waiting time notably amplifies crowding's impact on control loss or time displacement behavior, surpassing its effect on negative emotions. We infer that the impact of perceived waiting time on crowding may not indefinitely amplify tourists' extremely negative emotions. In addition, employing innovative fsQCA methodology provides a comprehensive insight into crowding's interplay with control loss, negative emotions, and waiting time, offering a fresh perspective beyond regression analysis contained in the SEM model, which will be beneficial to future management of crowded destinations. This highlights the pressing need to address tourists' emotional responses in crowding management, as these significantly shape motivation and expectations in destination settings. Therefore, the destination managers should reduce perceived waiting times, enhance crowding control, and implement crowding management to reduce tourists' negative emotions. [ABSTRACT FROM AUTHOR]

Livestreaming commerce research has grown amazingly and gained tremendous attention; however, a comprehensive model that explains and reveals customers' trustworthiness in livestreaming commerce is still absent and rarely studied. The present study seeks to capture the critical determinants that affect customers' trustworthiness in livestreaming commerce. In addition, this study is among the first to examine customer confidence in livestream commerce by investigating the influence of marketing influencers such as expertise, trustworthiness, and attractiveness and situational factors such as shopping motivation, time pressure, and product involvement on customers' trustworthiness in livestream commerce. The present study targeted Malaysian users of livestream commerce channels, and 562 valid responses were collected for analysis. The partial least squares path modeling (PLS-SEM) revealed influential factors represented by expertise, trustworthiness, and attractiveness, which are positively related to retailers' confidence in platforms that support livestreaming. At the same time, situational factors such as shopping motivation, time pressure, and product involvement represent factors that motivate the trustworthiness of livestream platforms. There is also an influence on the similarity factors between influencers and livestream commerce customers, which are represented by homophily and authenticity. Hence, live streamers provide real-time information and interactions that increase the proximity between streamers and customers. The five solutions captured from the Fuzzy Set Qualitative Comparative Analysis (fsQCA) analysis revolved around the importance of five basic elements, namely time pressure, trustworthiness, attractiveness, authenticity, and trust in streamer, to boost customers' trustworthiness in livestreaming commerce. The results of the present study provide insightful practical and theoretical implications for practitioners and academics that can benefit the livestreaming business and advance the trustworthiness of livestreaming platforms by emphasizing fun and entertaining experiences that arouse the emotions of customers and marketing influencers. [ABSTRACT FROM AUTHOR]

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. Task scheduling is a nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task scheduling decisions. The primary rationale for selecting these uncertainty parameters lies in the challenge of accurately measuring their values, as empirical estimations often diverge from the actual values. The integral-valued Pythagorean fuzzy set (IVPFS) is a promising mathematical framework to deal with parametric uncertainties. The Dyna Q+ algorithm is the updated form of the Dyna Q agent designed specifically for dynamic computing environments by providing bonus rewards to non-exploited states. In this paper, the Dyna Q+ agent is enriched with the IVPFS mathematical framework to make intelligent task scheduling decisions. The performance of the proposed IVPFS Dyna Q+ task scheduler is tested using the CloudSim 3.3 simulator. The execution time is reduced by 90%, the makespan time is also reduced by 90%, the operation cost is below 50%, and the resource utilization rate is improved by 95%, all of these parameters meeting the desired standards or expectations. The results are also further validated using an expected value analysis methodology that confirms the good performance of the task scheduler. A better balance between exploration and exploitation through rigorous action-based learning is achieved by the Dyna Q+ agent. [ABSTRACT FROM AUTHOR]

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

China is at a critical moment of transforming high-speed development to high-quality development, and it is significant to improve the efficiency of green technological innovation. In this paper, under the perspective of two-stage innovation value chain, we construct the evaluation index system of green technology innovation efficiency, adopt the super efficiency SBM model to measure the green technology innovation efficiency of China's high-tech industries, and based on the results obtained, we assume the fuzzy set qualitative comparative analysis method (fs-QCA) based on the group theory to explore the complex causal mechanism and grouping paths of the interaction between enterprises, government and market that affects the green technology innovation efficiency Mechanism and group path. The study results show that (1) enterprise, government, and market are not necessary conditions to influence the efficiency of green technological innovation, and even if a particular party plays a central role, it needs the assistance of other parties. (2) The improvement of green technological innovation efficiency requires the interaction of enterprises, government, and market, and even if any party does not have the core conditions, it can still produce high green technological innovation efficiency. (3) The path of the "innovative compensation" effect is identified, which indicates that enterprises will generate a high level of green innovation efficiency under sufficient investment brought about by the enterprise scale effect and matched with a good level of economic development. (4) The market economy-led pathway suggests that when the market economy is highly developed, firms do not need environmental regulation and government support to generate efficient levels of green technological innovation. [ABSTRACT FROM AUTHOR]