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Abstract: The process of assessing individual authors should rely upon a proper aggregation of reliable and valid papers’ quality metrics. Citations are merely one possible way to measure appreciation of publications. In this study we propose some new, SJR- and SNIP-based indicators, which not only take into account the broadly conceived popularity of a paper (manifested by the number of citations), but also other factors like its potential, or the quality of papers that cite a given publication. We explore the relation and correlation between different metrics and study how they affect the values of a real-valued generalized h-index calculated for 11 prominent scientometricians. We note that the h-index is a very unstable impact function, highly sensitive for applying input elements’ scaling. Our analysis is not only of theoretical significance: data scaling is often performed to normalize citations across disciplines. Uncontrolled application of this operation may lead to unfair and biased (toward some groups) decisions. This puts the validity of authors assessment and ranking using the h-index into question. Obviously, a good impact function to be used in practice should not be as much sensitive to changing input data as the analyzed one. [Copyright &y& Elsevier]

This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and differences among the surveyed works, along with commonly identified directions for future research in the topic. [ABSTRACT FROM AUTHOR]

To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present a new multi-attribute decision-making (MADM) approach for solving the problems under the uncertain and complex environment. The key challenges during any MADM problem are how to quantify the objective uncertainty information in the data and how to aggregate such collective information. To answer this, in this paper, we utilize the concept of the bipolar fuzzy information to mark the information in terms of the positive and negative support. To aggregate this different information, we propose some power aggregation operators based on the Aczel-Alsina operators (AAO). The AAOs are the generalized t-norm based operations with an additional parameter to analyze the influence of the expert preferences. Based on these AAO and bipolar fuzzy information, we stated bipolar fuzzy AA power weighted averaging and geometric operators and investigate their features. Later, based on these operators, we establish a MADM algorithm to solve the decision-making problems. The applicability of the stated algorithm is demonstrated through a case study related to quantum computing. The comparative studies and advantages of the study are also analyzed with the various prevailing theories. [ABSTRACT FROM AUTHOR]

A simple and effective active aggregation-induced emission (AIE) fluorescent sensor, 1-(2-hydroxynaphthylmethylene)−2-(2-hydroxybenzylidene) hydrazine, was synthesized via a simple reaction requiring inexpensive reagents. It displayed swift response, excellent selectivity, and speedy sensitivity toward Zn 2+ based on its notable chelation enhanced fluorescence/AIE characterization. The sensor can detect Zn 2+ by colorimetric and selective turn-on fluorescence responses. Furthermore, the formation of the sensor–Zn 2+ complex with 1:1 stoichiometry was confirmed by mass spectrometry. When the sensor bound with Zn 2+ , fluorescence-enhanced emission was observed via the chelation effect. Fluorescence intensity was considerably enhanced through aggregate formation when excessive Zn 2+ was added. As the response time was ~5 s, the sensor was used to track Zn 2+ at real time. The sensor could detect Zn 2+ as low as 46 nM, and the recovery of urine samples with addition of Zn 2+ ranged from 90.8% to 111.8%. The AIE mechanisms of the sensor and sensor–Zn 2+ complex were explored by scanning electron microscopy, optical fluorescence microscope, and fluorescence reversibility study. In addition, the potential for practical applications was demonstrated through a simple and effective filter-paper-based method by exploiting the AIE mechanism. [ABSTRACT FROM AUTHOR]

p , q -quasirung orthopair fuzzy set (p , q -QOFS) is an extension of the q -rung orthopair fuzzy set (q -ROFS) theory used to represent uncertainty and vagueness in decision-making processes. The paper aims to utilize robust sine-trigonometric operational laws to investigate the group decision-making process within the p , q -QOFSs framework. The p , q -QOFS possess a distinctive characteristic of handling uncertain information by utilizing a wider space for membership representation compared to q -ROFS. Consequently, the current paper has been categorized into three distinct phases. In the initial phase, novel operational laws will be presented for p , q -QOFSs. The fundamental concept behind these newly proposed operations is to integrate the properties of the sine function, which include being periodic and symmetric about the origin, into the decision-making process for objects. Then, based on these laws, several operators for aggregating information will be obtained, along with their necessary properties and relationships. Lastly, an algorithm will be presented for interpreting the problem of multi attribute group decision-making (MAGDM), utilizing the operators, and demonstrating it with an illustrative example. A comprehensive comparative analysis will be conducted with some of the existing methods to uncover their impacts. [ABSTRACT FROM AUTHOR]

• The interval data effectively aggregated by the induced continuous aggregation operator. • The Shanghai composite index can be accurately predicted by the combination prediction model. • Thorough study of effectiveness theory of the combination prediction model has been completed. This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others. [ABSTRACT FROM AUTHOR]

This paper proposes a new linguistic model called Linguistic q-Rung Orthopair fuzzy Z-number (LqROFZN), which combines the advantages of linguistic variables, Z-number and q-Rung Orthopair Fuzzy numbers. It can be used as a powerful tool for uncertain decision-making, which can effectively improve the accuracy and reliability of the decision-making results, and has a notable application prospect for the fields of information decision-making, risk assessment, diagnosis and so on. In this paper, firstly, the definition of LqROFZN and its operational rules are given, a new distance measure and the concept of entropy are given under LqROFZN, and the entropy of LqROFZN can assess the credibility situation of LqROFZN. Next, two aggregation operators under LqROFZN are given, namely the Linguistic q-Rung Orthopair fuzzy Z-number weighted aggregation (LqROFZWA) operator and the Linguistic q-Rung Orthopair fuzzy Z-number weighted Geometric aggregation (LqROFZWGA) operator. Finally, the MCDM method under LqROFZN is given and the credibility of the evaluation results is assessed using the entropy of LqROFZN. In a set of actual airline aircraft selection cases, the feasibility and advantages of the proposed method are verified through comparative analysis with other methods. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

The prevalence of adverse drug reactions (ADRs) associated with medications has emerged as a pressing global concern, impacting patients worldwide. Addressing the mitigation and prevention of ADRs to enhance patient safety stands as a paramount challenge within the healthcare community. This paper introduces a novel approach featuring the complex q-rung picture fuzzy Aczel–Alsina prioritized ordered weighted arithmetic (Cq-RPFAAPOWA) aggregation operator (AO) and the complex q-rung picture fuzzy Aczel–Alsina prioritized ordered weighted geometric (Cq-RPFAAPOWG) AO, each incorporating priority weightings. Furthermore, the paper devises a robust multi-attribute decision-making (MADM) methodology that employs these operators to discern suitable medication alternatives for patients with minimal or no impact from ADRs. In contrast to conventional operators, the Cq-RPFAAPOWA and Cq-RPFAAPOWG AOs effectively curtail potential redundancy in relationship assessments throughout the decision-making process. To exemplify the viability of the proposed MADM method, a comprehensive quantitative model is presented, showcasing its practical applicability. An exhaustive case study in ADR assessment empirically underscores the superiority of this approach over existing strategies. Importantly, the study underscores that drug selection based on the severity of ADRs stands as the most influential factor in elevating the public healthcare system. These findings offer valuable insights into decision-making processes and present actionable recommendations for policymakers, empowering them to make well-informed and impactful enhancements in the selection of pharmacological therapies. [ABSTRACT FROM AUTHOR]

There are two cases of the (conditional) distributivity of semi-t-operators over S -uninorms that have not been discussed by Fang and Hu in 2019. This paper further characterizes the necessary condition for the left (resp. right) distributivity of semi-t-operators over S -uninorms in these two cases. The sufficient conditions for the left (resp. right) distributivity of semi-t-operators over S -uninorms are also discussed. The outcome of one case is different from the conclusion obtained by Fang and Hu, whereby it is impossible to obtain the necessary and sufficient conditions for semi-t-operators distributed over S -uninorms as usual. In particular, this paper points out that the distributivity of semi-t-operators over S -uninorms is equivalent to their conditional distributivity over S -uninorms under the two cases mentioned above. Some examples are also presented to support our conclusions. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

Efficient aggregation methods and order relationships are crucial in solving group decision making (GDM) problems with linguistic interval-valued Atanassov intuitionistic fuzzy information. Based on a novel score function, this paper first presents a new method to rank linguistic interval-valued Atanassov intuitionistic fuzzy numbers (LIVAIFNs). The proposed ranking method overcomes the limitations of existing methods. Then, a new linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (NLIVAIFWA) aggregation operator (AO) is proposed. The NLIVAIFWA AO of LIVAIFNs presented in this paper can overcome the drawbacks of the linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (LIVAIFWA) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy weighted geometric (LIVAIFWG) AO, the linguistic interval-valued Atanassov intuitionistic fuzzy Hamacher weighted averaging (LIVAIFHWA) AO and the improved linguistic interval-valued Atanassov intuitionistic fuzzy weighted averaging (ILIVAIFWA) AO of LIVAIFNs. Furthermore, some primary properties of the NLIVAIFWA AO are discussed, and a novel GDM method based on the provided NLIVAIFWA AO of LIVAIFNs is constructed. Finally, the effectiveness of the proposed GDM method is demonstrated through numerical examples, highlighting its superiority, and advantages. [ABSTRACT FROM AUTHOR]

Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the comfort and well-being of learners.This paper proposes a decision-making model to address the complex task of selecting educational vendors within the Metaverse with a focus on extended reality (XR). Recognizing the challenges inherent in such selections, this work presents a consensus-based measurement of alternatives and ranking according to compromise solution (MARCOS) model, incorporating single-valued neutrosophic (SVN) information and SVN Dombi-Archimedean weighted aggregation operators. Attribute weights crucial for decision-making are determined through an optimization tool employing cross-entropy measures with partial weight information. The evaluation encompasses nine criteria, including cost, security, privacy, pedagogical alignment, configuration, maintenance, level of control, output tools, degree of immersion, availability of XR learning contents, and time. Four leading vendors including Victory XR, ARuVR, zSpace, and Varjo Technologies are assessed, with zSpace emerging as the most favorable alternative with the highest overall utility value of 0.6889. The paper further conducts sensitivity analysis and performance comparisons to establish the robustness of the ranking results, providing a comprehensive methodology for educational stakeholders addressing the evolving dynamics of the Metaverse. • A decision-support model is suggested for ranking XR educational vendors under metaverse. • Some robust Dombi-Archimedean weighted aggregation operators are presented under SVN context. • A consensus based SVN-MARCOS methodology is proposed with partial weights information of attributes. [ABSTRACT FROM AUTHOR]

In multiple attribute group decision-making (MAGDM), a consensus exists that the psychological factors of decision makers (DMs) generally influence their choice of optimal alternatives. To quantify well the influence of DMs' risk attitudes on the group decision-making process, this paper develops a new approach to MAGDM by introducing DMs' utility functions into the aggregation process. First, the ordered weighted utility distance (OWUD) operator is constructed by introducing DMs' utility functions into the distance operator. As a novel operator, the OWUD operator not only satisfies both the basic features of distance measures and the desirable properties of aggregation operators, but also has some unique characteristics. Then, the linear risk tolerance (LRT) utility function is further used in the OWUD operator as the basic utility function, and a specific form of the OWUD operator, which is named as the ordered weighted-linear risk tolerance utility distance (OW-LRTUD) operator, is proposed. The OW-LRTUD operator can quantify well the influence of DMs' risk attitudes on the group decision-making process. It also has an analytically tractable form and includes many well-known distance measures and aggregation operators, and thus can be applied to many different kinds of decision-making problems. Subsequently, to determine the weights of the OW-LRTUD operator, an extension of the maximum Bayesian entropy method is proposed by incorporating the prior known weight information and different risk attitudes of all individual DMs. An approach to MAGDM based on the OW-LRTUD operator and the proposed weight determination method is developed. An application for determining the optimal alternative in the investment selection is provided to show the feasibility, effectiveness and robustness of our approach in practice, and the corresponding sensitive analysis illustrates the influence of DMs' risk attitudes on the group decision-making process. • This paper develops a class of distance operators, named as the ordered weighted utility distance operators, which can quantify the influence of DM's risk attitudes or the satisfaction degrees on the group decision-making process. • New weighting models for both attributes and DMs are provided respectively by combining the prior known weight information and subjective considerations of all individual DMs. • It is shown that the Arrow-Pratt measure of absolute/relative risk aversions of DMs significantly impact the optimal option of alternatives by numerical experiments and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy, loss and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two different problems related to intermittent clients are discussed, together with two approaches to be followed for each of them. Specifically, this type of problems may occur because in a real scenario some clients may leave the training, and others enter it, and on the other hand because of client technical or connectivity problems. Finally, improvements and future work in the field are proposed. [ABSTRACT FROM AUTHOR]

In this paper we extend the notion of stability of each aggregation function as presented in a previous paper of the authors, where an aggregation function was denominated Family of Aggregation Operators (FAO) in order to stress that aggregation operators within an aggregation function should be consistent. We will show that the previous definition presents certain problems when dealing with a FAO that is not idempotent or continuous. Meanwhile the previous definition was based on the study of fixed points, without taking into account the environment of each point, as many applications demand, the new approach we propose now is more flexible, and at the end allows a more appropriate definition of the consistency of a FAO. It will be shown that under certain conditions our new proposal is equivalent to the previous definition of strict stability, but the new definition covers families of aggregation operators that are not strictly stable, but intuitively stable, and vice versa. In short, we will see that our new proposal fits much better the intuition of stability of a FAO. This reformulation of stability is based upon the concept of penalty function as a measure of proximity between a value and an array of values. [ABSTRACT FROM AUTHOR]

In this paper, the study of the functional equation of modularity for 2-uninorms recently published by Fechner, Rak and Zedam is revisited. A result from Corollary 6.3 in the mentioned paper is corrected and it is now given in a correct form. Furthermore, we characterize more general cases of modularity for 2-uninorms. [ABSTRACT FROM AUTHOR]

The harmful waste products, beverages, chemical agents that are discharged or emitted by industrial processes are categorized as industrial waste (IW). IW is one of the most severe problems facing governments around the world due to its environmental effect and severity. So the aim of this paper is to develop a method for selecting industrial waste management technique (IWMT) for industry. Dombi Norms were commonly referred to as Dombi operations, but their applications might be better expressed if they are presented with a new level of flexibility within the general parameter. Dombi operations have not yet been applied in non-linear Diophantine fuzzy sets (N-LDFSs) in a suitable form. Therefore we apply the Dombi operators on complex N-LDFS (CN-LDFS) and develop a generalized area of research in decision making problems (DMPs). The majority of decision-making challenges in the actual world contain a wide range of elements that must be taken into account. Making decision under such circumstances can frequently be challenging. To deal with such complicated issues, we require multi-attribute group decision-making (MAGDM) techniques. The aims to discuss MAGDM issues, this paper will introduce a new COmbinative Distance-based ASsessment (CODAS) methodology. In this paper, we put forward three novel aggregation operators (AOs): complex non-linear Diophantine fuzzy Dombi weighted averaging (CN-LDFDWA), complex non-linear Diophantine fuzzy Dombi order weighted averaging (CN-LDFDOWA), and complex non-linear Diophantine fuzzy Dombi hybrid weighted averaging (CN-LDFDHWA). These operators are used in the operational parameter to help a successful solution of the problem. Due to the growing energy and material shortages, as well as to comply with environmental laws and regulations and to take advantage of disposal economics in the current context, industrial waste (IW) should be taken into consideration as one of the potential resources in the steel industry. Further, the proposed decision support model applied to solve a case study regarding to IWMT for Pakistan steel mill corporation (PSMC) under complex N-LDF Dombi operators. The proposed method is more effective and reliable for decision making (DM) model, as compared to the existing method. • We developed aggregation operators (AOs) for CN-LDF Dombi Norms to solve MAGDM issue. • We developed CN-LDFDWA AOs based on CODAS method with all unknown information. • An example regarding to the selection of best IW technique for PSMC is provided. • Comparison provided the feasibility of our work and results are discussed based on ranking. [ABSTRACT FROM AUTHOR]

An ideal type of OWA aggregation operator is the one constructed on the so-called Olympic weights. The orness of this operator is 1/2. Therefore, sometimes we need a trade-off between the desire of having an OWA aggregation operator with weights as close as possible to the Olympic ones and the desire of having a predefined level of orness. In this paper, we choose these optimal weights by minimizing the Euclidean distance to the Olympic weights vector under the constraint of preserving a given level of orness. First, in the case n = 4 , we propose an iterative algorithm where the optimal solution is given for all possible values of the orness, values that are grouped along a partition of the unit interval. This iterative algorithm will inspire us to deduce the optimal weights in the general case. Consequently, we will obtain the analytical solution of the optimal weights as a function depending on the orness level. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) is a promising paradigm to promote the integration of renewable energy-based microgrid (MG) into the power system. This paper addresses the coordinated operation and energy trading problem for VPP aggregated with multi-stakeholder MGs. A two-stage hierarchical coordinated operation framework for VPP is developed consisting of the day-ahead bidding and intra-day dispatch fully considering the settlement of deregulated electricity market, the profit of the VPP operator and the aggregation interest of MGs. To cope with the intra-day operational uncertainties of individual MGs, receding horizon-based model predictive control (MPC) is adopted and extra power balancing services are provided by the VPP operator. The proposed solution is extensively assessed through simulation experiments in presence of four MGs with different ownerships and penetration levels of RDERs, and the numerical results confirm that the proposed solution outperforms two benchmarks. Further, the comparison with two existing methods demonstrates that the adoption of time-of-use price (ToUP) and extra power balancing services in the proposed solution can benefit both MGs and the VPP operator with enhanced energy management performance. [ABSTRACT FROM AUTHOR]

Game theory is a powerful tool in modeling actual decision-making problems. In many cases, it is difficult for players to give a crisp value of payoff. To conquer this issue, various extensions of fuzzy sets have been introduced to represent the payoffs. Although type-2 intuitionistic fuzzy (T2IF) set (T2IFS) can solve the game problem effectively, sometimes players prefer to give the primary (secondary) membership and non-membership degrees with some approximate ranges rather than crisp values. T2IFS cannot express such uncertain payoffs. Thus, this paper initiates a new concept called type-2 interval-valued intuitionistic fuzzy (T2IVIF) set (T2IVIFS) which has stronger ability than T2IFS and interval-valued intuitionistic fuzzy set (IVIFS) in representing uncertain information. Hamacher operations of T2IVIFSs are given. Hamming and Euclidean distances between T2IVIFSs are defined respectively. Some Hamacher aggregation operators for T2IVIFSs are developed and some desirable properties are analyzed. Afterwards, an effective method is proposed for solving T2IVIF matrix game, which can make game results more accurate and reliable. An example of new energy vehicle industry development is provided to validate the proposed T2IVIF matrix game. Comparative analyses reveal that the matrix game in T2IVIF environment has better flexibility and reliability than that in T2IF and IVIF environments. [ABSTRACT FROM AUTHOR]

T-Spherical fuzzy sets provide a robust framework for handling uncertainty in real-world decision-making. This paper addresses the construction of T-Spherical fuzzy Heronian mean averaging aggregation operators using novel Hamacher operations. The study mathematically verifies the properties of these operators and develops new distance measures such as Hamming, Euclidean and Jaccard for quantifying dissimilarity between T-Spherical fuzzy numbers. Furthermore, the TOPSIS method is implemented using the proposed operators and distance measures to solve a practical decision-making problem in selecting the best transportation firm for medical waste disposal. A sensitivity analysis is made to assesses the efficacy of the proposed operators, while a comparison study with existing methods is exhibited to establish their efficiency. • Novel Hamacher operational laws for T-spherical fuzzy (T-SF) numbers. • New distance measures: Hamming, Euclidean, Jaccard and T-SF Heronian mean averaging operators. • TOPSIS method using proposed operators and distance measures. • Effective handling of complexities and uncertainties. • Example: selecting best transportation firm. [ABSTRACT FROM AUTHOR]

Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known area of study in which aggregation operators, such as ordered weighted averaging (OWA) and induced OWA (IOWA) operators, play an important role in merging information and producing an overall ranking. The distance measures from ideal argument values in aggregation operators have gained attention in recent literature. Distance measures are traditionally used as argument variables, which leads to the depiction that the attribute cannot be aggregated directly. In this paper, a generalized form of distance-induced OWA (DIOWA) operators is proposed with distance measures used as order-inducing variables. A distinctive benefit of DIOWA operators is that they permit us to consider ideal argument values while simultaneously also taking the attribute values as argument variables. Three variants of DIOWA operators are proposed and investigated, namely a) the Hamming distance-induced OWA (HDIOWA) operator, b) the normalized Hamming distance-induced OWA (NHDIOWA) operator, and c) the weighted Hamming distance-induced OWA (WHDIOWA) operator. We highlight their important properties and provide proofs to necessary theorems, and also suggest the determination methods for calculating their associated weights. We discuss further extensions of the proposed DIOWA operators with the help of generalized and quasi-arithmetic means. We discuss the use of our proposed family of operators for two different decision making situations, and demonstrate their validity by an illustrative numerical example. Finally, we apply the proposed operators to a real-life problem of ranking Chinese provinces for their science and technology (S&T) development levels. The proposed operators are shown to be a useful addition to the aggregation toolbox for decision analysts. [ABSTRACT FROM AUTHOR]

The selection of a reliable and competent transportation company is a typical multi-criteria group decision-making (MCGDM) challenge in supply chain management. MCGDM has been widely used for decision support under ambiguity and uncertainty. This paper considers this problem in the setting of interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSS), a novel extension of fuzzy sets that presents an integrated approach to interpreting imperfect and ambiguous data. This study explores the novel Einstein aggregation operators (AOs) for this model, specifically the interval-valued q-rung orthopair fuzzy soft Einstein weighted average (IVq-ROFSEWA) and interval-valued q-rung orthopair fuzzy soft Einstein weighted geometric (IVq-ROFSEWG). These operators can consider large amounts of data that include all connections among parameters. Their fundamental properties (such as idempotency, boundedness, homogeneity, monotonicity, and shift invariance) are presented and proven. With the assistance of the new Einstein AOs, we design a novel MCGDM approach. A case study is presented to choose the most reliable transportation company that endorses the rationality and credibility of the proposed decision-making technique in supply chain management. Hence, this research helps with an innovative decision-support structure for assessing transport corporations. [ABSTRACT FROM AUTHOR]

Selecting sustainable suppliers is crucial to achieving sustainability objectives within the supply chain. However, the inherent complexity and uncertainty associated with evaluating supplier performance necessitate the use of fuzzy decision-making tools. This paper proposes the use of cubic intuitionistic fuzzy sets (CIFS) with four aggregation operators (AOs) based on Schweizer-Sklar (SS) t-norm and t-conorm: cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted average (CIFSSSWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted average (CIFSSSOWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted geometric (CIFSSSWG), and cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted geometric (CIFSSSOWG). The inclusion of pseudocodes increases practical application. An exhaustive sensitivity analysis assesses the influence of different parameters on the effectiveness of these techniques. The advantages of precision, efficiency, and execution time are underscored in the comparative analysis, which offers valuable insights that can inform more informed decision-making regarding the selection of green suppliers. The results demonstrate the efficacy of all the proposed approaches in providing a comprehensive and accurate evaluation of green suppliers. This study helps green supplier selection practices get better and gives practical advice to supply chain professionals who want to make their operations more sustainable. [ABSTRACT FROM AUTHOR]

Recently, Baczyński et al. have introduced two new methods of constructing ordinal sums of fuzzy implications. These methods are based on a construction of ordinal sum of overlap functions and extend some type of algebraic structures. In this paper, we explore the family of ordinal sum implications constructed by one of the method presented in (M. Baczyński, P. Drygaś, A. Król, R. Mesiar (2017), [7]) and examine intersections of this new family of fuzzy implications with (S , N)-, (T , N)-, QL -, D -, R -, Yager's f - and g -implications, respectively. Concretely, this new family of fuzzy implications is shown to be different from the above mentioned implications. Moreover, the necessary and sufficient conditions under which these new ordinal sum implications are distributive over t-norms and t-conorms are given. [ABSTRACT FROM AUTHOR]

In recent years, surprising amounts of news, messages, and reviews of products and services are generated in the online social media. Several efforts are being dedicated to detecting topics, as well as mining opinions in these unstructured texts. There are several approaches that compute opinion polarity, and some of them consider topics for their textual analysis. Nevertheless, discovering topics in opinions continues being challenging; due to opinions are generally short and informally written. Besides, opinions do not have a defined structure in several paragraphs; they are presented most of the time as a composition in a paragraph. In this paper, we propose a method to detect polarity in opinions by topics. Our proposal contributes to the fuzzy polarity calculation of detected topics in Spanish opinions. This method is comprised of three main steps: (1) preprocessing, (2) topic detection and (3) fuzzy polarity detection. It is important to notice the added values of this paper are: (1) the topic detection proposal based on the semantic processing when applying the clustering algorithm on opinion sentences, and (2) the evaluation of different aggregation operators for determining the opinion polarity from a fuzzy logic perspective. Aiming at assessing the quality of the resultant polarity detection by topics, we have conducted two main experiments over the Spanish corpus of opinions about Andalusian Hotels from the TripAdvisor site. The results have shown that our method is able to detect the topics correctly, as well as calculating their opinion polarities. [ABSTRACT FROM AUTHOR]

The decision making trial and evaluation laboratory (DEMATEL) is a pragmatic method used to construct the structural correlation of criteria in a multi-criteria decision making (MCDM) problem. This paper proposes modifications to DEMATEL. Different from the typical DEMATEL which utilizes crisp numbers, this modification introduces interval-valued intuitionistic fuzzy numbers to enhance judgements in group decision-making environment. We use the interval-valued intuitionistic fuzzy weighted averaging operator instead of the typical averaging operator to aggregate decision-makers' preferences. This paper proposes a combination of interval-valued intuitionistic fuzzy numbers, DEMATEL and the Choquet integral to allow a strong interrelationship between the sum of rows and the sum of columns of criteria in structural correlation analysis. The feasibility and applicability of the proposed method is illustrated by a numerical example about sustainable solid waste management. The result indicates that 'collaboration and synergy' is the most influential criterion for sustainable solid waste management. Comparative results are presented to show the validity of the proposed method. [ABSTRACT FROM AUTHOR]

• A novel expertise-based consensus reaching process (CRP) for PHFPRs is proposed. • An E-IOWA operator is introduced for the aggregation of individual PHFPRs. • An expertise-based feedback mechanism is presented. This paper proposes a novel expertise-based consensus reaching process (CRP) for probability-hesitant fuzzy preference relations (PHFPRs). First, to identify each expert's comprehensive expertise level, an expertise identification function is constructed based on consistency, hesitancy, and discrimination indicators of PHFPRs. Then, we introduce an expected value based expertise induced ordered weighted averaging (E-IOWA) operator for the aggregation of individual PHFPRs. Here, experts' weights are objectively and dynamically assigned according to their identified expertise levels at each round. To prompt a consensus, this paper presents an expertise-based feedback mechanism so as to provide highly personalized direction rules for experts' preference adjusting. Finally, a case study of risk assessment in food industry and comparative analysis are conducted to prove the validity of the proposed CRP. [ABSTRACT FROM AUTHOR]

The use of aggregation operators that require ordering of the data brings a problem when the structures to be aggregated are multi-valued, since there may be several admissible orders. To addressing this problem, the concept of admissible permutation was introduced for intervals. In this paper we extend this concept to vector domain. However, the problem of selecting the best possible permutation is still an open problem. In this paper we present a novel concept in order to choose the best admissible permutation for vectors: the degree of totalness. This concept allows us to represent to which degree the admissible permutation reorder given vectors as a chain with respect to the partial order. Finally, from the best admissible permutation we construct the Choquet integral. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a distributed machine learning approach in which clients contribute to learning a global model in a privacy preserved manner. Effective aggregation of client models is essential to create a generalised global model. To what extent a client is generalisable and contributing to this aggregation can be ascertained by analysing inter-client relationships. We use similarity between clients to model such relationships. We explore how similarity knowledge can be inferred from comparing client gradients, instead of inferring similarity on the basis of client data which violates the privacy-preserving constraint in FL. The similarity-guided FedSim algorithm, introduced in this paper, decomposes FL aggregation into local and global steps. Clients with similar gradients are clustered to provide local aggregations, which thereafter can be globally aggregated to ensure better coverage whilst reducing variance. Our comparative study also investigates the applicability of FedSim in both real-world datasets and on synthetic datasets where statistical heterogeneity can be controlled and studied systematically. A comparative study of FedSim with state-of-the-art FL baselines, FedAvg and FedProx, clearly shows significant performance gains. Our findings confirm that by exploiting latent inter-client similarities, FedSim's performance is significantly better and more stable compared to both these baselines. [ABSTRACT FROM AUTHOR]

The use of the Choquet integral in data fusion processes allows for the effective modelling of interactions and dependencies between data features or criteria. Its application requires identification of the defining capacity (also known as fuzzy measure) values. The main limiting factor is the complexity of the underlying parameter learning problem, which grows exponentially in the number of variables. However, in practice we may have expert knowledge regarding which of the subsets of criteria interact with each other, and which groups are independent. In this paper we study hierarchical aggregation processes, architecturally similar to feed-forward neural networks, but which allow for the simplification of the fitting problem both in terms of the number of variables and monotonicity constraints. We note that the Möbius representation lets us identify a number of relationships between the overall fuzzy measure and the data pipeline structure. Included in our findings are simplified fuzzy measures that generalise both k -intolerant and k -interactive capacities. [ABSTRACT FROM AUTHOR]

The (p,q,r)-Spherical fuzzy set is a useful generalization of other fuzzy structures such as fuzzy, intuitionistic fuzzy, Pythagorean fuzzy, picture fuzzy, q-rung orthopair fuzzy, spherical fuzzy, and T-spherical fuzzy sets. In this paper, we define concept of interval-valued (p,q,r)-spherical fuzzy set (IVpqr-SFS) and Set-theoretical and arithmetic operations of them. Two aggregation operators named interval-valued (p,q,r)-spherical fuzzy weighted arithmetic (IVpqr-SFWAA) and interval-valued (p,q,r)-spherical fuzzy weighted geometric (IVpqr-SFWGA) aggregation operators are introduced for IVpqr-SF numbers (IVpqr-SFN) and their properties were examined. The proposed operators are explained with examples. Furthermore, the score and accuracy functions of an IVpqr-SFN are introduced. We also define distance measures between two same types IVpqr-SFSs based on Hamming, Euclidean, and Hausdorff distance measures. Moreover, a multi-criteria group decision-making (MCGDM) method is developed based on the TOPSIS method by using the proposed distance measures and score function, and a numerical example is given to show the process of the proposed MCGDM method. In addition, a multi-criteria decision-making (MCDM) method is developed using the proposed IVpqr-SFWAA, IVpqr-SFWGA operators, and an illustrative example is given to show the process of the proposed MCDM method. Finally, a comparative analysis is presented between the proposed methods and the existing methods. • The concept of IVpqr-SF sets is defined. • Distance measures between two IVpqr-SF sets are defined. • Some arithmetic and geometric aggregation operators for IVpqr-SF numbers are defined. • The TOPSIS method is developed under IVpqr-SF environment. • A MCDM method is developed and applied to an illustrative example. [ABSTRACT FROM AUTHOR]

The contrivance of this paper is two-dimensional. The first is to formulate an extended Dombi-Hamy mean operator in an interval-valued neutrosophic environment for assembling inter-related multiple parameters. The second one is to develop a new ranking procedure named as GLF ranking by introducing the concept of Gaussian likelihood function for solving multi-criteria decision-making problems in an interval-valued neutrosophic circumstance. Also, we have proposed a novel algorithm named Gaussian likelihood function based algorithm or GLF algorithm in solving multi-criteria decision-making problem from the field of medical science to find out the vital cause responsible for the occurrence of cancer in India. Finally, we have verified the flexibility and etiquette of GLF method by comparing it with the existing multi-criteria decision-making methods such as Aiwu's method and Jun Ye's method. • The formulation of the Gaussian likelihood function and its characteristics. • Research into a new GLF ranking system has been done. • The Dombi–Hamy mean operator is an aggregation operator that has been created. • A brand-new GLF strategy has been developed to address practical MCDM issues. [ABSTRACT FROM AUTHOR]