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The United Nations' sustainable development goals have highlighted the significance of improving supply chain sustainability and ensuring the proper distribution of orders. This study proposes a novel framework involving Z-number, game theory, an indifference threshold-based attribute ratio analysis (ITARA), and a combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers' current choices for the sustainability of assessed suppliers and order allocations and enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are used to determine the objective weights of criteria and to rank the evaluated order allocations, respectively. A case study of a China company is then presented to demonstrate the usefulness of the proposed framework and to inform their decision-making process regarding which suppliers the orders should be assigned to. [ABSTRACT FROM AUTHOR]

To model fuzzy numbers with the confidence degree and better account for information uncertainty, Zadeh came up with the notion of Z-numbers, which can effectively combine the objective information of things with subjective human interpretation of perceptive information, thereby improving the human comprehension of natural language. Although many numbers are in fact Z-numbers, their higher computational complexity often prevents their recognition as such. In order to reduce computational complexity, this paper reviews the development and research direction of Z-numbers and deduces the operational rules for symmetric triangular Z-numbers. We first transform them into classical fuzzy numbers. Using linear programming, the extension principle of Zadeh, the convolution formula, and fuzzy number algorithms, we determine the operational rules for the basic operations of symmetric triangular Z-numbers, which are number-multiplication, addition, subtraction, multiplication, power, and division. Our operational rules reduce the complexity of calculation, improve computational efficiency, and effectively reduce the information difference while being applicable to other complex operations. This paper innovatively combines Z-numbers with classical fuzzy numbers in Z-number operations, and as such represents a continuation and innovation of the research on the operational laws of Z-numbers. [ABSTRACT FROM AUTHOR]

In research of a better description for information uncertainty, Z-numbers, which are related to both the objective information and the subjective criticism, were first conceptualized by Zadeh. Because of its neologism, there have been multitudinous attempts toward continuation and expansion of the prototype. In this paper, we mainly study varieties of theoretical preparations for classical Z-numbers and derive the maximum expected linear programming model of Z-numbers, which are constructed on the basis of reliability conversion factors and proliferation on applications due to their simplicity. Firstly, by means of transforming Z-numbers into LR fuzzy intervals through their reliability variable, the credibility distribution and inverse distribution of converted Z-numbers are stated precisely. Then, the operational law of independent variables and its expected value can be derived via credibility distribution. The maximum expected Z-number linear programming model is determined on the basis of previous theoretical preparations, and it transforms from a classical Z-number chance-constrained model into a crisp one. Finally, with the aim of improving the programming method, its application in pragmatic practice with the realistic examples of a supplier section and optimal portfolio problems are enumerated to interpret the effectiveness of our model. [ABSTRACT FROM AUTHOR]

This study models a two-stage agricultural supply chain consisting of a financially constrained manufacturer of agricultural products and a financially stable retailer. We examine two supply chain structures: traditional and dual channel. Our analysis centers on the effects of consumer green preferences, consumer sensitivity to the freshness of agricultural products, and substitution rate between green and non-green agricultural products on the profitability of supply chain members. The purpose of this work is to study the optimal financing strategy for companies producing both regular and organic products simultaneously. We analyze optimal financing strategies for the manufacturer using three models: bank financing, internal debt financing, and internal equity financing. Our results indicate that in both traditional and dual-channel supply chains, increased consumer green preferences or greater sensitivity to the freshness of agricultural products improve supply chain profitability, while a higher substitution rate between green and non-green products reduces profits. Additionally, the manufacturer prefers internal debt financing when consumer environmental awareness is low. Conversely, as consumer environmental awareness increases, internal equity financing becomes more profitable. These findings offer valuable insights for agricultural product manufacturers facing financial constraints, assisting them in selecting the most appropriate financing model. [ABSTRACT FROM AUTHOR]

Portfolio management typically aims to achieve better returns per unit of risk by building efficient portfolios. The Markowitz framework is the classic approach used when decision-makers know the expected returns and covariance matrix of assets. However, the theory does not always apply when the time horizon of investments is short; the realized return and covariance of different assets are usually far from the expected values, and considering additional factors, such as diversification and information ambiguity, can lead to better portfolios. This study proposes models for constructing efficient portfolios using fuzzy parameters like entropy, return, variance, and entropy membership functions in multi-criteria optimization models. Our approach leverages aspects related to multi-criteria optimization and Shannon entropy to deal with diversification, and fuzzy and fuzzy entropy variants provide a better representation of the ambiguity of the information according to the investors' deadline. We compare 418 optimal portfolios for different objectives (return, variance, and entropy), using data from 2003 to 2023 of indexes from the USA, EU, China, and Japan. We use the Sharpe index as a decision variable, in addition to the multi-criteria decision analysis method TOPSIS. Our models provided high-efficiency portfolios, particularly those considering fuzzy entropy membership functions for return and variance. [ABSTRACT FROM AUTHOR]

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

Although information theory resolves the inconsistencies (known in the form of famous enigmas) of the traditional approach of thermostatistics, its place in the corresponding literature is not what it deserves. This article supports the idea that this is mainly due to epistemological rather than scientific reasons: the subjectivity introduced into physics is perceived as a problem. Here is an attempt to expose and clarify where exactly this subjectivity lies: in the representation of reality and in probabilistic inference, two aspects that have been integrated into the practice of science for a long time and which should no longer frighten anyone but have become explicit with information theory. [ABSTRACT FROM AUTHOR]

This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: "How is it done?". The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities. [ABSTRACT FROM AUTHOR]

Online auctions are now widely used, with all the convenience and efficiency brought by internet technology. Despite the advantages over traditional auction methods, some challenges still remain in online auctions. According to the World Business Environment Survey (WBES) conducted by the World Bank, about 60% of companies have admitted to bribery and manipulation of the auction results. In addition, buyers are prone to the winner's curse in an online auction environment. Since the increase in information availability can reduce uncertainty, easy access to relevant auction information is essential for buyers to avoid the winner's curse. In this study, we propose an Online Auction Price Suggestion System (OAPSS) to protect the data from being interfered with by third-party programs based on Intel's Software Guard Extensions (SGX) technology and the characteristics of the blockchain. Our proposed system provides a smart contract by using α-Sutte indicator in the final transaction price prediction as a bidding price recommendation, which helps buyers to reduce the information uncertainty on the value of the product. The amount spent on the smart contract in this study, excluding deployed contracts, plus the rest of the fees is less than US$1. Experimental results of the simulation show that there is a significant difference (p < 0.05) between the recommended price group and the actual price group in the highest bid. Therefore, we may conclude that our proposed bidder's price recommendation function in the smart contract may mitigate the loss of buyers caused by the winner's curse. [ABSTRACT FROM AUTHOR]

Maritime emergency materials distribution is a key aspect of maritime emergency responses. To effectively deal with the challenges brought by the uncertainty of the maritime transport environment, the multi-agent joint decision-making location-routing problem of maritime emergency materials distribution (MEMD-LRP) under an uncertain decision-making environment is studied. First, two robust bi-level optimization models of MEMD-LRP are constructed based on the effect of the uncertainty of the ship's sailing time and demand of emergency materials at the accident point, respectively, on the premise of considering the rescue time window and priority of emergency materials distribution. Secondly, with the help of robust optimization theory and duality theory, the robust optimization models are transformed into robust equivalent models that are easy to solve. Finally, a hybrid algorithm based on the ant colony and tabu search (ACO-TS) algorithm solves multiple sets of numerical cases based on the case design of the Bohai Sea area, and analyzes the influence of uncertain parameters on the decision making of MEMD-LRP. The study of MEMD-LRP under uncertain decision-making environments using bi-level programming and robust optimization methods can help decision makers at different levels of the maritime emergency logistics system formulate emergency material reserve locations and emergency material distribution schemes that can effectively deal with the uncertainty in maritime emergencies. [ABSTRACT FROM AUTHOR]

This paper aims to empirically analyze the herd behavior in the VC market in the context of China, including the existence, causes and consequences of herding among venture capitalists. For our empirical analysis, we first construct a herding measure and confirm the existence of herd behavior in the Chinese VC market. Then, we perform OLS/logit regression to examine the causes and consequences of herding among venture capitalists. Our results suggest that herd behavior in the venture capital market are driven by positive signals of essential information and a higher degree of information uncertainty. However, we find no evidence of the influence of feedback trading signals on herding among venture capitalists. Further analysis suggests that a better external information environment would help weaken the herding among venture capitalists, while their reputation concerns might amplify the herding effect. Finally, we examine the economic consequence of the herding and find that the herd behavior of venture capitalists would have an adverse effect on their exit performance. In addition to the enrichment and development of herding theory, our study also provides an essential theoretical frame and policy implications for the steady growth of the venture capital market in emerging economies. [ABSTRACT FROM AUTHOR]

In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to abstractly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc. [ABSTRACT FROM AUTHOR]

In this paper, under the symmetric entropy and the scale squared error loss functions, we consider the maximum likelihood (ML) estimation and Bayesian estimation of the Shannon entropy and Rényi entropy of the two-parameter inverse Weibull distribution. In the ML estimation, the dichotomy is used to solve the likelihood equation. In addition, the approximation confidence interval is given by the Delta method. Because the form of estimation results is more complex in the Bayesian estimation, the Lindley approximation method is used to achieve the numerical calculation. Finally, Monte Carlo simulations and a real dataset are used to illustrate the results derived. By comparing the mean square error between the estimated value and the real value, it can be found that the performance of ML estimation of Shannon entropy is better than that of Bayesian estimation, and there is no significant difference between the performance of ML estimation of Rényi entropy and that of Bayesian estimation. [ABSTRACT FROM AUTHOR]

Hick's law describes the time that individuals take to make a preference decision when presented with a set of possible choices. Basically speaking, the law states that decision time is a logarithmic function of the number of choices when the choices are equiprobable. However, the evidence examined here suggests that this, and a variant of the law for non-equiprobable choices based on Shannon entropy, are not effective at predicting decision reaction times involving structured sets of alternatives. The purpose of this report is to communicate a theoretical alternative to Hick's law that is derived from a mathematical law of invariance for conceptual behavior at the heart of Generalized Invariance Structure Theory (Vigo, 2013, 2015). We argue that such an alternative accounts more precisely for decision reaction times on structured sets. Furthermore, we argue that Hick's law is a special case of this more general law of choice reaction times for categories with zero degree of invariance. [ABSTRACT FROM AUTHOR]

In this paper, the discrete-time version of a continuous-time model with fundamentalists and momentum traders is presented. Our aim consists of studying the impact of cross-sectional momentum traders on the dynamics of the model. To this end, the continuous-time deterministic skeleton of the benchmark model is transformed using sophisticated discretization techniques. It is worth noting that the model does not always maintain the same characteristics after moving from continuous to discrete time. In spite of this, our discrete-time system preserves the dynamic properties of the continuous-time original model. Moreover, heterogeneity introduces an important non-linearity into the market dynamics, causing our deterministic financial model to generate erratic time series similar to the patterns observed in real markets. In particular, we show that the time series originated by the perturbed deterministic system capture some of the main stylized facts of the U.S. financial market. Converting the benchmark model from continuous time to discrete time allows the use of financial data available in discrete time. [ABSTRACT FROM AUTHOR]

The sustainable Supplier Evaluation and Selection and Order Allocation (SSOA) problem has received significant attention in supply chain management due to its potential to enhance a company's performance, improve customer satisfaction, and reduce costs. In this study, an integrated methodology is proposed to address the SSOA problem. The methodology combines multiple techniques to handle the uncertainties associated with supplier evaluation, including a new ranking method based on the concept of Radius of Gyration (ROG) for interval type-2 fuzzy sets. The methodology also incorporates both subjective weights obtained using the Simple Multi-Attribute Rating Technique (SMART) and expert preferences, and objective weights calculated using the Method based on the Removal Effects of Criteria (MEREC) method to determine the weights of evaluation criteria. Some criteria for sustainable development are used to evaluate supplier performance, resulting in type-2 fuzzy sets, which are evaluated using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The ROG-based ranking method is employed to calculate the relative scores of suppliers. Finally, a multi-objective decision-making (MODM) mathematical model is presented to identify suitable suppliers and allocate their order quantities. The methodology is demonstrated in a sustainable SSOA problem and is shown to be efficient and effective, as the ROG-based ranking method allows for more accurate supplier performance evaluation, and the use of the criteria highlights the importance of sustainability in supplier selection and order allocation. The methodology's practicality is further supported by the analysis conducted in this study, which demonstrates the methodology's ability to handle the uncertainties associated with supplier evaluation and selection. The proposed methodology offers a comprehensive approach to the SSOA problem that can effectively handle the uncertainties in supplier evaluation and selection and promote sustainable practices in supply chain management. [ABSTRACT FROM AUTHOR]

The InterCriteria Analysis (ICrA) is based on the mathematical formalisms of index matrices and intuitionistic fuzzy sets. It has been elaborated to discern possible similarities in the behavior of criteria pairs when multiple objects are considered, allowing also the accounting of information uncertainty. The focus of this study is to validate the applicability of ICrA over a large set of ECG criteria extracted for arrhythmia analysis and to evaluate its ability to support the pre-selection of criteria that could be further involved in decision making procedures. ICrA is applied over 88 ECG criteria (resulting in 3828 criteria pairs) calculated for 8528 ECGs from PhysioNet/CinC Challenge 2017 database. Three criteria pairs show strong positive consonance, another 26—positive consonance, while another 15 are in negative consonance. ICrA also reveals lack of dependencies in 98 criteria pairs. The correspondence between our observations (high degrees of agreement/disagreement and lack of dependencies) and our expectations based on knowledge of the principles involved in the computation of the ECG criteria validates the application of ICrA for reliable evaluation of the relation between different criteria. This potential of ICrA to highlight useful relations between ECG criteria makes it suitable in the ECG pre-processing stage for criteria pre-selection. Thus, optimization of the feature space could be achieved together with minimization of the computations' complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of statistics, information entropy is proposed as an important indicator used to quantify information uncertainty. In this paper, maximum likelihood and Bayesian methods are used to obtain the estimators of the entropy for a two-parameter Burr type XII distribution under progressive type-II censored data. In the part of maximum likelihood estimation, the asymptotic confidence intervals of entropy are calculated. In Bayesian estimation, we consider non-informative and informative priors respectively, and asymmetric and symmetric loss functions are both adopted. Meanwhile, the posterior risk is also calculated to evaluate the performances of the entropy estimators against different loss functions. In a numerical simulation, the Lindley approximation and the Markov chain Monte Carlo method were used to obtain the Bayesian estimates. In turn, the highest posterior density credible intervals of the entropy were derived. Finally, average absolute bias and mean square error were used to evaluate the estimators under different methods, and a real dataset was selected to illustrate the feasibility of the above estimation model. [ABSTRACT FROM AUTHOR]

The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known or difficult to measure. In this paper, we attempt to overcome this problem and apply the theory of statistical causality for labor productivity management. We suggest new technology that provides the inference of causal relations between the special programs implemented in the company's and employee's labor productivity. The novelty of the proposed technology is that it is based on a hybrid object model, combines two models: 1—the structural object model about its functioning and development to provide a causal inference and prediction the effect of explicit factors; 2—the model based on observed data to clarify causality and to test it empirically. The technology provides integration of the theory of causal Bayesian networks, methods of randomized controlled experiments and statistical methods, allows under nonlinearity, dynamism, stochasticity and non-stationarity of the initial data, to evaluate the effect of programs on the labor effeciency. The difference between the proposed technology and others is that it ensures determination the synergistic effect of the action of the cause (program) on the effect—labor productivity in condition of hidden factors. The practical significance of the research is the results of its testing the proposed theoretical provisions, methods and technologies on actual data about food service company. The results obtained could contribute to the labor productivity growth over uncertainty of the external and internal factors and provide the companies sustainable development and its profitability growth. [ABSTRACT FROM AUTHOR]

In this article, we focus on considering different risk factors influencing the cash flows of a group of companies. A methodology is suggested for approximated consideration of both seasonal and random fluctuations in the environment, which have some impact on the overall group activity and may be considered via modification of the risk-adjusted discount rates. The main steps of the suggested methodology are described, and the elements of the risk-adjusted discount rate are presented. Although it is the general convention to use the market rate as the discount rate in most cases, under certain circumstances—i.e., stochastic shocks related to the level of interest rates, shifts, and turnabouts in the social environment, as well as the market transformations due to annual/seasonal epidemics, the use of a risk-adjusted discount rate becomes essential. The influence of the seasonal and random changes in the general environment on the companies' activity through modification of the discount rate is illustrated both numerically and graphically in the article, providing analysis of the impact of exogenous parameters on companies' output, profits, net present value, and discounted payback period for the initial investment. [ABSTRACT FROM AUTHOR]

The inverse Weibull distribution (IWD) can be applied to a various situations, including applications in reliability and medicine. In a reliability and medicine test, it is generally known that the results of test units may not be recorded. Recently, the generalized adaptive progressive hybrid censoring (GAPHC) scheme was introduced. In this paper, therefore, we consider the classical estimators (maximum likelihood estimator (MLE) and maximum product spacings estimator (MPSE)) and Bayes estimators (BayEsts) of the uncertainty measure of the IWD under GAPHC scheme. We derive the BayEsts of the uncertainty measure based on flexible (symmetrical and asymmetrical) priors. Additionally, we derive the Bayes estimators using the Tierney and Kadane approximation (TiKa) and importance sampling methods. In particular, the importance sampling method is used to obtain the credible interval for the uncertainty measure of the IWD under the GAPHC scheme. To compare the proposed estimators (classical and BayEsts), the Monte Carlo simulation method is conducted. Finally, the real dataset based on GAPHC scheme is analyzed. [ABSTRACT FROM AUTHOR]

The quality and experience of healthcare systems affect the economy and prosperity of cities all over the world. Governments of several countries are struggling to improve the efficiency of their healthcare systems and decrease healthcare spending costs. In this paper, we discuss one of the most critical and busy processes among healthcare topics: ambulatory care center (ACC) appointment scheduling systems. We investigate the appointment-scheduling issue using a robust optimization framework to minimize operational costs while ensuring the improvement of healthcare service quality. Our findings and insights provide healthcare practitioners with tools to improve operational efficiency and service quality. [ABSTRACT FROM AUTHOR]

The target of an unmanned aerial vehicle swarm will present near-field characteristics when it is integrated as an array, and the existence of the unmanned aerial vehicle swarm motion error will greatly deteriorate the beam pattern formed by the array. To solve these problems, a near-field array beamforming model with array element position error is constructed, and the Taylor expansion of the phase difference function is used to approximately simplify the model. The improved Newton maximum entropy algorithm is proposed to estimate and compensate for the phase errors. The maximum entropy objective function is established, and the Newton iterative algorithm is used to estimate the phase error iteratively. To select the proper Newton iteration initial value, based on a single reference source signal, the initial value of the phase error is estimated through the phase gradient information of the received array signal. Beamforming is carried out after phase error compensation regarding the array. In order to assess the mismatch of the phase error compensation function based on the proposed method, when the beam is scanning, the effectively compensated spatial area of the array beamforming is divided, which lays a foundation for subsequent spatial region division and unmanned aerial vehicle swarm path planning. The simulation results show that the beam formed by the method proposed in this paper has a lower sidelobe level, and as the signal-to-noise ratio changes, the robustness of the proposed method is better validated. The proposed algorithm can effectively suppress the adverse influence of array element position error on array beamforming, and when the beam is scanning, the effectively compensated area of the phase error compensation function is divided, based on the proposed method. [ABSTRACT FROM AUTHOR]

Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles. [ABSTRACT FROM AUTHOR]

Global economic integration drives the development of dynamic competition. In a dynamic competitive environment, the ever-changing customer demands and technology directly affect the leadership of the core competence of enterprises. Therefore, assessing the performance of enterprises in a timely manner is necessary to adjust business activities and completely adapt to new changes. Enterprise diagnosis is a scientific tool for judging the development status of enterprises, and building a scientific and rational index system is the key to enterprise diagnosis. Considering the large number of enterprise diagnostic indicators and the high similarity among indicators, this study proposes a selection method for enterprise diagnostic indicators based on interval-valued hesitant fuzzy clustering by comparing the existing indicator systems. First, enterprise organizations are considered as the starting point. Through the key analysis of relevant indicators of domestic and foreign enterprise diagnosis, enterprise diagnosis candidate indicators are constructed from three aspects, namely enterprise performance, employee health, and social benefit. In view of the ambiguity and inconsistency of expert judgment, this study proposes an interval-valued hesitant fuzzy set based on the characteristics of hesitant fuzzy sets and interval-valued evaluation. For improving the interval-valued hesitant fuzzy entropy function, an interval-valued hesitant fuzzy similarity measurement formula considering information features is designed to avoid the problem of data length and improve the degree of identification among indicators. Then, the similarity, equivalence, and truncation matrices are constructed, and the interval-valued hesitant fuzzy clustering method is used to eliminate redundant indicators with repeated information. The availability of the proposed method is illustrated via an example, and the key indicators in the enterprise diagnostic index system are found. Finally, the advantages of the proposed method are discussed using comparative analysis with existing methods. A rational and comprehensive enterprise diagnostic index system was constructed. The system can be used as a scientific basis for diagnosing the development of enterprises and providing an objective and effective reference. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LS-GDM) problems are common in the daily life of human beings. Both information fusion and computing with words (CWW) technologies in LS-GDM suffer from challenges. In the current research, a proportional hesitant fuzzy linguistic term set (PHFLTS) will be applied to capture the preferences of sub-groups in LS-GDM, which decreases the information lost in information fusion processes. Novel fuzzy semantic representation models of PHFLTS, such as type-1 fuzzy envelope and interval type-2 fuzzy envelope, are respectively studied. The application of the proposed fuzzy entropies facilitates the CWW process with the PHFLTS under the framework of a fuzzy linguistic approach. In particular, linguistic uncertainties contained in the PHFLTS can be reflected in a comprehensive way when the type-2 fuzzy envelope is applied, which contributes to the decrease in the information lost during the CWW process. A novel LS-GDM method cooperating with the fuzzy semantic models of PHFLTS is proposed, in which weights for the sub-groups are determined by size, cohesion, and degree of reliability among the sub-groups. Finally, the proposed decision method as well as CWW tools are applied to the process of urban renewal plan selection. [ABSTRACT FROM AUTHOR]

Enterprises in low-carbon supply chains have been exploring blockchain technology in order to make carbon data transparent. However, there is still some opaque information in the market, such as the value-added service efficiency. How do supply chain members make decisions between information sharing and blockchain adoption? This study considers blockchain adoption and information sharing in a low-carbon supply chain with a single manufacturer and a single retailer. The retailer has private information about value-added services and decides how to share it with the manufacturer. We examine six combined strategies comprised of blockchain scenarios and information sharing formats (no sharing, voluntary sharing, and mandatory sharing). The results indicate that supply chain members prefer blockchain technology under no sharing and voluntary sharing. Under mandatory sharing, supply chain members have incentives to participate in blockchain when the value-added service efficiency exceeds a threshold value. While the manufacturer prefers to obtain the value-added service information, the retailer decides to share information depending on the value-added service efficiency. Besides, supply chain members' attitude toward the sharing contract also depends on the value-added service efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we provide a new bivariate distribution obtained from a Kibble-type bivariate gamma distribution. The stochastic representation was obtained by the sum of a Kibble-type bivariate random vector and a bivariate random vector builded by two independent gamma random variables. In addition, the resulting bivariate density considers an infinite series of products of two confluent hypergeometric functions. In particular, we derive the probability and cumulative distribution functions, the moment generation and characteristic functions, the Hazard, Bonferroni and Lorenz functions, and an approximation for the differential entropy and mutual information index. Numerical examples showed the behavior of exact and approximated expressions. [ABSTRACT FROM AUTHOR]

Online portfolio selection (OLPS) is a procedure for allocating portfolio assets using only past information to maximize an expected return. There have been successful mean reversion strategies that have achieved large excess returns on the traditional OLPS benchmark datasets. We propose a genetic mean reversion strategy that evolves a population of portfolio vectors using a hybrid genetic algorithm. Each vector represents the proportion of the portfolio assets, and our strategy chooses the best vector in terms of the expected returns on every trading day. To test our strategy, we used the price information of the S&P 500 constituents from 2000 to 2017 and compared various strategies for online portfolio selection. Our hybrid genetic framework successfully evolved the portfolio vectors; therefore, our strategy outperformed the other strategies when explicit or implicit transaction costs were incurred. [ABSTRACT FROM AUTHOR]

In this paper, a new strategic analysis method is introduced, called the ten-element analysis (TEA) method to determine the firm's strategic position in the market. The new method is grounded on the computation of the reflections of the external factors on the firm's internal factors through the changes of the values of the internal factors throughout the time when a lack of complete information regarding the environmental factors exists. The TEA method takes ten effective key elements of the firm into account and investigates their changes through a maximum of nine periods and a minimum of two periods. To conduct the model, the paper is mainly focused on four main rubrics, including the detection of the reflection of the firm's environmental factors on the internal factors, deriving the strategic position of the firm from the reflections, the capability of the existing strategic models in determining the strategic position from the reflections in presence of uncertainty and incomplete information of the external factors. The method is applied to a dairy company in order to find its strategic position in the market. The results showed that the output of the TEA method and SWOT analysis is similar which makes the new method reliable to employ. The TEA method is developed under the grey environment to harness the uncertainty where a new grey comparison method is introduced to compare the grey numbers. [ABSTRACT FROM AUTHOR]

The quantum model has been considered to be advantageous over the Markov model in explaining irrational behaviors (e.g., the disjunction effect) during decision making. Here, we reviewed and re-examined the ability of the quantum belief–action entanglement (BAE) model and the Markov belief–action (BA) model in explaining the disjunction effect considering a more realistic setting. The results indicate that neither of the two models can truly represent the underlying cognitive mechanism. Thus, we proposed a more realistic Markov model to explain the disjunction effect in the prisoner's dilemma game. In this model, the probability transition pattern of a decision maker (DM) is dependent on the information about the opponent's action, Also, the relationship between the cognitive components in the evolution dynamics is moderated by the DM's degree of subjective uncertainty (DSN). The results show that the disjunction effect can be well predicted by a more realistic Markov model. Model comparison suggests the superiority of the proposed Markov model over the quantum BAE model in terms of absolute model performance, relative model performance, and model flexibility. Therefore, we suggest that the key to successfully explaining the disjunction effect is to consider the underlying cognitive mechanism properly. [ABSTRACT FROM AUTHOR]

The intuitionistic fuzzy entropy has been widely used in measuring the uncertainty of intuitionistic fuzzy sets. In view of some counterintuitive phenomena of the existing intuitionistic fuzzy entropies, this article proposes an improved intuitionistic fuzzy entropy based on the cotangent function, which not only considers the deviation between membership and non-membership, but also expresses the hesitancy degree of decision makers. The analyses and comparison of the data show that the improved entropy is reasonable. Then, a new IF similarity measure whose value is an IF number is proposed. The intuitionistic fuzzy entropy and similarity measure are applied to the study of the expert weight in group decision making. Based on the research of the existing expert clustering and weighting methods, we summarize an intelligent expert combination weighting scheme. Through the new intuitionistic fuzzy similarity, the decision matrix is transformed into a similarity matrix, and through the analysis of threshold change rate and the design of risk parameters, reasonable expert clustering results are obtained. On this basis, each category is weighted; the experts in the category are weighted by entropy weight theory, and the total weight of experts is determined by synthesizing the two weights. This scheme provides a new method in determining the weight of experts objectively and reasonably. Finally, the method is applied to the evaluation of railway reconstruction scheme, and an example shows the feasibility of the method. [ABSTRACT FROM AUTHOR]

The scientific and reasonable evaluation of the carrying capacity of water resources is of guiding significance for solving the issues of water resource shortages and pollution control. It is also an important method for realizing the sustainable development of water resources. Aiming at an evaluation of the carrying capacity of water resources, an evaluation model based on the cloud model theory and evidential reasoning approach is studied. First, based on the existing indicators, a water resources evaluation index system based on the pressure-state-response (PSR) model is constructed, and a classification method of carrying capacity grade is designed. The cloud model theory is used to realize the transformation between the measured value of indicators and the degree of correlation. Second, to obtain the weight of the evaluation index, the weight method of the index weights model based on the entropy weight method and evidential reasoning approach is proposed. Then, the reliability distribution function of the evaluation index and the graded probability distribution of the carrying capacity of water resources are obtained by an evidential reasoning approach. Finally, the evaluation method of the carrying capacity of water resources is constructed, and specific steps are provided. The proposed method is applied to the evaluation of water resources carrying capacity for Hunan Province, which verifies the feasibility and effectiveness of the method proposed in the present study. This paper applies this method of the evaluation of the water resources carrying capacity of Hunan Province from 2010 to 2019. It is concluded that the water resources carrying capacity of Hunan Province belongs to III~V, which is between the critical state and the strong carrying capacity state. The carrying capacity of the province's water resources is basically on the rise. This shows that the carrying capacity of water resources in Hunan Province is in good condition, and corresponding protective measures should be taken to continue the current state. [ABSTRACT FROM AUTHOR]

After the outbreak of the COVID-19, offline consumption has been significantly impacted. For the sake of safety, online consumption has become the most common manner, and this has generated e-commerce, which not only breaks the spatio-temporal or regional restrictions, but also conforms to the normal economic development needs for epidemic prevention and control. However, this new business model causes problems such as the shortage of post-sales service, false publicity, and uneven quality of live streaming anchors, seriously affecting the interests of consumers. Therefore, it is urgent to strengthen the management of the chaos of live streaming. This study focuses on exploring the problems and the behavioral strategies of stakeholders in the governance process. The paper obtained online user comments by python, and used topic clustering and subject extraction methods to dig out the problems and related multiple subjects in live streaming at first. Secondly, the theory of social preference was introduced to construct an evolutionary game model among multiple subjects, and how to guide the behavioral decision-making of multiple subjects to standardize and rationalize was studied, so as to control the problem of live streaming. Finally, simulation experiments were conducted and the results demonstrated that: (1) Compared with strengthening the reciprocal preference of the government, live streaming platforms, and consumers, changing the individual's altruistic preference is more effective in controlling the chaos of live streaming; (2) weakening the platform's altruistic preference for anchors is conducive to creating a good live streaming environment; and (3) changing consumers' altruistic preference or reciprocal preference is less effective in promoting the governance of the live streaming environment. [ABSTRACT FROM AUTHOR]

With the development of the social economy and an enlarged volume of information, the application of multiple-criteria decision making (MCDM) has become increasingly wide and deep. As a brilliant MCDM technique, the best–worst method (BWM) has attracted many scholars' attention because it can determine the weights of criteria with less comparison time and higher consistency between judgments than analytic hierarchy process. However, the effectiveness of the BWM is based on complete comparison information among criteria. Considering the fact that the decision makers may have limited time and energy to study all criteria, they cannot construct a complete comparison system. In this paper, we propose a novel MCDM method named BW-MaxEnt that combines BWM and the maximum entropy method (MaxEnt) to identify the weights of unfamiliar criteria with incomplete decision information. The model can be translated into a convex optimization problem that can be solved effectively and has an overall optimal solution. Finally, a practical application concerning the procurement of GPU workstations illustrates the feasibility of the proposed BW-MaxEnt method. [ABSTRACT FROM AUTHOR]

The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the open world assumption has been discovered as a new type of uncertain information. The classical Dempster's combination rules are difficult to solve the related problems of incomplete information under the open world assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the open world assumption. Therefore, this paper proposes a new method framework to process uncertain information and fuse incomplete data. This method is based on an extension to the Deng entropy in the open world assumption, negation of basic probability assignment (BPA), and the generalized combination rule. The proposed method can solve the problem of incomplete information under the open world assumption, and obtain more uncertain information through the negative processing of BPA, which improves the accuracy of the results. The results of applying this method to fault diagnosis of electronic rotor examples show that, compared with the other uncertain information processing and fusion methods, the proposed method has wider adaptability and higher accuracy, and is more conducive to practical engineering applications. [ABSTRACT FROM AUTHOR]

The 2014–2016 West African outbreak of Ebola Virus Disease (EVD) was the largest and most deadly to date. Contact tracing, following up those who may have been infected through contact with an infected individual to prevent secondary spread, plays a vital role in controlling such outbreaks. Our aim in this work was to mechanistically represent the contact tracing process to illustrate potential areas of improvement in managing contact tracing efforts. We also explored the role contact tracing played in eventually ending the outbreak. We present a system of ordinary differential equations to model contact tracing in Sierra Leonne during the outbreak. Using data on cumulative cases and deaths, we estimate most of the parameters in our model. We include the novel features of counting the total number of people being traced and tying this directly to the number of tracers doing this work. Our work highlights the importance of incorporating changing behavior into one's model as needed when indicated by the data and reported trends. Our results show that a larger contact tracing program would have reduced the death toll of the outbreak. Counting the total number of people being traced and including changes in behavior in our model led to better understanding of disease management. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG analysis and characterization implemented in a computer-aided decision-support system that can be used to assist medical experts in interpreting EEG patterns. The computerized method utilizes EEG spectral non-stationarity, which is clearly revealed in the time–frequency distributions (TFDs) of multicomponent signals. The proposed algorithm, which is based on the modification of the Rényi entropy, called local or short-term Rényi entropy (STRE), was upgraded with a blind component separation procedure and instantaneous frequency (IF) estimation. The method was applied to EEGs of both forward and backward movements of the left and right hands, as well as to EEGs of imagined hand movements, which were captured by a 19-channel EEG recording system. The obtained results show that in a given virtual instrument, the proposed methods efficiently distinguish between real and imagined limb movements by considering their signatures in terms of the dominant EEG component's IFs at the specified subset of EEG channels (namely, F3, F4, F7, F8, T3, and T4). Furthermore, computing the number of EEG signal components, their extraction, and IF estimation provide important information that shows potential to enhance existing clinical diagnostic techniques for detecting the intensity, location, and type of brain function abnormalities in patients with neurological motor control disorders. [ABSTRACT FROM AUTHOR]

The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time. In order to solve this problem, the base belief function is proposed to modify the basic probability assignment (BPA) in the exhaustive frame of discernment (FOD). However, in the non-exhaustive FOD, the mass function value of the empty set is nonzero, which makes the base belief function no longer applicable. In this paper, considering the influence of the size of the FOD and the mass function value of the empty set, a new belief function named the extended base belief function (EBBF) is proposed. This method can modify the BPA in the non-exhaustive FOD and obtain intuitive fusion results by taking into account the characteristics of the non-exhaustive FOD. In addition, the EBBF can degenerate into the base belief function in the exhaustive FOD. At the same time, by calculating the belief entropy of the modified BPA, we find that the value of belief entropy is higher than before. Belief entropy is used to measure the uncertainty of information, which can show the conflict more intuitively. The increase of the value of entropy belief is the consequence of conflict. This paper also designs an improved conflict data management method based on the EBBF to verify the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study examined the relationship between supply integration and relationship stability and the relationship between relationship stability and performance; furthermore, the moderation effect of environmental uncertainty on supply chain integration and relationship stability was analyzed. The subjects are typical small and medium-sized enterprises (SMEs) in developing countries that focus on niche markets to compete with large-scale manufacturers. Questionnaires were distributed to manufacturers to collect empirical data; in total, 566 valid samples were gathered. The results indicate that supply chain integration has positive effects on relational stability and that relational stability has positive effects on supply chain performance. Relational stability is a mediator between supply chain integration and supply chain performance. The contingency effects of environmental uncertainty on the relationships between internal integration and relational stability were determined in this research. This research framework extended past research on supply chain management; part of the research explored the relationship between supply chain integration and different measures of supply chain performance, as well as whether uncertainty affects supply chain integration and supply chain performance. [ABSTRACT FROM AUTHOR]

(1) Presented models are proposed for analyzing the resilience of an economic system in a framework of a 4 × 6 matrix, the core of which is a balanced scorecard (BSC). Matrix rows present strategic perspectives, matrix columns present strategic maps. (2) Resilience assessment models are based on fuzzy logic and soft computing, combined with systemic-cybernetic approaches to building presented models. The simplest models are Zadeh linguistic variables that describe key performance indicators (KPIs). The BSC model is an acyclic graph with fuzzy links that are calibrated based on special rules. The information obtained during the simulation is aggregated through a matrix aggregate calculator (MAC). (3) The BSC model was used to assess the economic resilience of a small electrical enterprise in Russia, numbering 2000 people with revenue of approximately 100 million euros per year. The BSC model included about 70 KPIs and 200 fuzzy links. Also, the presented MAC model was applied to obtain linguistic classifiers in five basic industries, using the example of a comparative analysis of 82 international industrial companies. (4) The proposed models allow not only to describe the economic system and its external environment, but also solutions aimed at increasing resilience, within the unified framework. [ABSTRACT FROM AUTHOR]

A multi-criteria decision-making (MCDM) method with single-valued neutrosophic information is developed based on the Partitioned Heronian Mean (PHM) operator and the Shapley fuzzy measure, which recognizes correlation among the selection criteria. Motivated by the PHM operator and Shapley fuzzy measure, two new aggregation operators, namely the single-valued neutrosophic PHM operator and the weighted single-valued neutrosophic Shapley PHM operator, are defined, and their corresponding properties and some special cases are investigated. An MCDM model is applied to solve the single-valued neutrosophic problem where weight information is not completely known. An example is provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

On the basis of decision-theoretical rough sets (DTRSs), the three-way decisions give new model of decision approach for deal with the problem of decision. This proposed model of decision method is based on the loss function of DTRSs. First, the concept of fractional orthotriple fuzzy β -covering (FOF β -covering) and fractional orthotriple fuzzy β -neighborhood (FOF β -neighborhood) was introduced. We combined loss feature of DTRSs with covering-based fractional orthotriple fuzzy rough sets (CFOFSs) under the fractional orthotriple fuzzy condition. Secondly, we proposed a new FOF-covering decision-theoretical rough sets model (FOFCDTRSs) and developed related properties. Then, based on the grade of positive, neutral and negative membership of fractional orthotriple fuzzy numbers (FOFNs), five methods are established for addressing the expected loss expressed in the form of FOFNs and the corresponding three-way decisions are also derived. Based on this, we presented a FOFCDTRS-based algorithm for multi-criteria decision making (MCDM). Then, an example verifies the feasibility of the five methods for solving the MCDM problem. Finally, by comparing the results of the decisions of five methods with different loss functions. [ABSTRACT FROM AUTHOR]

This paper provides a theoretical framework for studying the impact of self-interested managers on the level of corporate investment. I extend the standard neoclassical model of firm value maximization to incorporate the effect of misaligned managers on corporate investment via a firm's profit, adjustment costs of capital and shadow cost of external finance. Under some assumptions, commonly made by the relevant literature, the model shows that the intensity of agency conflicts between misaligned managers and outside shareholders affects a firm's investment decisions generating either under or overinvestment with respect to a perfect capital market and driving a higher cost of external finance. [ABSTRACT FROM AUTHOR]

Nowadays, along with increasing companies' activities, one of the main environmental protective tools is green supply chain management (GSCM). Since fast-moving consumer goods (FMCG) companies are manufacturing materials that usually require special warehousing as well as different distribution systems, and since companies of food products tend to fall into this area, the safety of their manufactured materials is a vital global challenge. For this reason, organizations in addition to governments have realized the importance of the green supply chain in these industries. Therefore, the present study examines the key performance indicators (KPIs) of the green supply chain in the FMCG industry. There are several performance indicators for the green supply chain. In this study, the KPIs were extracted based on the literatures as well as the opinions of experts through which key indicators in FMCG industries were identified. Using the fuzzy decision -making trial and evaluation laboratory (DEMATEL) method, the relationships and interactions of these key indices were determined. Moreover, a fuzzy nonlinear mathematical modeling was used to investigate the significance of these indicators. It is revealed that the organizational environmental management factor has the highest priority. [ABSTRACT FROM AUTHOR]