Uncertainty Theory Research Articles

An uncertainty theory based tri-objective behavioral portfolio selection model with loss aversion and reference level using a modified evolutionary root system growth algorithm

The outbreak of uncertain events, e.g., financial crisis, regional conflict and abrupt contagion, has a significant impact on residents’ income. Hence, the wealth management and portfolio selection become more and more important. In addition, the behavioral finance believes that decision-making process of the investors not only depend on utility maximization, but also on who to compare with. It differs from the traditional finance based on rational cognitive acquiring and decision-making of agents, in this paper, we consider both investment uncertainty and investment utility with reference level for portfolio selection. First, we introduce the uncertainty theory and loss aversion utility with reference level based on behavioral finance. Then we study an uncertain multi-period portfolio selection problem. Moreover, we formulate an uncertain behavioral portfolio selection model with three objective functions for which we propose an equivalent program consisting of a main model and two auxiliary models for tri-objective optimization model. Thereafter, we use a modified evolutionary root system growth algorithm to solve the transformed models. Finally, a numerical simulation allows to show the practicability of proposed models and validity of modified algorithm.

Uncertain least square support vector regression with imprecise observations

In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge.

Integrative Stress Perception Interventions for Leukemia Patients: A Multidisciplinary Approach

BACKGROUND: Leukemia patients often face significant psychological stress due to the uncertainty of their disease’s outcome, the side effects of treatment, and the profound changes to their lives. Effective stress management interventions are crucial for improving their quality of life and psychological well-being. OBJECTIVE: This article aims to explore and evaluate the theoretical frameworks and practical interventions for managing stress perception in leukemia patients, providing an integrated view of the current methodologies and their outcomes. METHODS: We review and synthesize the literature on stress perception interventions within leukemia care, focusing on mindfulness theory, cognitive behavioral theory, and the theory of uncertainty in illness. We assess the efficacy of various interventions, including mindfulness-based stress reduction, cognitive behavioral therapy, stress inoculation training, and graded interventions, alongside the utilization of measurement tools such as the Perceived Stress Scale (PSS) and the Perceived Stress Questionnaire (PSQ). RESULTS: The reviewed interventions demonstrate significant potential in reducing stress perception among leukemia patients. Mindfulness-based approaches help patients focus on the present and reduce rumination, cognitive behavioral interventions address negative thought patterns contributing to stress, and graded interventions tailor care to individual patient needs. Furthermore, the application of stress measurement tools provides a quantitative basis for evaluating intervention efficacy. CONCLUSION: Integrative stress management interventions, grounded in comprehensive theoretical frameworks, offer significant benefits for leukemia patients, contributing to lower stress levels and improved overall well-being. Healthcare providers are encouraged to incorporate these interventions into standard leukemia care, ensuring a holistic approach to patient treatment. Future research should aim to innovate and refine these interventions, ensuring they are accessible, feasible, and effective for the diverse leukemia patient population.

Analysis of Resource Allocation Problem with Different Techniques: A Comparative Study

Assigning n’s entities to m’s other entities in an invertible manner is a challenging task that arises in various circumstances. In combinatorial optimisation, assignment problems are a heavily researched area. These problems have many applications in different domains like production planning, VLSI design for telecommunications, economics, etc. The assignment problem plays a crucial role in assigning things to machines so as to reduce assignment costs. To achieve this purpose, this paper examines many ways for solving assignment problems, including Game theory, choice under uncertainty and Graph theory approaches. In order to demonstrate the efficacy of these models, the results were evaluated against one another. Throughout the study, modifications and special situations of the assignment problem and its applications are also examined. The simulation of the proposed WAP approaches is carried out using MATLAB software. The computational findings for a collection of randomly created problems of varying sizes demonstrate that the proposed methods are capable of locating solutions of high quality.

Analysis Of The Application Of Merle H. Mishel's Theory: Uncertainty In Illness Theory In Patients With Colon Cancer Cases

Introduction: Mishel's Theory of Uncertainty in Illness offers insights into how patients navigate uncertainty in health contexts. This study examines its application in individuals with colon cancer.Methods: Literature review and case study analysis were employed to explore Mishel's theory's relevance in colon cancer care.Results: Mishel's theory provides a framework for understanding and addressing uncertainty among colon cancer patients. Nursing interventions focusing on education, social support, and coping strategies facilitate patient adaptation.Conclusion: Mishel's Theory of Uncertainty in Illness enhances nursing practice by guiding interventions tailored to address uncertainty in colon cancer patients. Integrating this framework fosters patient understanding and coping, ultimately improving quality of care and patient outcomes.

Windy Postman Problem Under Uncertainty

The postman problem is one of the optimization problems that has many applications in today’s world. With the progress of science, such optimization problems are used more. The postman’s problem is to find a route that has the minimum cost. The postman starts the route from one point and returns to the same point and must travel all the routes. If the round-trip costs are symmetric, in other words, the round-trip cost is the same, the problem is called the Chinese postman problem, and if the costs are asymmetric, that is, the round-trip cost is different from the return cost, the problem is called the windy postman problem (WPP). Here we describe two models of the WPP, which include the WPP and the rural WPP. The model for these problems in the real world needs to apply uncertainty theory. Liu expressed the theory of uncertainty. Here is a model of the windy postman uncertainty problem and a model of the windy rural postman uncertainty problem. So far, there has been no such uncertainty analysis on these models. To solve these problems, we use the Lagrangian heuristic method after turning it into a deterministic model. To solve these problems, after turning the problem into a deterministic problem, the heuristic Lagrangian method was used, and for the wind postman problem, we stated the corresponding Lagrangian algorithm and for the rural wind postman, the Lagrangian algorithm. Examples of these are problems and uncertainties. We expressed them and finally solved them by direct and Lagrangian methods.

Optimized distance measures on hesitant fuzzy numbers: An application

As the rapidly progressing applications of uncertainty theories, the need for modifications to some of their existing mathematical tools or creating new tools to deal correctly with them in various environments is also exposed. Hesitant fuzzy numbers (HFNs), as a particular case of fuzzy numbers, are not an exception to this rule. Considering the necessity of determining the distance between given HFNs in many of their practical applications, this article shows that the existing methods either do not provide correct results or are not able to meet the needs of users. This paper aims to present new methods for distance measures of hesitant fuzzy numbers. To do them, three prevalent distance measures, i.e., the generalized distance measure, the Hamming distance measure, and the Euclidean distance measure, will be optimized into three distinct trinal categories. With the approach of reducing error propagation via reducing some unnecessary mathematical computations, new distance measures on HFNs will be introduced, first. The middle is the modification of the first category, which is more suitable when the given HFNs are equal-distance by the previous formula. Also, as the third category, the weighted form of these distance measures has been proposed, to be used where the real and membership parts of HFNs are not of equal importance. As an application of these, a TOPSIS-based technique for solving multi-attribute group decision-making problems with HFNs has been proposed. A numerical example will be implemented to describe the presented method. Finally, along with the validation of the proposed method, its numerical comparison with some other existing methods will be discussed in detail.

A New Coherent Risk Measure of Entropic Value at Risk for Uncertain Systems

Uncertain risk analysis was initially explored by Liu, who introduced the concepts of loss function and risk index within uncertainty theory. Various risk measures, such as value at risk (VaR), tail VaR (TVaR), expected loss and entropic VaR (EVaR), have been proposed within probability theory. To date, Peng extended the concepts of VaR and TVaR, while Liu and Ralescu extended the notion of expected loss for uncertain systems. This paper mainly extends the concept of EVaR as a novel uncertain coherent risk measure. It will be demonstrated as the tightest upper bound one can find using Chernoff inequality for the VaR. In addition, we verify the properties of EVaR under uncertain measure, including positive homogeneity, translation invariance, monotonicity and subadditivity under independence. Furthermore, this paper provides a comparison of VaR, TVaR and EVaR under uncertain measure. Two examples will be given to illustrate this comparison.

НЕВИЗНАЧЕНІСТЬ В КОНТЕКСТІ ФОРМУВАННЯ ФІНАНСОВОЇ СТРАТЕГІЇ

The financial development of business entities and the strengthening of the influence of external factors on it actualize the renewal and further development of the uncertainty theory. Therefore, the purpose of the study is to develop conceptual approaches to the generation of uncertainty management methodology to build a theoretical basis for the development of a financial strategy taking into account the uncertainty factor. Groups of factors influencing uncertainty on the formation and implementation of the financial strategy of business entities were analyzed. The essence and content of strategic uncertainty are determined from the point of view of economic theory, adaptive approach, informational approach. The classification features of uncertainty are summarized: time horizon, focus, level of impact, predictability, information requirements, difficulty of forecasting,scope, the degree of impact on business,ability to manage. On the basis of the conducted content analysis, the characteristics of uncertainty and strategic uncertainty are distinguished. A comparative description of tactical and strategic uncertainty in the plane of financial and economic activity is given. It is noted that the theory of uncertainty has a complementary nature and contains theories, concepts of management, financial management, economic theory, risk management, strategic management, praxeology, and applied mathematics. The system of basic concepts on which the uncertainty theory is based is summarized: theory of "black swan", theory of games and economic behavior, theory of chaos, theory of risk management, theory of risk management, theory of complex adaptive systems, theory of asymmetric information, theory of stochastic control, theory of options, dynamic capabilities, the open system theory. The components of the methodology of strategic uncertainty management are systematized and substantiated. The methodical provision of strategic uncertainty management is substantiated. The main components of strategic uncertainty management are determined, which allows building the gradient of financial development of the business entity.

Care of a Patient with Planned Left Ventricular Assist Device According to Michel’s Theory of Uncertainty in Dısease: Case Report

Care of a Patient with Planned Left Ventricular Assist Device According to Michel’s Theory of Uncertainty in Dısease: Case Report

Efecto de una intervención enfermera sobre la incertidumbre de familiares en Cuidado Intensivo

Introduction: Family members of patients admitted to an Intensive Care Unit present high uncertainty level due to not knowing what is happening and to not having clear details about the related events; therefore, interventions are required to allow modulating those levels. Objective: To evaluate the effect of an educational Nursing intervention compared to conventional care on the uncertainty of family members of patients hospitalized in an ICU. Materials and methods: An experimental study with a sample comprised by 132 relatives of patients admitted to an ICU, randomly distributed in four Solomon groups (33 in each group). The Nursing intervention based on the concepts of the Uncertainty in Illness Theory was applied to both experimental groups and devised under the Whittemore and Grey parameters with three moments: assessment; education about the relative's hospitalization in the ICU; and accompaniment. This was done with pre-assessments for two groups and post-assessments for the four groups, using the PPUS-FM Uncertainty Scale. The data were analyzed by means of descriptive statistics and respective non-parametric analyses. The study took into account the ethical principles in research. Results: The family members in the experimental groups presented a lower final uncertainty level when compared to the control groups, with a difference of 73.04 points and a p-value of 0.001. Discussion: Standardized interventions and under a theoretical model allow reducing uncertainty in relatives of patients in ICUs. Conclusions: The Nursing intervention based on the Uncertainty theory allows reducing uncertainty in relatives of patients hospitalized in an Intensive Care Unit.

Uncertain logistic regression models

<abstract><p>Logistic regression is a generalized nonlinear regression analysis model and is often used for data mining, automatic disease diagnosis, economic prediction, and other fields. In this paper, we first aimed to introduce the concept of uncertain logistic regression based on the uncertainty theory, as well as investigating the likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters.</p></abstract>

Evaluating the Connectivity Reliability of the Seaborne Transportation Network Using Uncertainty Theory

Evaluating the Connectivity Reliability of the Seaborne Transportation Network Using Uncertainty Theory

Empirical Approach on Uncertainty Analysis of Differential Equations in Mathematical Modeling

Uncertainty theory, introduced by Professor Baoding Liu of Tsinghua University in China in 2007, has lately made significant progress. It serves as an advanced and adaptable mathematical tool for modeling uncertainties and handling unforeseen outcomes that may arise when using likelihood and/or fuzzy set approaches, which are sometimes favored alternatively. Mathematical models are used in several academic disciplines to provide accurate quantitative estimations based on facts. Crucially, these mathematical frameworks must offer a dependable evaluation of the certainty in their predictions. Uncertainty quantification (UQ) is a discipline that focuses on delivering accurate and dependable assessments of trust regarding forecasts made by models. Empirical models utilize data and statistical methods to build connections between parameters in a system, whereas mechanistic frameworks are built upon prior knowledge and understanding of the underlying mechanism that governs changes in the overall structure. This article will demonstrate the effectiveness of a data-driven empirical technique in solving ordinary differential equations under conditions of uncertainty. The research aims to determine the practicality and accuracy of its approach compared to existing numerical methods, which may often yield unsatisfactory results.

Cooperative Optimization of Bus-Subway-Shared Bicycle Based on Uncertainty Theory

This research considered the uncertainty of the operation time of shared bicycle, constructed the urban coupling network, and established the optimization model for the cooperative operation of conventional buses, subways and shared bikes. In the programming model, the optimization objective is to minimize the total travel time of passengers, and by optimizing and adjusting the departure intervals of conventional buses and subways as well as the stopping time of vehicles, the travel efficiency of passengers is improved. The interchange cooperative system composed of Nanchang Metro Line 1 and Bus 520 and the surrounding shared bicycle concentration area was taken as the case study. According to the optimization results, the passenger waiting time, passenger transfer time, and total passenger on-board time are effectively reduced by 21.5%, 23.2%, and 7.37%; ultimately, the total travel time is reduced by 8.2% at a confidence level of 95%. Case analyses show that the conventional bus stopping time has the greatest impact on the total travel time of the passengers as compared to the other variables. The research is of positive practical significance for promoting the efficient and coordinated development of urban transportation networks and facilitating the travel of residents.

Uncertain random accelerated degradation modelling and statistical analysis with aleatory and epistemic uncertainties from multiple dimensions

Accelerated degradation testing (ADT) is commonly employed for degradation analysis and reliability evaluations. Due to limitations in practical ADTs, aleatory and epistemic uncertainties are simultaneously embodied in time, unit, and stress dimensions but they are not considered comprehensively and integrated scientifically currently. To address such problems, we focus on the most common situation in ADTs that test time is relatively long (reflecting aleatory uncertainties in time dimension) while the numbers of test units and stress levels are quite small (reflecting epistemic uncertainties in unit and stress dimensions), and establish an uncertain random accelerated degradation model (URADM). In the URADM, aleatory and epistemic uncertainties are quantified using probability theory and uncertainty theory, respectively, and they are integrated using chance theory. Then, first passage time and reliability evaluations are derived. Next, a three-step uncertain random statistical method is presented for parameter estimations, where aleatory and epistemic uncertainties are divided carefully and quantified separately. A simulation study and a practical case are conducted to show the effectiveness of the URADM. Results reveal that the URADM can not only predict deterministic degradation trends with high accuracy as existing methods do, but contribute degradation boundaries well covering ADT data with narrower boundaries and higher stability.

A Simulated Annealing Algorithm for Quay Crane Scheduling and Assignment Problem Under Uncertain Conditions

In this paper, a mixed integer programming (MIP) mathematical model was formulated for the quay crane scheduling and assignment problem (QCSAP) in container terminals with the aim of minimizing the total cost of activities. With the assumption that due to the uncertain nature of the ship’s activities and operations, we have considered most of the parameters of the problem to be indeterminate in order to bring the optimal solution closer to the real world. In this paper, the uncertain nature of the parameters was investigated by using uncertainty theory and in the form of indeterminate quantities. These uncertainties might be subject to the conditions, such as the failure of cranes, container terminal transportation means, congestion in the dockyard, adverse weather conditions, etc. Further, considering the large dimensions of such real problems and the complexity of solving them in terms of processing time, the simulated annealing (SA) meta-heuristic optimization algorithm was utilized to solve the above model. The proposed algorithm has been coded by MATLAB software and its efficiency was put on test by comparing its results for a low-dimensional problem with the accurate solution from GAMS software is compared in terms of computational times for small sizes. The proposed algorithm exhibited promising potentials for quick approximation of usable solutions to high-dimensional QCSAPs. The calculations show that, nondeterministic model with the help of the above proposed algorithm can be one of the basic factors in increasing the productivity of container terminals and the main purpose and advantage of this research is compared to the basic model presented by Tavakkoli-Moghaddam et al. in 2009 which has been analyzed and investigated with definitive data and optimized with the help of genetic algorithm. In addition, some restrictions were added to the basic model in order to make the above crane assignment scheduling problem more practical.

On the choice problem in two-dimensional Ellsberg experiment with randomness and uncertainty

ABSTRACT The classical Ellsberg experiment provides decision-makers with a choice problem in which the likelihood of winning the gambling is uncertain. In this paper, we consider a two-dimensional Ellsberg experiment in which the likelihood of winning the gambling and the income after that are both uncertain, establish a mathematical model to characterize the corresponding Ellsberg experiment and solve the choice problem based on probability theory, uncertainty theory, and chance theory.

Interval strategy-based regularization approach for force reconstruction with multi-source uncertainties

A force reconstruction method for uncertain structures instead of direct measurement is proposed in this paper. Considering the multi-source uncertainties as unknown-but-bounded (UBB) parameters including unknown structural dynamics and measurement errors, this study proposed a novel force reconstruction method using an interval strategy-based regularization approach with truncated singular value decomposition (TSVD). To overcome the drawbacks of incompleted models and limited information, the overall modelling and inversion of the interval force are reconstructed based on the non-probabilistic uncertainty theory. First, The interval uncertainty-based kernel function for force inversion is deduced using the second-order interval perturbation method, in which the uncertainties of the reconstructed force originate from the uncertain singular values and measurement errors. Based on the regularization of the TSVD, the interval bounds of the singular values can be accurately obtained. Combining the two typical regularization approaches, a novel interval strategy-based regularization approach is proposed to optimize the best regularization parameter based on the uncertain interval distribution of singular values. This novel strategy can not only balance the two typical regularization approaches but also reduce the burden of force inversion. Two numerical examples including a truss and space capsule are applied to access the proposed method, and the estimated accurate force can prove the effectiveness.

Is the economic uncertainty- human health relationship nonlinear? An empirical analysis for the China.

The health costs of economic uncertainty always remain a major concern among policymakers of China. The theoretical and empirical literature on the economic uncertainty-human health nexus is still in its infancy stage. This study is firmly rooted in the economic uncertainty theory advanced by Baker, Bloom, & Davis. In this study, the primary objective of the analysis is to estimate the asymmetric impact of economic uncertainty on human health in China's economy. In order to evaluate the short and long-run estimates of economic uncertainty on human health across various quantiles, we have employed the linear and nonlinear QARDL models. The linear QARDL model shows that the long-run relationship between economic uncertainty and the infant mortality rate is positive and significant at all quantiles, while the long-run relationship between economic uncertainty and the death rate is positive and significant at higher quantiles. The nonlinear QARDL model reveals that, in the long run, the relationship between the positive shock of economic uncertainty and the infant mortality rate is positive and significant at quantiles 0.30 to 0.95, while the long-run relationship between the positive shock of economic uncertainty and the death rate is positive and significant at higher quantiles. The relationship between the negative shock of economic uncertainty and the infant mortality rate is negative and significant at the highest quantiles, while the relationship between the negative shock of economic uncertainty and death rate is negative and significant at higher quantiles in the long run. The findings indicate a positive relationship between economic uncertainty in China and higher rates of infant mortality and death. Thus, adopting suitable policies for controlling economic uncertainty can help in improving human health in China.