Uncertainty Theory Research Articles

Adherence and uncertainty during rehabilitation for urinary incontinence: Validation of a scale

AbstractWe sought to create an Italian version of Mishel's Uncertainty in Illness Scale, dedicated to people undergoing conservative rehabilitation for urinary incontinence, for studying uncertainty as a determinant of therapeutic adherence. Urinary incontinence has a high prevalence worldwide, ranging from 25% to 45%. Incontinence is often treatable with conservative interventions but demands a long and intensive commitment from the patient. Results are not immediate, and relapses are possible. These patients can experience uncertainty and difficulty complying with rehabilitation programs, hence the importance of the therapeutic relationship with a healthcare professional. Mishel's theory of uncertainty can be used to measure uncertainty and the effects of such a relationship, but no instrument currently exists for this purpose. Prospective observational study enrolling all male and female adult patients admitted to a nurse‐led outpatient pelvic clinic for non‐neurogenic urinary incontinence, excluding puerpera. A scale named MUIS‐PF (pelvic floor) was created, based on previous versions of Mishel's scale, and administered during the first consultation and at the end of the rehabilitation program. Internal consistency was assessed, and exploratory factor analysis was conducted. A total of 109 patients enrolled (54 M, 55 F) aged 64 ± 5 years, medial initial leakage 245 grams/day, IQR [90; 370]. Seventy‐nine percent obtained continence; there were no dropouts during the study. Internal consistency of the MUIS‐PF was high (93%), and structure analysis yielded a clear separation of the factors. Patient uncertainty decreased significantly at the end of the program compared to the first consultation (p < 0.001). The MUIS‐PF is valid and reliable. Utilizing the correct approach, the nurse could significantly reduce the uncertainty of persons with incontinence by listening, giving clear information and searching for the best solution for their continence issues.

Selection of aerodynamic characteristics and engine parameters of a maneuverable aircraft under epistemic uncertainty

At the preliminary stage of aircraft design, it is usually required to solve the problem of insufficient initial data for applying traditional algoristic-type mathematical programming models. In many cases, numerous input parameters cannot be accurately specified at the time of making design decisions. If the inaccuracy of parameters is not taken into account, the actual values of target functions may differ significantly from the calculated ones when solving optimization problems. In this regard, the issue of current interest is the development of algorithms to improve the reliability of design decisions under conditions of epistemological uncertainty when experts engage in the formation of initial data. The paper considers the problem of selecting aerodynamic characteristics and engine parameters of maneuverable aircraft under conditions of uncertainty associated with inaccuracy of expert data. The applied problem under consideration is further complicated by the necessity to include “black box” models in the algorithms being developed. The paper proposes algorithms that apply the uncertainty theory together with “black box” models that implement optimization calculation techniques derived from previous engineering practice of aircraft design. Using these algorithms, experts are able to set uncertain parameters whereby the lack of data is factored in using uncertainty distribution functions. In cases of monotonicity of uncertain parameters in target functions, application of the uncertainty theory provides a significant reduction in computational costs compared to the method of statistical modeling in optimization calculations. The paper presents the results of computational studies of the developed algorithms. A genetic algorithm (mathematical optimization solver) is used to optimize the search for design solutions. Pareto frontiers are obtained for different confidence levels enabling to make design decisions including values of aerodynamic characteristics and engine parameters of maneuverable aircraft.

Research on network time reliability evaluation method based on uncertainty theory

In view of the problem that existing network time reliability assessment methods often only consider the impact of inherent uncertainty, while ignoring the impact of epistemic uncertainty caused by insufficient fault information on reliability assessment results, a new method based on uncertainty theory is proposed. According to the node range of network reliability, two types of measurement parameters of single node pair and multi-node pair time reliability are designed. The extended uncertain network model is proposed to directly model the epistemic uncertainty characteristics on nodes and links, and further single node and multi-node pair time reliability algorithms based on the most reliable path and the most reliable extended uncertain subnetwork are designed. Finally, taking a six-node network and the backbone network of China Education and Research Network (CERNET) as examples, the method is applied to evaluate two time reliability indicators, verifying the correctness and effectiveness of the method.

MCDM Method for Managing the Water Resources Based on Possibility Theory under Bipolar Fuzzy Environment

Background: Uncertainty is a common factor in every real-life decision-making problem. Possibility theory is one uncertainty theory in Fuzzy sets (FS). The possibility-based decision-making under a fuzzy environment is a significant multi-criteria decision-making (MCDM) method. Methods: A bipolar FS is an extension of a fuzzy set. With the bipolarity concept, we can handle both positive and negative thoughts. In this study, we have provided a possibility mean of a bipolar fuzzy number. We have developed a ranking method for bipolar fuzzy numbers using this pos-sibility concept. A novel possibility MCDM method is suggested for solving the water resources management (WRM) in the Nagpur area, Maharashtra State, India. Results: The MCDM technique is an effective tool for solving WRM problems in an area. Many uncertainties and bipolarities occur together in Nagpur water resources. WRM technique with fuzzy is one approach that can be used to solve the area's water problem. We have used the pro-posed MCDM to address the water-related issues of this district. With this proposed MCDM method, numerically, we employed water resource problems under a bipolar fuzzy environ-ment. Conclusion: The Nagpur area is covered by Basaltic rock and faces water shortage. The district is experiencing severe water shortages. Groundwater, surface water, and rainfall are three water resources considered as alternatives. According to the proposed MCDM technique in Nagpur district, Groundwater is the best water source from three flanks: quality of water, affordability, and availability.

Parameter Estimation of Uncertain Differential Equations Driven by Threshold Ornstein–Uhlenbeck Process with Application to U.S. Treasury Rate Analysis

Uncertain differential equations, as an alternative to stochastic differential equations, have proved to be extremely powerful across various fields, especially in finance theory. The issue of parameter estimation for uncertain differential equations is the key step in mathematical modeling and simulation, which is very difficult, especially when the corresponding terms are driven by some complicated uncertain processes. In this paper, we propose the uncertainty counterpart of the threshold Ornstein–Uhlenbeck process in probability, named the uncertain threshold Ornstein–Uhlenbeck process, filling the gaps of the corresponding research in uncertainty theory. We then explore the parameter estimation problem under different scenarios, including cases where certain parameters are known in advance while others remain unknown. Numerical examples are provided to illustrate our method proposed. We also apply the method to study the term structure of the U.S. Treasury rates over a specific period, which can be modeled by the uncertain threshold Ornstein–Uhlenbeck process mentioned in this paper. The paper concludes with brief remarks and possible future directions.

Applications of Strongly Deferred Weighted Convergence in the Environment of Uncertainty

In the context of uncertainty theory, we present strongly deferred weighted convergence of complex uncertain sequences. Also, we introduce strongly deferred weighted convergence of complex uncertain sequences in all five aspects of uncertainty, that is through almost surely, mean, measure, distribution and uniformly almost surely. Further, with the aid of interesting examples and diagram we investigate some interrelationships among these complex uncertain sequences.

Uncertain Logistic Population Model with Allee Effect

By considering the influence of various uncertain noises, we established an uncertain Logistic population model with Allee effect, which was characterized by an uncertain differential equation. Firstly, we obtained the solution of the model, and discussed the stability of the equilibrium state. Secondly, the unknown parameters in the model were estimated by using the generalized moment estimation method under the framework of uncertainty theory. Finally, the parameter estimations of the model and the properties of the solution were explained by an example analysis.

Supply chain management based on uncertainty theory: a bibliometric analysis and future prospects

Supply chain management based on uncertainty theory: a bibliometric analysis and future prospects

Holistic Methodology of Hypersoft in Tandem with Uncertainty-based Triangular Neutrosophic for Wave Energy Converter Optimization

Global warming, environmental degradation, and climate change have recently emerged as compelling arguments against the combustion of fossil fuels. As well Eliminating and forgoing the intake of fossil fuels is also encouraged by the Sustainable Development Agenda's goals. The healthy substitute for fossil fuels, renewable energy is one of the main devoid power sources found in our natural surroundings. An effective proposition for harnessing wave energy with an emphasis on increasing energy and efficiency are wave energy converters, or WEnCos. It contributes to producing a clean, limitless energy source that might drastically lessen our need for fossil fuels. Generally speaking, the selecting of the appropriate converter is an important endeavor that this study takes on. The process of selecting the optimal converter has been conducted based on effective benchmarks or criteria and its attributes. Multiple Criteria Decision Making (MCDM) techniques seem to be effective tools when it comes to handling the complex issue of benchmarking WEnCos. These approaches consider a variety of criteria, allowing for a comprehensive and nuanced examination. Each utilized technique plays vital role in our decision problem. Hence, CRiteria Importance Through Inter-criteria Correlation (CRITIC) method—which determines the weights of criteria and its attributes in decision making problems. These weights are utilized in Weighted Sum Product (WISP) method. Totally, these techniques are implemented under uncertainty theory of Neutrosophic which combined with soft sets notion to generate Triangular Neutrosophic HyperSoft (TrNSHSS). Totally, all these techniques are contributed to constructing soft decision-making model for analyzing and recommending optimal WEnCo.

Development of Theory of Uncertainty on COVID-19: Theory Derivation Based on Uncertainty in Illness Theory

Purpose: The purpose of this study is to develop the Theory of Uncertainty on COVID-19 using the strategy of theory derivation. Methods: Theory derivation was carried out in the following steps: review the literature to explore the phenomena related to uncertainty on COVID-19; select a parent theory that provides valuable concepts and a useful structure for derivation, and identify the concepts and structure of the parent theory to use in derivation; modify and redefine the concepts and structure of the parent theory to create a derived theory. In the literature review process, relevant findings were synthesized to support the propositions of the derived theory. Results: The Theory of Uncertainty on COVID-19 was derived from the Uncertainty in Illness Theory to make it relevant and applicable to a specific aspect of uncertainty on COVID-19, health-related uncertainty perceived by a person who has not contracted COVID-19. It is a middle-range theory targeting the general population and consists of a linear and unidirectional model centered on three themes: antecedents of uncertainty, appraisal of uncertainty, and coping with uncertainty. Conclusion: The Theory of Uncertainty on COVID-19 will be able to contribute to efforts to manage perceived uncertainty on pandemic diseases and improve individual biopsychosocial health in the future.

Reliability modeling and analysis of uncertain competing failure systems

The competing failure system is quite common in real life, such as automotive system and aerospace system. Two distinct failure modes usually coexist in this type of system: gradual internal degradation and sudden external shock, where the occurrence of any failure mode can lead to the system failure. The system failure such as aircraft accident may cause a terrible loss of human life and property. Hence, it is worth reducing the rate of system failure by improving the system reliability. While, reliability model is the foundation of any research of reliability and epistemic uncertainty exists in the process of modeling and estimating. Therefore, we introduce uncertainty theory to investigate the competing failure system with epistemic uncertainty. The internal degradation and external shock of the system are characterized by an uncertain differential equation and an uncertain renewal process, respectively. The system reliability is defined as the uncertain measure that neither internal degradation nor external shock surpass corresponding thresholds. In addition, some reliability formulas are derived for the systems subjected to extreme shock, cumulative shock, running shock and δ-shock, respectively. Finally, a case study with respect to micro-electro-mechanical system is given to show how the proposed model is implemented.

Navigating chronic uncertainty: a theory synthesis for nursing communication in life-limiting illness

BackgroundDespite the proven benefits of early palliative care, patient communication regarding these services remains elusive. Therefore, this paper aims to (a) provide a focused literature review on nurse palliative care communication addressing chronic uncertainty in life-limiting illness (LLI), (b) define the Reconceptualization of Uncertainty in illness Theory and Problematic Integration Theory within a nursing Unitary Caring Science philosophical worldview and, (c) synthesize these theories and literature review into a unique theoretical framework for early palliative care communication in acute care nursing.MethodTurner’s theory synthesis methodology was combined with a PRISMA-style literature review. The literature search was conducted in July 2023 and updated in May 2024 using EBSCOhost, Pubmed, and PsychINFO databases. A second literature search was undertaken to identify applicable uncertainty theories in chronic or LLIs.ResultsThis theory synthesis highlights the interconnectedness of all facets of uncertainty for those living with severe LLI (personal factors, types of uncertainty, and the nursing communicatory process) and conceptualizes uncertainty communication as a series of events happening simultaneously, not simply a cause-and-effect process.DiscussionThe framework resulting from this synthesis encourages nurses’ holistic understanding of the complex nature of uncertainty in LLI, highlights the integral communicatory role nurses have in their patients’ health and wellness, and promotes further nursing-specific communication research. Future research on enabling nurse-initiated early palliative care communication and narrative communication techniques will support patients’ values and dignity throughout more than a single hospital stay but their entire disease trajectory.

The Age of Uncertainty, Organization, and Chaos Theory

The Age of Uncertainty, Organization, and Chaos Theory

Uncertain Bass model with application to new energy vehicle sales forecasting

The dissemination of predictive innovation is critical to technological development and engineering management. Given the uncertainty that exists in the real-world scenario, this paper incorporates uncertainty theory into the Bass model to improve product sales forecasts. An uncertain Bass model is developed to consider dynamic market conditions and unpredictable consumer behavior. Applying the sales data of the BYD Song Plus electric vehicle, the effectiveness of the uncertain Bass model in predicting product sales is verified. In addition, the model calculates the first hitting time, further demonstrating its practicality.

Finding Efficient Solutions in Interval Multi-Objective Linear Programming Models by Uncertainty Theory

Interval multi-objective linear programming (IMOLP) ímodels are one of the methods to tackle uncertainties. In this paper, we propose two methods to determine the efficient solutions in the IMOLP models through the expected value, variance and entropy operators which have good properties. One of the most important properties of these methods is to obtain different efficient solutions set according to decision makers’ preferences as available information. We first develop the concept of the expected value, variance and entropy operators on the set of intervals and study some properties of the expected value, variance and entropy operators. Then, we present an IMOLP model with uncertain parameters in the objective functions. In the first method, we use the expected value and variance operators in the IMOLP models and then we apply the weighted sum method to convert an IMOLP model into a multi-objective non-linear programming (MONLP) model. In the second method, the IMOLP model using the expected value, variance and entropy operators can be converted into a multi-objective linear programming (MOLP) model. The proposed methods are applicable for large scale models. Finally, to illustrate the efficiency of the proposed methods, numerical examples and two real-world models are solved.

A condition‐based maintenance optimization method with oscillating uncertain degradation process

AbstractCondition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method.

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

An uncertain programming model for fixed charge transportation problem with item sampling rates

This paper analyzes the transportation issue involving multiple objectives and items with fixed costs amid uncertainty, which aims to increase net profit while minimizing carbon emissions, to determine an optimal product shipping strategy. This paper introduces the use of uncertain theory to address the transportation dilemma, considering various challenges such as potential uncertainties during the actual transport process. It involves defining variables such as supply, demand and the rate of product sampling qualification as uncertain factors, constructing mathematical models, and deriving the corresponding model as well as the respective equivalent form by means of uncertainty theory. A linear weighted method is adopted to reflect the significance of each objective as identified by policymakers and suggest a sparrow optimization algorithm combined with butterfly search for numerical experiments to discover the optimal solution. This demonstrates the practicality and effectiveness of the proposed models.

Uncertain Multilevel Programming Based on Hurwicz Criterion with Application to Resource Allocation

A number of uncertain multilevel programming models have been proposed and applied to decentralized decision-making problems. It is assumed that the models of uncertain multilevel problems involve uncertain variables. In these models, the chance constraints are used as constraints, and the objective functions vary according to the requirements of decision-makers. The objective functions in this paper are based on the Hurwicz criterion, which combines optimistic value and pessimistic value to find a balance between optimism and pessimism. To deal with situations in which decision-makers have this type of risk appetite, the uncertain Hurwicz multilevel programming model is proposed based on uncertainty theory and the Hurwicz criterion. Finally, two numerical examples and a case study on resource allocation are given to demonstrate the potential uses of this study.

Uncertainty measures: A critical survey

Classical probability is not the only mathematical theory of uncertainty, or the most general. Many authors have argued that probability theory is ill-equipped to model the ‘epistemic’, reducible uncertainty about the process generating the data. To address this, many alternative theories of uncertainty have been formulated. In this paper, we highlight how uncertainty theories can be seen as forming clusters characterised by a shared rationale, are connected to each other in an intricate but interesting way, and can be ranked according to their degree of generality. Our objective is to propose a structured, critical summary of the research landscape in uncertainty theory, and discuss its potential for wider adoption in artificial intelligence.