Uncertainty Theory Research Articles

An uncertain bi-objective mean-entropy model for portfolio selection with realistic factors

In the imprecise investment environment, there are many indeterminate factors impacting security returns. This paper introduces a portfolio optimization problem where cross-entropy is utilized to control portfolio risk within the framework of uncertainty theory and presents an uncertain bi-objective mean-entropy portfolio selection model. To be more realistic, some realistic factors such as minimum transaction lots, dividend factors and tax factors are also considered. By introducing a risk preference coefficient, the bi-objective model is converted into a single-objective model and some equivalents are discussed. Additionally, a hybrid intelligent algorithm integrating a genetic algorithm with uncertain estimation is designed to solve the proposed model. Finally, a case study is executed to confirm the practicability of the model and the performance of the algorithm, and an empirical analysis based on the proposed model and the uncertain mean–variance model is developed to illustrate the advantage of the uncertain mean-entropy model in practical investment.

Evaluation of extracting biomass energy using a strategic decision support system

Electricity significantly influences the long-term development of society. Traditional power generation technologies have encountered numerous challenges. The rising demand for power generation and the scarcity of fossil fuel resources have prompted the adoption of renewable energy sources. Biomass is a renewable energy source that can be utilized to generate power and promote a more sustainable environment. This paper presents improved Pythagorean Probabilistic Hesitancy Fuzzy Set (PPHFS) multi-criteria information fusion processes. The development of connection numbers (CN) and the proposal of a distance measure between CNs have been initiated. The suggested solution involves a distance measure based on the CN score function. Set-pair analysis (SPA) is a prior uncertainty theory that incorporates three elements of CN and overlaps with the PPHFS. Biomass energy generation poses a fuzzy multi-criteria decision-making (MCDM) problem that requires a wide range of resources for evaluation. The study introduces a novel Projection Ranking by Similarity to the Referencing Vector (PRSRV) and Simultaneous Evaluation of Criteria and Alternatives (SECA) to effectively handle data ambiguity and generate weights for all criteria in the MCDM problem. The criteria’s weights were found to fall within the range of 0.2122 and 0.0232. The proposed method indicates that animal residues are the most desirable resource for bioenergy production, followed by municipal solid residues, agricultural residues, forest and crop residues, industrial residues, and algae. The research aims to assist decision-makers and organizations in evaluating and prioritizing various energy sources on a sustainable scale. The method presented exhibits superior data adaptability compared to other MCDM approaches.

Uncertain programming model for designing multi-objective reverse logistics networks

Given the contradiction between the rapid growth of products and the modest recovery rate of end-of-life products, there is a pressing need to understand the societal significance of establishing a reverse logistics network for end-of-life products. This research constructs an open-loop five-tier reverse logistic network model encompassing customers, centres for collection, disassembly and inspection, remanufacturing, and disposal. A multi-objective mixed-integer nonlinear programming model under uncertainty has been developed. Unlike previous research, this model accounts for surrounding residents' disutility of facilities while simultaneously minimizing economic costs and environmental impact. Besides, uncertainty theory is introduced in solving the proposed model. More specifically, the formulated model converts all uncertain variables into uncertain distributions by implementing the uncertain multi-objective programming method. Furthermore, a customised non-dominated sorting genetic algorithm III (NSGA-III) is proposed and is employed for the first time to address facility selection and recycling volume distribution within the network. The model is then validated using a real-life case study focusing on end-of-life vehicles in Changchun, China. This research could assist decision-makers in both governmental and private sectors in achieving a balanced approach to the interests of the economy, environment, and local communities comprehensively when designing reverse supply chains.

Analysis of vertical differentiation of vegetation in Taishan World Heritage site based on cloud model

While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

A Bibliometric Analysis of Possibilistic Portfolio Selection Models

Possibility theory is one of the most used uncertainty theories in decision-making. This study aims to examine possibilistic portfolio selection models. In this context, we perform their bibliometric analysis with the Web of Science (WOS) data, using the Bibliometrix, without limiting the timespan. We get many results by analyzing the data of 303 documents, of which timespan is from 1995 to 2023. We see that W. G. Zhang is the most influential author in this field. The paper introducing the possibilistic mean-variance (MV) model is the most influential document in this field. The paper introducing Markowitz’s MV model is the most influential reference. China is the most productive country in this field, whereas The South China University of Technology is the most productive institution in this field. Fuzzy Sets and Systems is the most influential journal in this field. Variance originated from Markowitz’s MV model is the most critical keyword plus in this field. It has also maintained its trend topic position for a long time. To the best of our knowledge, this is the first paper making a bibliometric analysis of possibilistic portfolio selection models.

Order-Guided Non-Conservative Maximum Flow in Uncertain Network Interdiction Problem with Budget Constraint

Probability theory in decision science fails in the absence of sufficient data. In these situations, uncertainty theory is applied to the problem-solving process, taking into account the opinions of the domain experts. In order to minimize the maximum flow carried by the evader, we describe an interdiction problem in an uncertain network with intermediate storage and resource constraints. Such approaches would help law enforcement combat drug-related and smuggling activities. This work first converts the bi-level deterministic conservative model of the interdictor vs evader problem (IEP) into a non-conservative deterministic IEP. With intermediate storage, a binary interdiction variable, and an interdiction resource constraint, the model is further transformed into an uncertain measure-based model. Then, utilizing uncertainty theory, the uncertain model is again converted into its deterministic equivalent. We have presented two techniques to identify the arc that the interdictor needs to block and to direct the order-guided maximum flow that the evader needs to deliver to the target. The models’ and algorithms’ efficacy is confirmed by an example. Lastly, a graphic representation of the shifting trend of the value of the objective function (VOF) is shown for a range of confidence levels.

Enhancing pore pressure prediction accuracy: A knowledge-driven approach with temporal fusion transformer

Pore pressure plays a crucial role in various fields, including geology, petroleum engineering, hydrology, and carbon capture, utilization, and storage (CCUS). Traditional pore pressure prediction methods, which rely on physical models, often fail to meet the required accuracy standards due to their inflexibility and technical parameterization. Although machine learning methods have been increasingly used for pore pressure prediction, they struggle to integrate domain-specific knowledge and capture the inherent temporal dynamics of the data, resulting in suboptimal prediction accuracy. To address these limitations, this study introduces the Knowledge-Aware TFT model, which determines the key parameter attributes for pore pressure prediction by comprehensively analyzing well logging data, rock mechanics theory, and pore pressure evolution theory. Additionally, the model effectively captures the temporal dynamic characteristics of the data, thereby enhancing the accuracy of pore pressure prediction. To validate the model's accuracy, uncertainty theory is employed to evaluate the experimental results, providing a description of the uncertainty associated with pore pressure prediction and supporting credibility assessment. A case study involving three vertical wells in Block X, China, compares the Knowledge-Aware TFT model with SVM, XGBoost, MLP, and LSTM models, further confirming its superior prediction accuracy (R^2 = 0.997, RMSE = 0.135) and demonstrating its practicality in pore pressure prediction. In conclusion, the proposed pore pressure prediction model not only improves prediction accuracy but also achieves excellent results in practical applications, making it a viable alternative to traditional technologies.

Концептуализация феномена негативной идентичности

This article provides a systematic analysis of research devoted to the study of various aspects of social identity in order to determine the theoretical framework for studying the phenomenon of negative identity within the framework of sociology. The following theories are considered: social identity, realistic conflict of identity uncer-tainty. The theory of social identity constitutes a conceptual model elucidating how individuals define their iden-tities through affiliation with social groups, emphasizing the significance of social categories and stereotypes in identity formation. The theory of realistic conflict offers an approach to analyzing conflicts based on the prem-ise that they arise from struggles for material, “realistic” values and resources, even when groups are nominally divided. The theory of identity uncertainty, in turn, focuses on situations wherein individuals encounter difficul-ties in defining their identities, encompassing the concept of “entitativeness” within its scope. In synthesizing these theoretical perspectives, this article offers a comprehensive framework for understanding the multifacet-ed nature of negative identity within sociological discourse. By elucidating the underlying mechanisms and contextual factors shaping negative identity formation, scholars can advance theoretical and empirical inquiries into this complex phenomenon.

Convex set-oriented singular value decomposition with bounded uncertainties

Singular value decomposition (SVD) has played a crucial role in mathematics and applied mathematics, particularly in the field of practical engineering technology, which is almost indispensable in these fields. However, incomplete information caused by uncertainties from multiple sources and fluctuations in inherent properties during engineering practice can seriously interfere with obtaining singular values. Based on the convex set uncertainty theory, a novel convex set-oriented singular value decomposition (CSSVD) is proposed to overcome the limitations of using the nominal SVD method. The CSSVD aims to accurately and efficiently calculate the uncertainty bounds of singular values and vectors through the bounds and correlations of initial uncertainty parameters. Traditional probability methods rely on a sufficient sample size, and when the sample size is insufficient, accurate results cannot be obtained. However, the CSSVD method is based on set theory and can still demonstrate excellent performance in scenarios with small samples. This study investigates the detailed derivation steps of the proposed CSSVD method, taking into consideration properties such as normalization, orthogonality, and Monte Carlo simulation (MCS) to verify the accurate bounds of convex sets. The proposed method is finally applied to several numerical examples to demonstrate its superiority. This includes verifying singular values and vectors, handling high-order matrices, matrices with close singular values, and rectangular matrices. An application of image denoising is also used to evaluate the effectiveness of the proposed method.

Socioeconomic Status as a Factor of Male Domestic Violence: Adaptations Due to Paternity Uncertainty Theory

Socioeconomic Status as a Factor of Male Domestic Violence: Adaptations Due to Paternity Uncertainty Theory

A robust internal model control design for low-quality waste heat power generation systems

Recovering low-quality waste heat using industrial waste heat is challenging, and the reuse technology needs to erupt. Moreover, the gas source of low-quality waste heat is relatively volatile, which makes it challenging to keep the actual working condition of the plant stable. Therefore, it is inspiring to research the robustness of root-waste heat power generation processed measurement and control system to improve the stability of the plant operation. Hence, in this paper, we have applied uncertainty theory to analyze it and formulate the uncertainty model based on the Bode diagram. We also proposed a control method based on the uncertainty model, which combines robust control and internal model control to make the roots waste heat power generation system operate stably under the effect of external disturbances and changes of internal structure or parameters in actual operation. Experimental results show that the robust internal model control method has a speed deviation of no more than 7.9 r/min compared with the PID control method. The adjustment time to track the set value does not exceed 73.1 seconds within the allowed fluctuation range. The fluctuation variance is 30.95% of that of the PID controller. The dynamic performance is better, with strong anti-interference capability and significantly improved tracking performance. It ensures the stability of the roots-type waste heat utilization system, which is essential for future intelligent grid-connected power generation.

Bayesian inference in the framework of uncertainty theory

Bayesian inference in the framework of uncertainty theory

Intelligent Deep Adaptive Intuitionistic Fuzzy Classifier Based Dyslexia Prediction Among Children at its Early Stage

The inability of standard linear research to clearly define the role of cognitive abilities in reading difficulties is most likely due to the fact that reading and the components that are related to reading are both part of a system of factors. There are numerous interrelated and mitigating elements that conventional statistical models are unable to deal effectively. This work aims to increase dyslexia prediction accuracy by introducing two stage non-linear methodology which incorporate both deep learning and uncertainty theory. In the first stage, the pattern of dyslexic symptoms is learned by introducing deep adaptive neural network. During the training phase, the hyperparameters weight and learning rate are optimized using a metaheuristic algorithm called the fruit fly optimization algorithm. This algorithm imitates the behavior of fruit flies to search for the best set of values. The acquired knowledge is infused in the uncertainty inference model known as intuitionistic inference classifier. Each characteristic that defines an instance as dyslexic or non-dyslexic is represented in tristate degrees to precisely address the inconsistent or vague instance which neither or nor exhibit dyslexic symptoms by introducing the hesitancy factor. The simulation results support the proposed intuitionistic fuzzy rules prominently improves the accuracy rate of dyslexia prediction compared to the other existing state of arts.

Uncertain stochastic hybrid zero-sum games based on forward uncertain difference equations and backward stochastic difference equations

We investigate the interplay between forward uncertain difference equations and backward stochastic difference equations, which belong to distinct mathematical frameworks: uncertainty theory and probability theory, respectively. By establishing the connections between uncertain expectation, probabilistic expectation, and chance expectation, we develop the chance theory framework for analyzing uncertain stochastic hybrid zero-sum games constrained by both forward uncertain difference equations and backward stochastic difference equations. Through an equivalent transformation, we convert these games into deterministic difference equation solving problems. By backward-solving the difference equations and applying mathematical induction, analytical expressions for the equilibrium solutions of two types of uncertain stochastic hybrid zero-sum games are obtained. Furthermore, we explore solution methods for zero-sum games with different operational orders of uncertain and probabilistic expectations and highlight the fundamental differences through numerical examples.

Use of the theory of measurement uncertainty in procedures for data processing and results obtained by checking-calibration gas flow meters

The work is dedicated to the certification of natural gas consumption meters – calibration methods for processing, which meets the requirements of modern international standards. This article discusses the rules for the processing of measurement data and the correct formatting of the obtained results. An illustrative example of a real practical measurement is highlighted in the article.

The role of internal culture for coping with uncertainty in forest management

Theories about uncertainty usually focus on its reduction. However, a significant source of uncertainty lies in the innovative and creative capabilities of the human mind. This particular source entangles us in contradictions: both reducing and creating uncertainty simultaneously. Therefore, a theory of uncertainty that solely investigates ways to reduce it is incomplete. It must be complemented by aspects of how uncertainty contributes the betterment of people.In our study, we establish a foundation for extending the theory of uncertainty in five steps. Drawing on the insights of William A. Duerr and Jean B. Duerr, who assert that faith, rather than measureable facts, guides forest resource management under uncertainty, we concentrate our analysis on informal institutions, customs, traditions etc.Firstly, we reconstruct the evolution of the internal culture of forestry. Secondly, we delineate the bourgeois culture and compare it with the internal culture of forestry. Thirdly, we develop a theoretical approach to the re-valuation of uncertainty. Fourthly, we combine the theoretical approach with a historical analysis of the Great Enrichment to provide a comprehensive understanding of an overall picture how people re-valuated uncertainty. This involves placing higher value on the chances of self-induced unforeseen changes higher than on the associated dangers. Finally, we summarize our findings and close the paper with suggestions for future research on the extension of a theory of uncertainty.

Multi-objective planning for time-cost trade-offs in multi-project parallel environment

PurposeProject managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.Design/methodology/approachIt employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.FindingsThe study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.Originality/valueThe research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

Soft rough sets based on covering and their applications

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ICT Innovative for Adapting and Mitigating Climatic Variability: Towards an Integrated Approaches for Sustainable Environment

The most significant threat to both human and planetary health is climate change, which is caused by changes in the weather and air quality on Earth because of human activities. The climate is changing on a global scale, which has effects on a variety of domains, notably transportation, agriculture, livestock and so on. The variability of climate threatens sustainability and its pillars for any nation. Evidence of that, regarding 68.5 million people were affected, and $131.7 billion in economic losses were incurred; storms, hunger, wildfires, and droughts contributed for almost 93% of these costs. Globally, the many effects of climate change have sparked research efforts to achieve sustainable development (SD). From a scientific standpoint, the concepts of “adaption” and “mitigation” have been embraced to treat obstacles of climate. Hence, the notion of information communication and technology (ICT) is embracing to mitigate and adapt climate variability. Accordingly, the evaluation and ranking alternatives of ICT where this is objective of our study. Herein, mutli-mriteria mecision making (MCDM) techniques are the primary factor in this study. As opinion weight criteria method (OWCM) utilized for analyzing ICT’s criteria and sub-criteria and obtaining its weights. Whilst these weights have been utilized in multi-attributive ideal-real comparative analysis (MAIRAC) to rank alternatives of ICT. Whilst Tree Soft Sets is leveraging for modeling ICT’S criteria and its sub-criteria. Essentially, these techniques have the ability to apply in uncertainty environment and incomplete information through supporting neutrosophic theory, which characterized with uncertainty theory especially, single value neutrosophic sets (SVNs) to construct soft decision maker (SDM) framework. Ultimately, the constructed SDM is applied in a real-world case study. The findings of SDM framework indicated that alternative1 (ALT1) is the optimal conversely (ALT2) is the worst alternative.

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.