Journal articles: 'Application uncertainty'

1

Macdonald, Iain, and Paul Strachan. "Practical application of uncertainty analysis." Energy and Buildings 33, no. 3 (2001): 219–27. http://dx.doi.org/10.1016/s0378-7788(00)00085-2.

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Coleman, Hugh W., and W. Glenn Steele. "Engineering application of experimental uncertainty analysis." AIAA Journal 33, no. 10 (1995): 1888–96. http://dx.doi.org/10.2514/3.12742.

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Pronskikh, Vitaly. "Model uncertainty in accelerator application simulations." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 977 (October 2020): 164299. http://dx.doi.org/10.1016/j.nima.2020.164299.

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Payo, A., A. Baquerizo, and M. Losada. "Uncertainty assessment: Application to the shoreline." Journal of Hydraulic Research 46, sup1 (2008): 96–104. http://dx.doi.org/10.1080/00221686.2008.9521944.

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Dantan, J. Y., J. P. Vincent, G. Goch, and L. Mathieu. "Correlation uncertainty—Application to gear conformity." CIRP Annals 59, no. 1 (2010): 509–12. http://dx.doi.org/10.1016/j.cirp.2010.03.040.

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Jaka, Zgajnar, and Kavcic Stane. "Optimal allocation of production resources under uncertainty: Application of the muliticriteria approach." Agricultural Economics (Zemědělská ekonomika) 62, No. 12 (2016): 556–65. http://dx.doi.org/10.17221/238/2015-agricecon.

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Sahid, Hana' Hamidah. "It’s A Match: Strategi Pengurangan Ketidakpastian oleh Pengguna Perempuan di Aplikasi Kencan Online Bumble." PERSPEKTIF 12, no. 4 (2023): 1336–43. http://dx.doi.org/10.31289/perspektif.v12i4.9873.

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Every interpersonal relationship always goes through a stage of reducing uncertainty so that an interpersonal relationship can develop. This includes interpersonal relationships in online dating applications. This study examines the strategies used by female users to reduce the uncertainty in online dating application Bumble. Bumble was chosen as it’s the only online dating application that put woman in charge to make the first move. Uncertainty reduction theory was used to analyse the findings. Three informants were interviewed and literature studies from previous studies in similar topics were used to gain further information. The result shows that each informant has different approach and strategies to reduce the uncertainty while using online dating application Bumble. The strategies help each informant to decide whether to continue or stop the interaction while using Bumble.

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Ktata, Ismail, Fakhreddine Ghaffari, Bertrand Granado, and Mohamed Abid. "Dynamic Application Model for Scheduling with Uncertainty on Reconfigurable Architectures." International Journal of Reconfigurable Computing 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/156946.

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Applications executed on embedded systems require dynamicity and flexibility according to user and environment needs. Dynamically reconfigurable architecture could satisfy these requirements but needs efficient mechanisms to be managed efficiently. In this paper, we propose a dedicated application modeling technique that helps to establish a predictive scheduling approach to manage a dynamically reconfigurable architecture named OLLAF. OLLAF is designed to support an operating system that deals with complex embedded applications. This model will be used for a predictive scheduling based on an early estimation of our application dynamicity. A vision system of a mobile robot application has been used to validate the presented model and scheduling approach. We have demonstrated that with our modeling we can realize an efficient predictive scheduling on a robot vision application with a mean error of 6.5%.

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Mao, Yifan. "Research on Uncertainty and its Application in Financial Markets." Highlights in Business, Economics and Management 24 (January 22, 2024): 1127–32. http://dx.doi.org/10.54097/w30nsc97.

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This paper explores the theoretical frameworks and practical implications of uncertainty in economics and decision-making. And also delves into categorical uncertainty, including Economic Policy Uncertainty (EPU), Climate Policy Uncertainty (CPU), and Trade Policy Uncertainty (TPU). These forms of uncertainty can have profound effects on economic activities, market behaviour, and investment decisions. Furthermore, the paper discusses an innovative approach to measuring uncertainty using online big data, particularly Twitter-based uncertainty indices. These indices provide real-time insights into public sentiment and have been shown to impact stock market forecasts, although they are not without limitations, such as potential manipulation and data representativeness issues. The impact of uncertainty on financial markets is multifaceted, affecting different asset classes differently. High uncertainty can lead to increased volatility in stock markets, while oil and digital currency markets are also sensitive to geopolitical and economic uncertainties. The role of technology and algorithmic trading in uncertain markets is highlighted, with High-Frequency Trading (HFT) becoming increasingly prevalent. HFT can provide speed and efficiency but is a subject of debate regarding its impact on market stability. Future research trends may continue to focus on the intersection of technology and market uncertainty, seeking to better understand its implications and potential risks.

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He, Qin Shu, Shi Fu Xiao, and Xin En Liu. "Application of ANN and SVM for Uncertainty Quantification and Propagation." Advanced Materials Research 230-232 (May 2011): 192–96. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.192.

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All measurements have error that obscures the true value. The error creates uncertainty about the quality of the measured value, which is requiring testing and calibration laboratories to provide estimates of uncertainty with their measurements. Measurement uncertainties include input uncertainty, the propagation of input uncertainty, the output uncertainty and the systematic error uncertainty. Several methods for estimating the uncertainty of measurements have been introduced for different kinds of uncertainty quantification, and two data mining methodologies-Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used to build the unknown propagation model. This paper will discuss the quantification of measurement uncertainty (MU) and the separation of various uncertainty sources to MU and will discuss the advantages and limitations of SVM and ANN for building the propagation model of MU.

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Rahmat, Alyssa Melita, and Irwansyah Irwansyah. "PENGGUNAAN REDUKSI KETIDAKPASTIAN KETIKA MEMULAI HUBUNGAN DALAM APLIKASI ONLINE DATING DI INDONESIA." Linimasa : Jurnal Ilmu Komunikasi 4, no. 1 (2024): 31–43. http://dx.doi.org/10.23969/linimasa.v4i1.3378.

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Along with the development of technology and the ease of communicating, there is a significant rise of using online dating application not only in global scope but also in Indonesia. However, in this ease of communicating, there is a problem that arose and become a barrier in the development of an online relationship. Uncertainty of an information found in the online dating applications be the focus of this study. Using Uncertainty Reduction Theory, this study explains how uncertainty that was found in the use of online dating application can be reduced by using three strategies in seeking information that was developed by Charles Berger and Richard Calabrese. This study uses qualitative approach with the method of literature review which compiles various journals and theses that were obtained through Google Scholar in the range of year 2010 – 2020. The result of this study shows that Uncertainty Reduction Theory has a significant role in reducing the uncertainty of information or dating profiles that were found in the use of online dating application in Indonesia.

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Zhu, Yanwei, Aitor Atencia, Markus Dabernig, and Yong Wang. "Quantifying the analysis uncertainty for nowcasting application." Geoscientific Model Development 18, no. 5 (2025): 1545–59. https://doi.org/10.5194/gmd-18-1545-2025.

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Abstract. This study proposes a method to quantify uncertainty represented by errors in very-high-resolution near-surface analysis, specifically for weather nowcasting applications. Gaussian distributed perturbations are used to perturb the first guess and observation with a variance equal to that of the first-guess error. This error reflects the spatial characteristics of the difference between the first guess and observations and dominates the primary sources of analysis uncertainty. However, mapping perturbations to analyse the grid mesh through interpolation results in underdispersion, particularly in areas without stations. To address this issue, Gaussian perturbations are inflated with an inflation factor to amplify the dispersion. This method was applied to high-resolution analysis and nowcasting for hourly temperature, humidity, and wind components in the Beijing–Tianjin–Hebei region to assess its effectiveness in representing uncertainty. The generated ensemble analysis exhibits reasonable spread and high reliability, indicating accurate quantification of analysis uncertainty. Ensemble nowcasting is extrapolated from ensemble analysis to evaluate the transmission of perturbation during extrapolation. Verification results of ensemble nowcasting reflect the fact that the spread increases effectively during extrapolation up to a lead time of 6 h. However, the increase in the spread is highly dependent on the persistence of numerical weather prediction. The results demonstrate that generating appropriate perturbations based on analysis errors effectively represents the analysis uncertainty and contributes to estimating uncertainty in nowcasting.

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Szarvas, György, Veronika Vincze, Richárd Farkas, György Móra, and Iryna Gurevych. "Cross-Genre and Cross-Domain Detection of Semantic Uncertainty." Computational Linguistics 38, no. 2 (2012): 335–67. http://dx.doi.org/10.1162/coli_a_00098.

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Uncertainty is an important linguistic phenomenon that is relevant in various Natural Language Processing applications, in diverse genres from medical to community generated, newswire or scientific discourse, and domains from science to humanities. The semantic uncertainty of a proposition can be identified in most cases by using a finite dictionary (i.e., lexical cues) and the key steps of uncertainty detection in an application include the steps of locating the (genre- and domain-specific) lexical cues, disambiguating them, and linking them with the units of interest for the particular application (e.g., identified events in information extraction). In this study, we focus on the genre and domain differences of the context-dependent semantic uncertainty cue recognition task. We introduce a unified subcategorization of semantic uncertainty as different domain applications can apply different uncertainty categories. Based on this categorization, we normalized the annotation of three corpora and present results with a state-of-the-art uncertainty cue recognition model for four fine-grained categories of semantic uncertainty. Our results reveal the domain and genre dependence of the problem; nevertheless, we also show that even a distant source domain data set can contribute to the recognition and disambiguation of uncertainty cues, efficiently reducing the annotation costs needed to cover a new domain. Thus, the unified subcategorization and domain adaptation for training the models offer an efficient solution for cross-domain and cross-genre semantic uncertainty recognition.

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Schiering, Nadine, and Olaf Schnelle-Werner. "Uncertainty evaluation in industrial pressure measurement." Journal of Sensors and Sensor Systems 8, no. 2 (2019): 251–59. http://dx.doi.org/10.5194/jsss-8-251-2019.

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Abstract. In the process and manufacturing industry, pressure is one of the variables that most often need to be recorded and monitored. Whether in standard applications or under special operating conditions, each application requires pressure gauges that are perfect for their needs. In Germany, pressure gauges are calibrated by accredited calibration laboratories, according to guideline DKD-R 6-1 (DKD-R 6-1, 2014). This calibration guideline establishes minimum requirements for the calibration procedure and the measurement uncertainty calculation when calibrating pressure gauges. In addition to the uncertainty contributions due to the calibration, the uncertainty contributions due to the specific application, like extreme temperatures, high pressure in containers, extreme height differences, shocks, aggressive media or problematic physical product properties, should be taken into account. This paper presents the approach in which the measurement uncertainty can be calculated in industrial pressure measurements. Furthermore, the individual uncertainty contributions and their identification or origin are discussed. Finally, an example of a measurement uncertainty budget is shown as an important tool in the measurement uncertainty calculation.

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Wang, Jiajia, Hao Chen, Jing Ma, and Tong Zhang. "Research on application method of uncertainty quantification technology in equipment test identification." MATEC Web of Conferences 336 (2021): 02026. http://dx.doi.org/10.1051/matecconf/202133602026.

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This paper introduces the concepts of equipment test qualification and uncertainty quantification, and the analysis framework and process of equipment test uncertainty quantification. It analyzes the data uncertainty, model uncertainty and environmental uncertainty, and studies the corresponding uncertainty quantification theory to provide technical reference for the application of uncertainty quantification technology in the field of test identification.

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Morgan, D. J., and K. B. Kirkland. "Uncertainty in the Application of Contact Precautions." Clinical Infectious Diseases 55, no. 3 (2012): 474–75. http://dx.doi.org/10.1093/cid/cis398.

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Jo, Areum, Taksoo Kim, JungKwan Seo, Hyojung Yoon, Pilje Kim, and Kyunghee Choi. "Uncertainty Analysis and Application to Risk Assessment." Korean Journal of Environmental Health Sciences 41, no. 6 (2015): 425–37. http://dx.doi.org/10.5668/jehs.2015.41.6.425.

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Zimmermann, H. J. "An application-oriented view of modeling uncertainty." European Journal of Operational Research 122, no. 2 (2000): 190–98. http://dx.doi.org/10.1016/s0377-2217(99)00228-3.

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Bean, Wilna L., Johan W. Joubert, and M. K. Luhandjula. "Inventory management under uncertainty: A military application." Computers & Industrial Engineering 96 (June 2016): 96–107. http://dx.doi.org/10.1016/j.cie.2016.03.016.

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Datta, Arpana Rani, and Tirupati Bolisetti. "Application of variance decomposition approach in the uncertainty analysis of a hydrological model." Canadian Journal of Civil Engineering 40, no. 4 (2013): 373–81. http://dx.doi.org/10.1139/cjce-2012-0337.

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Decomposition of uncertainty into individual sources is important for understanding the risks of decisions made under the modelling uncertainty. This paper has applied the predictive uncertainty analysis and variance decomposition (VD) approach for quantifying hydrological modelling uncertainty. The VD analysis is used for quantifying the contribution of various sources of uncertainty to total modelling uncertainty. The goal is to increase the reliability of predictive uncertainty analysis by the inclusion of the results of VD analysis. The approach is evaluated by analyzing uncertainty of the Soil and Water Assessment Tool (SWAT) model for a watershed of Southwestern Ontario, Canada. Three uncertainty analyzing frameworks are employed for quantifying modelling uncertainty by both predictive uncertainty analysis and VD approach. The contribution of parameter uncertainty to total error variance is expressed by the percentage of total variance explained by parameter uncertainty and this contribution is quantified for three uncertainty analyzing frameworks. The contributions of other uncertainties excluding model parameters and precipitation uncertainties to total error variance are quantified by applying the VD approach. The results obtained from predictive uncertainty and VD analyses are observed to be consistent. The underlying hypotheses of each uncertainty framework are also verified for the reliability of the uncertainty analysis.

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Xu, Y., and A. E. Mynett. "Application of uncertainty and sensitivity analysis in river basin management." Water Science and Technology 53, no. 1 (2006): 41–49. http://dx.doi.org/10.2166/wst.2006.006.

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Considering uncertainty in the decision-making process in river basin management is important because uncertainty is regarded as one of the main obstacles to sound decision-making. In case of high uncertainty, the risks of making a wrong decision could be quite high, which may have severe consequences. This paper applies a screening sensitivity analysis method, the Morris method, to investigate the propagation of uncertainty from factors in a flood damage model into the model outputs and explores the importance of factors based on the sensitivity analysis. Uncertainty reduction in the most influential factors identified by the Morris method is proposed as a means to reduce the uncertainty in model outputs. In this way the risks of making a wrong decision could be reduced. The results in this paper show that the Morris method is an efficient approach to help reduce the uncertainty in model outputs.

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Atanov, Andrey, and Vasilii Rudyakov. "Application of the Concept of Uncertainty in Economic Theory." Bulletin of Baikal State University 34, no. 2 (2024): 299–306. https://doi.org/10.17150/2500-2759.2024.34(2).299-306.

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Mobility and volatility of the current macroeconomic environment, growth of the role of scientific and technological progress and associated risks, lack of consistency and dysfunctionality of international institutions against the backdrop of the collapse of globalization, chaotization of the international policy principles and many other factors receive increased attention from researchers to the problems of uncertainty, its causes and finding ways to reduce the negative effects of this uncertainty. Diversity and alternativeness of the future, that are typical for modern systems in the conditions of non-linear development, increasingly focus the attention of authors on non-equilibrium and unsustainable models in economic theory. At the same time, many works aimed at applying the concept of uncertainty in specific cases pay only slight attention to the category of «uncertainty» itself from the point of its scientific definition and development. Applying essential method the authors manage to explain two possible main contents of the nature of uncertainty — «fundamental» and «subjective» approach to uncertainty. Also the researchers expand the established classification in domestic literature adding uncertainty by degree (speed) of its level changing. This expansion is necessary to research in a more proper way issues connected with adaptive efficiency at macro level.

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Stefopoulos, Georgios, Stylianos Rigas, Panagiotis Tsirikoglou, and Anestis I. Kalfas. "Evaluation of pressure and species concentration measurement using uncertainty propagation." E3S Web of Conferences 345 (2022): 02008. http://dx.doi.org/10.1051/e3sconf/202234502008.

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This paper presents a probabilistic uncertainity evaluation method as described in the Guide to the Expression of Uncertainty in Measurements (GUM) and its application to probe measurements on pressure and fuel concentration. All sources of unceratinties are expressed as probability distributions. Consequently, the overall standard uncertainty of the quantity can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of rectangular (standard uncertainty) or gaussian (“expanded” uncertainty) distribution. A pitot-static probe and a fuel-concentration stem probe are used in order to demonstrate the principle of the probabilistic uncertainty evaluation method. The uncertainty induced by the pressure and concentration data acquisition system as well as the calibration of the fuel-concentration probe are included in the analysis. The overall “expanded” uncertainties for the measured and calculated values are presented as a function of different inlet fuel flows. In addition to this, the individual sources of uncertainty to the overall standard uncertainty are presented and discussed. Moreover, the transformation of standard uncertainty to “expanded” uncertainty will provide the deviation of the measurement in a 95% or 99% normal distributed interval instead of a 67% rectangular distributed interval.

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Moges, Edom, Yonas Demissie, Laurel Larsen, and Fuad Yassin. "Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis." Water 13, no. 1 (2020): 28. http://dx.doi.org/10.3390/w13010028.

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Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

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Li, Hui, Yanpeng Sun, Jingxiao Zhang, Die Liu, Zhengji Han, and Yu Li. "Critical Uncertainty Analysis of the Application of New-Generation Digital Technology in China’s Construction Industry—A Study Based on the LDA-DEMATEL-ISM Improvement Model." Applied Sciences 14, no. 1 (2023): 57. http://dx.doi.org/10.3390/app14010057.

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As the main driving force for the digital transformation of the construction industry, the uncertainty of digital technology in the application process has seriously hindered the high-quality development of the construction industry. In order to promote the wide application of digital technology in the construction industry and clarify the key uncertainties in its application process, this paper identifies the uncertainty index system of digital technology application based on the LDA topic model and literature analysis; the DEMATEL-ISM method is used to construct the multilevel hierarchical structure model of the uncertainty indicators in the application of digital technology to study the mutual influence among the indicators and to find the key uncertainty indicators. The research results show that the uncertainty indicators of the application of digital technology in the construction industry are divided into five levels: policy, industry, personnel, economy and law, and that the perfection of the policy guarantee system is the key uncertainty indicator for the investment return period of digital technology application. The standard contract model for digital technology is a direct uncertainty indicator for the application of digital technology in the construction industry. The results of this study help researchers and practitioners to focus on the key barriers and provide a list of key elements for construction companies to promote the application of next-generation digital technologies to improve digitalization of the construction industry. This study also provides a policy reference to further promote the digital transformation of the construction industry.

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PYLYPIUK, Tetiana, Viktor SHCHYRBA, and Rostyslav MOTSYK. "MATHEMATICAL METHODS APPLICATION IN SYSTEM ANALYSIS FOR DECISION-MAKING IN THE EDUCATIONAL PROCESS." Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 30 (December 18, 2024): 130–33. https://doi.org/10.32626/2307-4507.2024-30.130-133.

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Modern education is characterized by a high level of uncertainty and dynamism, which requires the educators ability to make informed decisions in limited information conditions. Mathematical methods of system analysis are applied in the research to optimize the decision-making process in the educational environment. One of the models in the educational process for the description and analysis of various aspects of the educational process, such as the motivation of the participants of the educational process, the quality of educational services, is considered. The search for optimal solutions in conditions of uncertainty for the processing of inaccurate and incomplete information, which is typical for the educational environment, was carried out. The algorithm of the decision-making process under uncertainty conditions is described. The problem of finding the optimal solution in conditions of uncertainty is considered using the example of one model of promoting an educational service with various modifications of the risk matrix. Optimal solutions were found using the Bayes criteria, the minimum variance of the estimated functional with its modifications, the maximizing the probability distribution of an estimated functional, the modal criterion, the minimum entropy, and the combined criterion. The results of calculations for decision-making according to various criteria in the conditions of uncertainty are obtained for various risk matrix modifications. Relevant conclusions were drawn. The study results can be used to optimize educational programs and teaching methods, improve the quality of educational services, increase the motivation of the educational process participants and more effectively use educational resources. The obtained results are useful for researchers and practitioners who are dealing with decisionmaking problems under uncertainty in various fields.

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Stephens, Tim, and Brian Bledsoe. "Simplified Uncertainty Bounding: An Approach for Estimating Flood Hazard Uncertainty." Water 14, no. 10 (2022): 1618. http://dx.doi.org/10.3390/w14101618.

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Deterministic flood hazard estimates neglect the inherent uncertainty associated with model estimates and can substantially underestimate flood risk. Monte Carlo simulation (MCS) has been a valuable tool for conducting uncertainty analysis. However, its application has primarily been limited to a single research setting. Recent development of a point approximation method, simplified uncertainty bounding (SUB), simulated the uncertainty from MCS with high accuracy (e.g., a critical success index of 0.75). However, an evaluation of additional flood hazard metrics and hydro-climate settings that impact the distribution of uncertainty is required. We evaluated SUB at two contrasting study sites by comparing their results with MCS and identified scenarios where performance increased and decreased. The SUB method accurately matched aerial inundation metrics, but performance was reduced for relative errors in flood depth and top width. Hydraulic structures had a heterogeneous impact on accuracy, and the confinement ratio had a positive relationship with the top width error. While SUB generally performed well with relative errors of approximately ±10% for a 90% confidence interval, some outliers did exist. The acceptability of the approach will depend on the specific application. Though SUB overestimated uncertainty, it provides a conservative estimate and is a cost-effective alternative to MCS.

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Jia, Zhifu, and Xinsheng Liu. "Complex uncertain differential equations with application to time integral." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 2275–89. http://dx.doi.org/10.3233/jifs-211030.

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In this paper, we propose complex uncertain differential equations (CUDEs) based on uncertainty theory. In order to describe the evolution of complex uncertain phenomenon related to belief degrees, we apply the complex Liu process to CUDEs. Firstly, we pose a concept of a linear CUDE and prove that homogeneous linear CUDE and general linear CUDE have solutions. Then, we prove existence and uniqueness theorem of a special CUDE. Further, we design a numerical algorithm to obtain inverse uncertainty distribution of the solution. Finally, as an application, we analyse the inverse uncertainty distributions of time integral of CUDEs and design numerical algorithms to obtain inverse uncertainty distributions of time integral.

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Qin, Miao, Yongchuan Tang, and Junhao Wen. "An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making." Entropy 22, no. 4 (2020): 487. http://dx.doi.org/10.3390/e22040487.

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Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory framework remain unresolved. The goal of this paper is to define a new belief entropy for measuring uncertainty of BPA with desirable properties. The new entropy can be helpful for uncertainty management in practical applications such as decision making. The proposed uncertainty measure has two components. The first component is an improved version of Dubois–Prade entropy, which aims to capture the non-specificity portion of uncertainty with a consideration of the element number in frame of discernment (FOD). The second component is adopted from Nguyen entropy, which captures conflict in BPA. We prove that the proposed entropy satisfies some desired properties proposed in the literature. In addition, the proposed entropy can be reduced to Shannon entropy if the BPA is a probability distribution. Numerical examples are presented to show the efficiency and superiority of the proposed measure as well as an application in decision making.

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Alomani, Ghadah, and Mohamed Kayid. "Further Properties of Tsallis Entropy and Its Application." Entropy 25, no. 2 (2023): 199. http://dx.doi.org/10.3390/e25020199.

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The entropy of Tsallis is a different measure of uncertainty for the Shannon entropy. The present work aims to study some additional properties of this measure and then initiate its connection with the usual stochastic order. Some other properties of the dynamical version of this measure are also investigated. It is well known that systems having greater lifetimes and small uncertainty are preferred systems and that the reliability of a system usually decreases as its uncertainty increases. Since Tsallis entropy measures uncertainty, the above remark leads us to study the Tsallis entropy of the lifetime of coherent systems and also the lifetime of mixed systems where the components have lifetimes which are independent and further, identically distributed (the iid case). Finally, we give some bounds on the Tsallis entropy of the systems and clarify their applicability.

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Abernethy, R. B., R. P. Benedict, and R. B. Dowdell. "ASME Measurement Uncertainty." Journal of Fluids Engineering 107, no. 2 (1985): 161–64. http://dx.doi.org/10.1115/1.3242450.

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The purpose of this paper is to introduce the new ASME measurement uncertainty methodology which is the basis for two new ASME/ANSI standards and the ASME short course of the same name. Some background and history that led to the selection of this methodology are discussed as well as its application in current SAE, ISA, JANNAF, NRC, USAF, NATO, and ISO Standards documents and short courses. This ASME methodology is rapidly becoming the national and international standard.

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Khodakarami, Vahid, Norman Fenton, and Martin Neil. "Project Scheduling: Improved Approach to Incorporate Uncertainty Using Bayesian Networks." Project Management Journal 38, no. 2 (2007): 39–49. http://dx.doi.org/10.1177/875697280703800205.

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Project scheduling inevitably involves uncertainty. The basic inputs (i.e., time, cost, and resources for each activity) are not deterministic and are affected by various sources of uncertainty. Moreover, there is a causal relationship between these uncertainty sources and project parameters; this causality is not modeled in current state-of-the-art project planning techniques (such as simulation techniques). This paper introduces an approach, using Bayesian network modeling, that addresses both uncertainty and causality in project scheduling. Bayesian networks have been widely used in a range of decision-support applications, but the application to project management is novel. The model presented empowers the traditional critical path method (CPM) to handle uncertainty and also provides explanatory analysis to elicit, represent, and manage different sources of uncertainty in project planning.

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Prakosa, Jalu Ahmad, Purwowibowo Purwowibowo, Edi Kurniawan, et al. "Experimental Design of Fast Terminal Sliding Mode Control for Valve Regulation under Water Load Uncertainty for Precision Irrigation." Actuators 12, no. 4 (2023): 155. http://dx.doi.org/10.3390/act12040155.

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The application of control systems in precision irrigation is critical to ensure the accurate distribution of water in crops under various uncertainties. Shifts in the loading of the water supply on the control valve can be a significant uncertainty. Changes in weather and the uncertainty of the water level in the reservoir are also challenging issues. Sliding Mode Control (SMC) is a robust control technique that is simple to apply to deal with uncertainty, while Fast Terminal Sliding Mode Control (FTSMC) has the benefit of the rapid convergence. The DC electric motor, which is a common component of electric control valves, can be employed in designing control techniques for precision irrigation applications. This study aims to design a proposed experimental-based method, namely FTSMC for valve regulation under water load uncertainty for precision irrigation application. Modification of the signum function should be used to eliminate the chattering effect in real experiments.The results of experiments showed that the proposed method was superior to the conventional Proportional Integral Derivative (PID) and traditional SMC techniques in terms of overshoot, convergence rate and error. Because of those reasons, the FTSMC approach should be implemented on control valves against load uncertainty in precision irrigation applications.

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Sanders, Mitchell S. "Uncertainty and Turnout." Political Analysis 9, no. 1 (2001): 45–57. http://dx.doi.org/10.1093/oxfordjournals.pan.a004864.

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This article develops a model that simultaneously considers individual turnout and vote choice while also accounting for uncertainty about candidates. The theoretical development of this model implies that the effects of uncertainty on turnout vary with the strength of individual preferences. Application of the model to individual choice in the 1996 American presidential election confirms that decreasing uncertainty about the character traits of the candidates decreases the probability of abstention for individuals with strong preferences but increases the probability of abstention for individuals with weak preferences.

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Kim, Eung Seok. "Analysis of Runoff According to Application of SWMM-LID Element Technology (II): Parameter Uncertainty Analysis." Journal of the Korean Society of Hazard Mitigation 20, no. 6 (2020): 445–50. http://dx.doi.org/10.9798/kosham.2020.20.6.445.

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This study quantitatively analyzed the degree of uncertainty associated with runoff based on the sensitivity analysis of runoff parameters using Low Impact Development (LID) element technology of study (I). Uncertainty was analyzed for parameter uncertainty, uncertainty of runoff, and uncertainty about the degree of parameter and runoff. Parameter uncertainty indices showed lower uncertainty indices as a whole and uncertainty indices of peak runoff were higher than that of total runoff in runoff uncertainty. The reason for this is that the LID element technology itself is intended to store low-frequency small-scale rainfall, so that the uncertainty index of peak rainfall seems to be highly uncertain. As a result of the analysis of uncertainty degree associated with runoff, it was found that the uncertainty of storage depth of bio retention cell and rain garden was low, while the heaviness parameters of rain barrel had the highest uncertainty index. In future experiments and research, it is necessary to modify the parameter range suitable for Korea, which will be helpful for urban development, reduction of nonpoint source pollution, and designing of low frequency rainfall storage facilities.

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Krbez, Joshua M., and Adnan Shaout. "Interval Type-2 Fuzzy Application for Diet Journaling." International Journal of Fuzzy System Applications 8, no. 2 (2019): 34–67. http://dx.doi.org/10.4018/ijfsa.2019040103.

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In this article, an improved system is constructed using interval type-2 fuzzy sets (IT2FS) and a fuzzy logic controller (FLC) with non-singleton inputs. The primary purpose is to better model nutritional input uncertainty which is propagated through the Type-2 FLC. To this end, methods are proposed to (1) model nutrient uncertainty in food items, (2) extend the nutritional information of a food item using an IT2FS representation for each nutrient incorporating the uncertainty in the extension process, (3) accumulate uncertainties for IT2FS inputs using fuzzy arithmetic, and (4) build IT2FS antecedents for FLC rules based on dietary reference intakes (DRIs). These methods are then used to implement a web application for diet journaling that includes a client-side Type-2 non-singleton Interval Type-2 FLC. The produced application is then compared with the previous work and shown to be more suitable. This is the first known work on diet journaling that attempts to model uncertainty for all anticipated measurement error.

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Liefeng Zhu, Liefeng Zhu, and Yongbiao Luo Liefeng Zhu. "Application of Bayesian Networks and Reinforcement Learning in Intelligent Control Systems in Uncertain Environments." 電腦學刊 35, no. 2 (2024): 001–16. http://dx.doi.org/10.53106/199115992024043502001.

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<p>Reinforcement learning is a machine learning paradigm that focuses on how an agent can perform actions in an environment to achieve a certain goal. The agent learns through interaction with the environment, observing the state and making decisions to maximize its reward. Reinforcement learning has wide applications in intelligent control systems. However, one limitation of reinforcement learning is the uncertainty in handling the environment model. Usually, reinforcement learning is performed without a clear model, which requires estimating environmental uncertainty and state transitions. Bayesian Networks are effective in modeling uncertainty, which can aid in establishing a probabilistic model of environmental dynamics. This allows for the integration of uncertainty information into the environmental model, leading to a more accurate understanding of the dynamic characteristics of the environment. In this study, we propose a reinforcement learning algorithm based on Bayesian Networks. We utilize optimal generalized residual differentiation, parallel integration causal directional reasoning, and other modeling techniques to address reinforcement learning tasks. The main idea is to utilize the prior distribution to estimate the uncertainty of unknown parameters. Then, the obtained observation information is used to calculate the posterior distribution in order to acquire knowledge. Experiments demonstrate that this approach is feasible in intelligent control systems operating in uncertain environments.</p> <p>&nbsp;</p>

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Reimer, Ron W., та Paula J. Reimer. "An Online Application for ΔR Calculation". Radiocarbon 59, № 5 (2016): 1623–27. http://dx.doi.org/10.1017/rdc.2016.117.

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AbstractA regional offset (ΔR) from the marine radiocarbon calibration curve is widely used in calibration software (e.g. CALIB, OxCal) but often is not calculated correctly. While relatively straightforward for known-age samples, such as mollusks from museum collections or annually banded corals, it is more difficult to calculate ΔR and the uncertainty in ΔR for 14C dates on paired marine and terrestrial samples. Previous researchers have often utilized classical intercept methods that do not account for the full calibrated probability distribution function (pdf). Recently, Soulet (2015) provided R code for calculating reservoir ages using the pdfs, but did not address ΔR and the uncertainty in ΔR. We have developed an online application for performing these calculations for known-age, paired marine and terrestrial 14C dates and U-Th dated corals. This article briefly discusses methods that have been used for calculating ΔR and the uncertainty and describes the online program deltar, which is available free of charge.

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Lee, Hyo-Sang, Min-Woo Jeon, Daniela Balin, and Michael Rode. "Application of Rainfall Runoff Model with Rainfall Uncertainty." Journal of Korea Water Resources Association 42, no. 10 (2009): 773–83. http://dx.doi.org/10.3741/jkwra.2009.42.10.773.

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Polužanski, Vladimir, Uros Kovacevic, Nebojsa Bacanin, Tarik A. Rashid, Sasa Stojanovic, and Bosko Nikolic. "Application of Machine Learning to Express Measurement Uncertainty." Applied Sciences 12, no. 17 (2022): 8581. http://dx.doi.org/10.3390/app12178581.

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The continuing increase in data processing power in modern devices and the availability of a vast amount of data via the internet and the internet of things (sensors, monitoring systems, financial records, health records, social media, etc.) enabled the accelerated development of machine learning techniques. However, the collected data can be inconsistent, incomplete, and noisy, leading to a decreased confidence in data analysis. The paper proposes a novel “judgmental” approach to evaluating the measurement uncertainty of the machine learning model that implements the dropout additive regression trees algorithm. The considered method uses the procedure for expressing the type B measurement uncertainty and the maximal value of the empirical absolute loss function of the model. It is related to the testing and monitoring of power equipment and determining partial discharge location by the non-iterative, all-acoustic method. The example uses the dataset representing the correlation of the mean distance of partial discharge and acoustic sensors and the temperature coefficient of the sensitivity of the non-iterative algorithm. The dropout additive regression trees algorithm achieved the best performance based on the highest coefficient of determination value. Most of the model’s predictions (>97%) fell into the proposed standard measurement uncertainty interval for both “seen” and “unseen” data.

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Hussein Al-Marshadi, Ali, Muhammad Aslam, and Alharbey Abdullah. "Uncertainty-Based Trimmed Coefficient of Variation with Application." Journal of Mathematics 2021 (October 4, 2021): 1–6. http://dx.doi.org/10.1155/2021/5511904.

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In this paper, the neutrosophic trimmed average, neutrosophic trimmed standard deviation, and neutrosophic trimmed coefficient of variation (NTCV) are introduced. The application of the proposed neutrosophic trimmed descriptive statistics is given with the help of measurement data. The comparisons of the proposed NTCV are compared with the existing neutrosophic coefficient of variation (NCV). From the comparisons, it is concluded that the proposed NTCV is more efficient than NCV in terms of consistency and measures of indeterminacy. Based on the study, it is recommended to apply the proposed NTCV in the industry when there is a need to make decisions on the basis of measurement data.

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Zhang, Yingzi. "Uncertainty in Illness: Theory Review, Application, and Extension." Oncology Nursing Forum 44, no. 6 (2017): 645–49. http://dx.doi.org/10.1188/17.onf.645-649.

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Beard, Randal W. "Decision Making Under Uncertainty: Theory and Application [Bookshelf]." IEEE Control Systems 38, no. 6 (2018): 114–15. http://dx.doi.org/10.1109/mcs.2018.2866656.

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Friedlob, George Thomas, and Lydia L. F. Schleifer. "Fuzzy logic: application for audit risk and uncertainty." Managerial Auditing Journal 14, no. 3 (1999): 127–37. http://dx.doi.org/10.1108/02686909910259103.

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Hossain, Sayed, Nik Hashim Nik Mustapha, and Lee Tak Chen. "A quadratic application in farm planning under uncertainty." International Journal of Social Economics 29, no. 4 (2002): 282–98. http://dx.doi.org/10.1108/03068290210419852.

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Shi, Boqiang, Yanhua Shen, Guoqing Yu, Guochen Duan, and Ruiyue Liu. "Application of evolution-based uncertainty design on gear." Mathematical Models in Engineering 2, no. 2 (2016): 143–50. http://dx.doi.org/10.21595/mme.2016.18012.

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Camac, Daniel, Raúl Bastidas, Ramón Nadira, Carlos Dortolina, and Hyde M. Merrill. "Transmission Planning Criteria and Their Application Under Uncertainty." IEEE Transactions on Power Systems 25, no. 4 (2010): 1996–2003. http://dx.doi.org/10.1109/tpwrs.2010.2049035.

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Hart, Andy, Graham C. Smith, Roy Macarthur, and Martin Rose. "Application of uncertainty analysis in assessing dietary exposure." Toxicology Letters 140-141 (April 2003): 437–42. http://dx.doi.org/10.1016/s0378-4274(03)00040-7.

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Traple, Marcus Augusto Lyrio, Alessandro Morais Saviano, Fabiane Lacerda Francisco, and Felipe Rebello Lourenço. "Measurement uncertainty in pharmaceutical analysis and its application." Journal of Pharmaceutical Analysis 4, no. 1 (2014): 1–5. http://dx.doi.org/10.1016/j.jpha.2013.11.001.

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Bobba, A. G., V. P. Singh, and L. Bengtsson. "Application of uncertainty analysis to groundwater pollution modeling." Environmental Geology 26, no. 2 (1995): 89–96. http://dx.doi.org/10.1007/bf00768321.

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Journal articles on the topic 'Application uncertainty'

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