Consideration Of Uncertainties Research Articles

An integrated uncertainty analysis method for the risk assessment of hydrogen refueling stations

In order to assess more rigorously the risks of hydrogen refueling stations (HRSs) with consideration of uncertainties, an uncertainty analysis method integrating the Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+), Monte Carlo simulation, and Latin Hypercube Sampling (LHS) method is proposed in this paper. In order to discuss and demonstrate the potential ability of the proposed method, the leak risk of a 35 MPa dispenser at a HRS is assessed. The results indicate that the proposed method can effectively quantify the leak risk uncertainty of the dispenser at a HRS. These uncertainties considered have significant effects on the leak risks of the dispenser, and the risk contribution of jet fire is much larger than that from explosion. Moreover, the leak risk of the dispenser is underestimated by the HyRAM+, so it should be used by combining a reliable safety factor. Current work can provide a more accurate method for the risk assessment and performance-based design of HRSs.

A cost-effectiveness analysis of fluoride varnish application in preventing Root caries in elderly persons: a Markov simulation study

BackgroundRoot caries are prevalent issues that affect dental health, particularly among elderly individuals with exposed root surfaces. Fluoride therapy has shown effectiveness in preventing root caries, but limited studies have addressed its cost-effectiveness in elderly persons population. This study aimed to evaluate the cost-effectiveness of a fluoride treatment program for preventing root caries in elderly persons within the context of Chinese public healthcare.MethodsA Markov simulation model was adopted for the cost-effectiveness analysis in a hypothetical scenario from a healthcare system perspective. A 60-year-old subject with 23 teeth was simulated for 20 years. A 5% sodium fluoride varnish treatment was compared with no preventive intervention in terms of effectiveness and cost. Tooth years free of root caries were set as the effect. Transition probabilities were estimated from the data of a community-based cohort and published studies, and costs were based on documents published by the government. The incremental cost-effectiveness ratio (ICER) was calculated to evaluate cost-effectiveness. Univariate and probabilistic sensitivity analyses were performed to evaluate the influence of data uncertainty.ResultsFluoride treatment was more effective (with a difference of 10.20 root caries-free tooth years) but also more costly (with a difference of ¥1636.22). The ICER was ¥160.35 per root caries-free tooth year gained. One-way sensitivity analysis showed that the risk ratio of root caries in the fluoride treatment group influenced the result most. In the probabilistic sensitivity analysis, fluoride treatment was cost-effective in 70.5% of the simulated cases.ConclusionsRegular 5% sodium fluoride varnish application was cost-effective for preventing root caries in the elderly persons in most scenarios with the consideration of data uncertainty, but to a limited extent. Improved public dental health awareness may reduce the incremental cost and make the intervention more cost-effective. Overall, the study shed light on the economic viability and impact of such preventive interventions, providing a scientific basis for dental care policies and healthcare resource allocation.

Hierarchical-stochastic model predictive control for a grid-interactive multi-zone residential building with distributed energy resources

Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms; (2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost; (3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs; and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

Reliability-Based Design Optimization of the PEMFC Flow Field with Consideration of Statistical Uncertainty of Design Variables

Recently, with the fourth industrial revolution, the research cases that search for optimal design points based on neural networks or machine learning have rapidly increased. In addition, research on optimization is continuously reported in the field of fuel cell research using hydrogen as fuel. However, in the case of optimization research, it often requires a large amount of training data, which means that it is more suitable for numerical research such as CFD simulation rather than time-consuming research such as actual experiments. As is well known, the design range of fuel cell flow channels is extremely small, ranging from hundreds of microns to several millimeters, which means the small tolerance could cause fatal performance loss. In this study, the general optimization study was further improved in terms of reliability by considering stochastic tolerances that may occur in actual industry. The optimization problem was defined to maximize stack power, which is employed as objective function, under the constraints such as pressure drop and current density standard deviation; the performance of the optimal point through general optimization was about 3.252 kW/L. In the reliability-based optimization problem, the boundary condition for tolerance was set to 0.1 mm and tolerance was assumed to occur along a normal distribution. The optimal point to secure 99% reliability for the given constraints was 2.918 kW/L, showing significantly lower performance than the general optimal point.

Fault diagnosis of virtually‐coupled trains by adaptive observer with pattern‐matched detection and reinforced identification

SummaryVirtual coupling is gaining in popularity as a promising development direction to maximize the rail‐line efficiency by minimizing the headway distance among trains in the presence of potentially encountered traction engines' faults with unknown amplitude, happening time and probability, which would be huge threat to the safety of trains without proper sensing and handling. This paper considers the fault diagnosis problem for virtually‐coupled multiple trains using adaptive observer design. In order to generate false‐free and timely fault alarming and relieving signals, an adaptive threshold function design is firstly given using a novel pattern‐matched gain technique with explicit consideration of model uncertainty. Then, reinforced regressor‐based fault identification algorithm is proposed to generate precise estimation of unknown fault values, activated and powered‐off by the fault alarming and relieving signals output by fault detection observer, with globally Lipschitz property and fast convergence performance. Finally, comparative and simulation results are given to demonstrate the effectiveness and advantages of proposed fault diagnosis algorithms.

A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models

ABSTRACT This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties.

Investigating the dynamic and contingent effects of the US import air freight market throughout the phases of the COVID‐19 disruption

AbstractAir freight is a fast and reliable mode of transportation in global supply chains. Because of its speed and reliability, air freight has been argued to help firms respond to supply chain disruptions. Yet, the literature lacks a quantitative analysis that examines the dynamics of air freight demand throughout the phases of a disruption as well the moderating factors that influence air freight demand throughout these phases. Drawing on the microanalytic view of property rights theory applied to the dynamics of air freight demand during the COVID‐19 disruption, the study hypothesizes that importers adjust air freight demand based on considerations of demand and lead time uncertainty and that these dynamics will be contingent on heterogeneous product characteristics. Specifically, the study hypothesizes that air freight demand will increase more for products used as intermediate inputs, and that products, regardless of their value, will have higher air freight demand during the large surge in demand after reopening from the initial COVID‐19 lockdowns. The hypotheses are tested using archival import data and other publicly available sources with panel regression models that include product and quarter fixed effects. The study finds support for these hypotheses.

Study on the benefit analysis based on whole life cycle carbon emission calculation after the construction of photovoltaic systems in macau's construction waste landfills

This study seeks to assess both environmental and economic effects associated with installing photovoltaic systems within construction waste landfills in Macau by employing an effective carbon emissions calculation methodology and benefit analysis method. Beginning by outlining characteristics and challenges associated with construction waste landfills, as well as photovoltaic systems used for this application in this paper. Here, we present a detailed outline of our methodology design, outlining its principles of life cycle analysis, data collection processes and the creation of carbon emissions calculation models. Subsequently, we examine photovoltaic systems within Macau's construction waste landfills by studying system design, component selection and operational strategies as well as carbon emission data collection during their operational time period. Under life cycle carbon emissions calculations, we assess the carbon emissions generated from photovoltaic systems as well as conduct an environmental and economic benefit analysis for carbon reduction benefit analysis purposes. This research incorporates sensitivity analysis and uncertainty consideration in order to conduct an extensive benefit analysis. The research results offer strong support for sustainable photovoltaic systems within Macau waste landfills as well as insights to inform planning and policy formation for similar future projects.

Incorporating local uncertainty management into distribution system planning: An adaptive robust optimization approach

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level.

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

AbstractThe combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

Analyzing the relationship between natural resource management, environmental protection, and agricultural economics for sustainable development in China

Sustainable development is crucial for achieving a balance between economic growth, social well-being, and environmental preservation. This study aims to evaluate the dimensions of natural resource management, environmental protection, and agricultural economics for promoting sustainable development in China. In this regard, the fuzzy Analytical Hierarchy Process (AHP) and fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) approaches are used for the analysis. The hybrid approach combines the benefits of fuzzy logic and multi-criteria decision-making (MCDM) techniques, allowing for the consideration of uncertainties and imprecise data in decision-making. The study incorporates six criteria, twenty-four sub-criteria, and five strategies in a comprehensive evaluation framework. According to the results obtained using the fuzzy AHP method, it is evident that the primary criteria influencing sustainable development in China are economic viability, policy and governance, and environmental impact. Within these criteria, specific sub-criteria such as cost-effectiveness, policy alignment, air quality, and social inclusion emerge as key areas of focus. Furthermore, the results of the fuzzy VIKOR method show that investing in research and innovation, strengthening environmental regulations, and strengthening climate change adaptation are the most suited strategies to guide sustainable development efforts. To ensure sustainable development in China, policymakers need to give importance to implementing strategies stressing research and innovation, strengthened environmental regulations, and enhanced climate change adaptation.

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system.

Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training samples with L2 loss in current training pipeline. However, little attention has been paid to the uncertainty property which is crucial to the prediction task. We argue that the recorded motion in training data could be an observation of possible future, rather than a predetermined result. In addition, existing works calculate the predicted error on each future frame equally during training, while recent work indicated that different frames could play different roles. In this work, a novel computationally efficient encoder-decoder model with uncertainty consideration is proposed, which could learn proper characteristics for future frames by a dynamic function. Experimental results on benchmark datasets demonstrate that our uncertainty consideration approach has obvious advantages both in quantity and quality. Moreover, the proposed method could produce motion sequences with much better quality that avoids the intractable shaking artefacts. We believe our work could provide a novel perspective to consider the uncertainty quality for the general motion prediction task and encourage the studies in this field. The code will be available in https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB.

Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty

ABSTRACT One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs.

A Benefit-Risk Conceptual Framework for Biologic Use During Pregnancy: A Mini-Review.

Recent reports related to in utero exposure of marketed immunosuppressive biologics led to clinical recommendations to delay live vaccinations for infants due to the concern of reduced vaccine effectiveness and/or increased risk of vaccine-related disease. These delays can increase the risk of children contracting vaccine preventable diseases, yet the alternative cessation of biologics during pregnancy may result in increased autoimmune disease activity for the pregnant person, raising complex benefit-risk (B-R) considerations and trade-offs. Our goal is to develop a conceptual framework for B-R assessment based on the key benefits and risks pregnant people would consider for themselves and their children when continuing (vs. discontinuing) a biologic during pregnancy. The proposed framework defines the decision contexts, key domains and attributes for potential benefits, and risks of biologic use during pregnancy, informed by a literature review of indications for biologics and refined with key clinical stakeholders. The framework includes both the pregnant person taking the biologic and the infant potentially exposed to the biologic in utero, with potential benefit and risk domains and attributes for each participant. To advance this conceptual framework, there are considerations of potential biases and uncertainty of available data that will be imperative to address when quantifying the B-R framework. For these reasons, we recommend the formation of a consortium to ensure development of a robust, validated framework that can be adopted in the healthcare setting.

Use of fuzzy logic for analyzing the socio-economic well-being of the population

The article reveals a new approach to the analysis of population welfare using the theory of fuzzy sets. The purpose of the study is to develop and apply a methodology based on fuzzy logic to quantify and analyze the social and economic well-being of citizens. The main task includes the development of a methodological approach, the construction of a model for assessing well-being and the development of recommendations for planning socio-economic strategies. As a solution, the use of fuzzy logic is proposed, which allows better consideration of ambiguities and uncertainties in the data on the well-being of the population. The study presents a detailed description of the developed model, which includes the formation of lin-guistic variables, the definition of term sets, and the development of inference rules. These components allow the model to adapt to different conditions and types of data, providing flexibility and a wide field of application of the method. The study also emphasizes the impor-tance of analyzing factors that affect the well-being of the population, such as household in-come, expenditure on medicine and food, education level, housing conditions, the importance of personal economy and others. The authors indicate the dynamic influence of these factors and the need for their regular updating to increase the accuracy of forecasts. The results of the study demonstrate that the developed model provides more accurate and reasonable fore-casts of the level of socio-economic well-being compared to traditional methods. The ob-tained data can be used to plan socio-economic strategies at the micro and macro levels, con-tributing to the development of effective measures to improve the well-being of the population. The article makes a significant contribution to the development of socio-economic welfare analysis methods, showing the potential of fuzzy logic as a tool for increasing the accuracy of forecasting and developing sound socio-economic policies.

An XGBoost-assisted evolutionary algorithm for expensive multiobjective optimization problems

Many expensive optimization problems exist in various real-world applications. However traditional evolutionary algorithms are inadequate for solving these problems directly. Surrogate-assisted evolutionary algorithm (SAEA) can effectively solve expensive optimization problems using computationally inexpensive surrogate models. However, both the Kriging and ensemble models most SAEAs adopted have limited uncertainty of prediction, especially for expensive multiobjective optimization problems (EMOPs). To enhance the optimization performance of SAEA for EMOPs, this paper proposes a new XGBoost-assisted evolutionary algorithm, calling XGBEA. Specifically, XGBoost is used as the surrogate model, and a neighborhood density selection strategy based on a mixed population and archive space (NDS-MPA) is proposed to measure the uncertainties of individuals. XGBoost helps to best fit objective functions with different fitness landscapes. NDS-MPA selects non-dominated individuals with minimal density for re-evaluation, incorporating considerations of convergence, diversity and uncertainty. Experimental results on two well-studied benchmarks demonstrated the superiority of XGBEA over seven state-of-the-art SAEAs.

Consignment contract in a supply chain with price, demand and yield randomness: A distributionally robust approach

We investigate a supply chain consisting of a supplier and an e-commence platform who sign a consignment contract with uncertainty in price, demand, and yield. Due to the challenges of actually determining the joint distribution of random variables, we assume that only the partial distributional information is available for these three uncertainties. To address the issue of distributional ambiguity and to more accurately account for arbitrary correlations among these uncertainties, we introduce a moment-based distributionally robust optimization (DRO) approach that assumes only the first and second moments of these three uncertainties are known. Given an exogenous consignment rate, we derive the closed-form expression for the supplier’s optimal production decision and her worst-case expected profit. We study the impact of random price and random yield within the ambiguity set on the supplier’s production decision and the worst-case expected profit. From the e-commerce platform’s perspective, we further examine the contract design problem when the consignment rate is endogenous and analyze equilibrium solutions for both contracting parties. Our results show that the additional consideration of price or yield uncertainty within the ambiguity set does not always mitigate or enhance the degree of conservatism of the contracting parties, which is closely related to the correlation between any two types of uncertainty, as well as the degree of uncertainty itself.

Optimal control of multilateral waterflooding wells in carbonate reservoirs with uncertainty consideration

The benefits of adjoint formulation in studying and improving the economic and production viability of reservoir waterflooding problems has been reported in several researches. However, there is little or no recorded report on its use for multilateral wells in uncertain waterflood reservoir conditions. This study aims to investigate the effect of geological uncertainty to multilateral well performance using adjoint formulation in oil reservoir waterflooding. First, an oil reservoir model with uncertainty assumption in permeability, Corey value, and physical dimension in reservoir compartmentalization was developed. Second, an optimal control sequence for the multilateral injection well was developed to optimize the net present value (NPV). The study revealed that with the obtained control sequence, reservoir compartmentalization notably faults in rock structure, significantly influenced the production capacity of multilateral wells which resulted in a higher NPV and oil rate when compared to other uncertainty cases.

Robust optimal operation scheduling method for integrated energy system considering flexible energy storage and mixed hydrogen natural gas

ABSTRACT At present, the number of electric vehicles is increasing day by day. The full integration of electric vehicles and multi-energy coupled integrated energy systems is the future development trend. This paper introduces a novel form of flexible energy storage in the integrated energy system ;(IES): electric vehicle (EV). And It proposes that mixed natural gas hydrogen be used as the primary energy source for IES, which replaces traditional natural gas. Combined with the above, the IES is scheduled to meet the system’s multi-load for heating, cooling, and electricity demands. Based on the integrated energy system’s structure and energy conversion principles, a robust two-stage optimization model for the IES is proposed to minimize the daily operation scheduling costs. The proposed model considers the uncertainty of distributed renewable generation and flexible energy storage. Uncertain adjustment parameters are introduced into the model, which can flexibly adjust the conservatism of the scheme. Using the column and constraint generation algorithm (C&CG) and the strong duality theory, the model presented in this paper is solved and the optimal scheduling scheme is obtained. The final simulation results validate the effectiveness of the model and algorithm. The addition of flexible energy storage and mixed natural gas hydrogen effectively improves the economy and flexibility of the system while reducing carbon emissions costs. Consideration of uncertainty improves system robustness and safety. In addition, sensitivity analyses of the influence of the proportion of hydrogen mixed with natural gas, the setting of uncertain adjustment parameters, electricity prices, weather, and load fluctuations on system operation. The results show that compared to existing research, electric vehicles as a flexible energy storage device and mixed natural gas hydrogen supply, increase the economic benefits of IES by 19.8% and 23%, respectively.