Uncertainty Factors Research Articles

Illness uncertainty, resilience, and perceived social support among patients with moyamoya disease: a cross-sectional study.

The present study aims to investigate the levels of illness uncertainty in patients with moyamoya disease and to determine the association of socio-demographic characteristics, perceived social support and resilience with illness uncertainty in patients with moyamoya disease. A cross-sectional survey using convenience sampling was conducted in two hospitals in China from August to December 2023.A socio-demographic characteristics questionnaire, the Chinese versions of Mishel's Unsurety in Disease Scale (MUIS), the Chinese version of Connor-Davidson Resilience Scale (CD-RISC), and the Chinese version of Multidimensional Scale of Perceived Social Support (MSPSS) were used to perform this research. The collected data were analyzed using SPSS 24.0 statistical software. The t-test, one-way analysis of variance (ANOVA), pearson correlation analysis and hierarchical regression analysis were used to identify associated factors. A total of 263 patients with moyamoya disease were recruited in this survey. The score of illness uncertainty was at a moderate level of (100.03 ± 18.59). The present study identified a negative correlation between illness uncertainty with resilience perceived social support. Hierarchical regression analysis showed that gender, occupation, education level, resilience and perceived social support were the related factors of illness uncertainty. Patients with moyamoya disease experienced moderate disease uncertainty on average, which was related to gender, occupation, education level, resilience and perceived social support. Future research is needed to better explore the complex relationships between illness uncertainty, resilience, and perceived social support with different types of moyamoya disease using longitudinal research.

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

Computational modeling of near-fault earthquake-induced landslides considering stochastic ground motions and spatially varying soil

Landslides represent a large-deformation process influenced by various factors of uncertainty, such as types of ground motion (GM), randomness of GMs, and spatial variability of soils. The behavior of landslides is profoundly affected, making their assessment and measurement challenging. This paper proposes a computational approach for simulating the large-deformation process of landslides based on three-dimensional (3D) non-ordinary state-based peridynamics (NOSBPD). The analysis results of NOSBPD indicate that the mean runout distance triggered by pulse-like ground motions (PLGMs) is 16% greater than that induced by non-pulse ground motions (NPGMs), suggesting that PLGMs exhibit higher destructiveness. Besides, this paper establishes a runout distance assessment framework by considering the stochastic nature of PLGMs. This framework allows for the evaluation of the specific risk probability of landslides caused by stochastic PLGMs that match the target spectrum specified by codes. By introducing the theory of random fields and implementing a coupled procedure in peridynamics, we conducted a detailed analysis of the impact of spatial heterogeneity on the evolution process and consequences of landslides. Additionally, compared to two-dimensional (2D) analysis, the mean runout distance obtained from 3D analysis increased by 27.5%. This suggests that 2D analysis may underestimate the consequences of landslides. The findings of this study can serve as a scientific foundation for predicting the extent and scope of landslides triggered by near-fault earthquakes.

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Cooperative interference to achieve interval many-objective evolutionary algorithm for association privacy secure computing migration

In this paper, we study secure computing migration scenarios in uncertain environments with the presence of multiple malicious eavesdroppers (MEs). Specifically, when edge servers (ESs) execute tasks delivered by smart devices (SDs), SDs may move beyond the coverage of ESs, and computing migration (CM) of unfinished tasks is required to ensure service continuity. There is a risk of privacy leakage during task migration, and MEs use colluding eavesdropping to eavesdrop on the migrated tasks, and we consider eavesdropping on the associated tasks through data sharing among MEs to improve the eavesdropping efficiency. For eavesdropping in MEs, we achieve eavesdropping strikes using cooperative interference by jammers, which benefit by providing jamming services. In addition, uncertain computational scenarios directly affect the efficiency of task execution, and we consider the uncertainty factor in the malicious eavesdropping environment. To this end, this paper proposes the secure computational migration of associative privacy in uncertain environments (SCMAPUE) model, which transforms uncertainties into interval parameters, and optimizes the five objectives of migration delay, maximum completion time, energy consumption, load balancing and migration reliability to achieve efficient task execution and reliable migration. Aiming at the model characteristics, this paper designs an interval many-objective evolutionary algorithm for reliable migration (IMaOEA-RM), which employs a condition-based interval confidence strategy and a multi-access secure migration selection strategy to improve the convergence of the algorithm, and utilizes a dual-migration crossover strategy in order to adjust the jammer partners and improve the population diversity. Simulation results show that our proposed IMaOEA-RM algorithm can provide a more reliable and efficient migration scheme than existing algorithms.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Uncertainty quantification and data consistency analysis for the development of hydrogen and syngas oxidation model

The exponential growth of contemporary chemical kinetics data coupled with the potential inherent high uncertainty poses significant challenges for developing chemical kinetic models. To obtain a high-quality dataset for the optimization of H2 and syngas oxidation model, this study developed a framework for data consistency analysis and uncertainty analysis of chemical kinetic data. The framework combines simulations based on solving conservation equations, data-driven metaheuristic algorithms, and statistical analysis. Over 2000 experimental data points, including ignition delay times measured in shock tubes, laminar flame speeds, and concentration profiles measured in perfectly stirred reactors and plug flow reactors, were collected and used to construct the experimental dataset. Seventeen parameters of a detailed H2 and syngas oxidation model were selected to build the reaction rate constant (RRC) dataset. The initially estimated experimental uncertainties and RRC uncertainty factors were updated by slack variables in the iterative algorithm. Some high-uncertainty data were identified and excluded from the optimization dataset. The final experimental dataset achieves data consistency, and the feasible parameter set of RRC shows good prediction capability. The experimental data with high uncertainties were highlighted, and the uncertainty sources were analyzed.

Robust Design Based on Cost-Quality Model in Micro-Manufacturing

This paper proposes a novel total cost model for the micro‐products' entire life cycle that takes into account the uncertainty of the model parameters. The total cost includes pre-sale manufacturing and post-sale warranty costs. Additionally, different marketing strategies are also given based on the weight of internal and external costs. Furthermore, limited data and unknown effects in experiments may cause large errors in parameter estimates. This could prevent the achievement of reliable designs. To address this, robust optimization and interval estimation are used. This approach reduces the impact of uncertainty on parameter estimates. It ensures optimality and robustness in micro-manufacturing parameters. Example analysis and numerical simulation results show that the proposed method assists companies in selecting the optimal manufacturing parameter level that aligns with their marketing strategies. Besides, considering uncertainty factors can ensure that the optimization results remain guaranteed, even under the worst-case scenarios.

One crash, too many: Global uncertainty, sentiment factors and cryptocurrency market

Recent studies document that cryptocurrencies offer an alternative store of value, medium of exchange and can be used to hedge against currency and price fluctuations. However, the frequent collapse of the crypto-market undermines its safe-haven characteristics, as investors’ fear and anxiety could intensify market volatility and trigger a financial crisis. Motivated by the current global vicissitudes, this study examines the impact of uncertainty and sentiment factors on price behaviour of cryptocurrencies. To estimate our model, we used daily, low, high and closing price data for major crypto projects, from January 2018 to January 2023. We show that economic and political uncertainty factors significantly drive crypto prices. Furthermore, the interaction between sentiment dynamics as expressed by investors on different social platforms has a significant adverse effect on the returns of the cryptocurrency market, and the impact is more pronounced for tokens within the same ecosystem. Using the asymmetric GARCH-MIDAS model and TVP-VAR, we also demonstrate the existence of a significant contagion among tokens within the same ecosystem when bad (or good) news occurs. Considering the massive unprotected losses incurred by crypto investors during crises, our results provide important insights into how portfolio managers can effectively design investment strategies.

Scientific Committee guidance on appraising and integrating evidence from epidemiological studies for use in EFSA's scientific assessments.

EFSA requested its Scientific Committee to prepare a guidance document on appraising and integrating evidence from epidemiological studies for use in EFSA's scientific assessments. The guidance document provides an introduction to epidemiological studies and illustrates the typical biases, which may be present in different epidemiological study designs. It then describes key epidemiological concepts relevant for evidence appraisal. This includes brief explanations for measures of association, exposure assessment, statistical inference, systematic error and effect modification. The guidance then describes the concept of external validity and the principles of appraising epidemiological studies. The customisation of the study appraisal process is explained including tailoring of tools for assessing the risk of bias (RoB). Several examples of appraising experimental and observational studies using a RoB tool are annexed to the document to illustrate the application of the approach. The latter part of this guidance focuses on different steps of evidence integration, first within and then across different streams of evidence. With respect to risk characterisation, the guidance considers how evidence from human epidemiological studies can be used in dose-response modelling with several different options being presented. Finally, the guidance addresses the application of uncertainty factors in risk characterisation when using evidence from human epidemiological studies.

Testing the ionospheric model delay and uncertainty estimates with an uncombined navigation filter

In the last decade, new algorithmic positioning techniques have been developed for Global Navigation Satellite Systems (GNSS). These have brought a new focus on high accuracy applications which do not combine multiple frequencies to remove ionospheric errors (i.e. PPP-RTK, Fast-PPP). Not only do these algorithms focus on improvements in the position domain but also in acquiring the positioning solution as fast as possible. In this work, capabilities of different global ionospheric models are assessed, analyzing both the Ionospheric delay accuracy and the associated model uncertainty. Accurate model uncertainties are crucial for reducing the convergence time in uncombined filters, and to guarantee unbiased convergence in the first place. The assessment is done using an uncombined navigation filter with different ionospheric models: GPS ICA, IGS vTEC (vertical Total Electron Content) maps (IGSG, CODG and UQRG), two realizations of the ESA-UGI (Voxel and Multi-Layer), the Madrigal TEC, and the Spire Global vTEC maps. To quantify the model uncertainties without the use of a reference ionospheric model, global maps of an uncertainty inflation factor are computed to show the inflation required to produce optimal filter convergence. These maps demonstrate that some models are too optimistic in the reporting of their own uncertainty estimates, requiring an uncertainty factor up to 10 times the quoted value.

Supply chain model having stochastic lead time demand with variable production rate and demand dependent on price and advertisement

In this present study, a single-manufacturer and single-retailer supply chain management model are formulated for a single product. This study specifically looks at a supply chain with variable production rate, stochastic lead time demand, and price- and advertisement-dependent demand. By incorporating these complex aspects into a model, which enables to examine their combined effects on supply chain performance, this study adds to the body of knowledge. The study reveals unique insights into the complex interplay between pricing tactics, advertising efforts, production dynamics, and the variability brought on by stochastic lead times through meticulous study and modelling. Finally, the total system profit is calculated and optimized with all the decision variables. A classical approach is performed to obtain the optimized solution of the joint profit function along with the decision variables. Two models are discussed in the study: (1) the model with normally distributed lead time demand and (2) the model with distribution-free lead time demand. The joint profit of the supply chain is found to be lesser by 1% for the normally distributed lead time demand than the distribution free pattern. The comparison of the shipment policies and the safety factors for the different distribution patterns of the lead time demand are shown. Though the huge increment in the safety factor for unknown leadtime demand distribution may help in reducing the uncertainity factor and disruptions in the supply chain, but also it may unnecessarily tie up more capital which can be invested in other sectors of the supply chain.

Update of the scientific opinion on tetrabromobisphenol A (TBBPA) and its derivatives in food.

The European Commission asked EFSA to update its 2011 risk assessment on tetrabromobisphenol A (TBBPA) and five derivatives in food. Neurotoxicity and carcinogenicity were considered as the critical effects of TBBPA in rodent studies. The available evidence indicates that the carcinogenicity of TBBPA occurs via non-genotoxic mechanisms. Taking into account the new data, the CONTAM Panel considered it appropriate to set a tolerable daily intake (TDI). Based on decreased interest in social interaction in male mice, a lowest observed adverse effect level (LOAEL) of 0.2 mg/kg body weight (bw) per day was identified and selected as the reference point for the risk characterisation. Applying the default uncertainty factor of 100 for inter- and intraspecies variability, and a factor of 3 to extrapolate from the LOAEL to NOAEL, a TDI for TBBPA of 0.7 μg/kg bw per day was established. Around 2100 analytical results for TBBPA in food were used to estimate dietary exposure for the European population. The most important contributors to the chronic dietary LB exposure to TBBPA were fish and seafood, meat and meat products and milk and dairy products. The exposure estimates to TBBPA were all below the TDI, including those estimated for breastfed and formula-fed infants. Accounting for the uncertainties affecting the assessment, the CONTAM Panel concluded with 90%-95% certainty that the current dietary exposure to TBBPA does not raise a health concern for any of the population groups considered. There were insufficient data on the toxicity of any of the TBBPA derivatives to derive reference points, or to allow a comparison with TBBPA that would support assignment to an assessment group for the purposes of combined risk assessment.

Refinement of the train formation plan calculation model by considering the effect of switchyard stations' workload

The article is dedicated to improving the mathematical model for calculating the freight train formation plan (TFP) on the railway network of JSC «Ukrzaliznytsia». The system of organizing freight flows in rail transport is based on strategic planning through TFP, which allows increasing the efficiency of using cars and infrastructure, reducing delays in forming trains, and reducing their transportation time. At the same time, the existing TFP calculation models have certain shortcomings, in particular, the complexity of taking into account the numerous factors that influence the process of train formation. This study proposes a refinement of the TFP mathematical model by including the dependence of wagon-hour costs on the function of the variable utilization factor of technical stations. An automated calculation method developed by one of the authors, based on the application of the mathematical apparatus of genetic algorithms, was used for the calculations. A comparative analysis of the calculation results using the standard and improved models showed that taking into account the impact of station congestion allowed reducing costs by 405.7 wagon-hours, or 3.5% of the total costs for train accumulation and reformatting. The application of the proposed refinement of the TFP model allows the railway operator to obtain more accurate and justified planning results, which will contribute to improving the efficiency of wagon flow management. In addition, the implementation of this approach can serve as the basis for further improving the methods of strategic planning of the transportation process and minimizing the impact of uncertainty factors. The proposed refinement of the TFP calculation model has not only a practical focus, but also represents a theoretical example of the application of an approach aimed at reducing the level of epistemic uncertainty in the management system of the operational work of rail transport. The systematic development and implementation of such approaches creates the basis for mitigating the negative impact of factors that represent sources of other types of uncertainty

Liquidity Connectedness Among Major Financial Asset Classes: Do Uncertainty Factors Matter?

Liquidity Connectedness Among Major Financial Asset Classes: Do Uncertainty Factors Matter?

Family determination of social phobia in students

Introduction. Numerous scientific studies show a tendency for the spread of social phobia among students, which is explained by factors of uncertainty, global risk, a sharp change in the conditions of higher education, as well as, mainly, the personal characteristics of students. However, family determination plays an important role in its formation, which has not been sufficiently studied. Attached to their parents, but feeling insignificant in the family, boys and girls are not sufficiently prepared to accept new challenges outside the family - their social anxiety can increase to the level of social phobia. The purpose of the study was to identify the features of the family determination of social phobia in students, determined by their proximity to their parents and importance in the parental family.Materials and Methods. The sample was made up of students of the II and III courses of Moscow universities (125 boys and 125 girls) who had not previously been married at the time of the survey. The methods of socio-psychological diagnostics were used: "Family sociogram" by E. G. Eidemiller and O. V. Cheremisin; questionnaire of social anxiety and social phobia by O. A. Sagalakova and D. V. Truevtsev (STiSF). In the course of the study, group differentiation was carried out: according to the level of social anxiety, 2 groups were distinguished (students with and without social phobia); according to family status, 4 groups (close to the father, close to the mother, equally close to both parents, "alienated" from the parental family). Empirical data were subjected to mathematical and statistical processing based on the MS Office Excel package, IBM SPSS Statistics 23. Intergroup differences were determined by the Student's t-criterion; correlation analysis was performed by Pearson's r-criterion.Results. In the combined sample, about 29% of students were found with low indicators of the general level of social phobia, about 27% with elevated indicators and about 44% with a high general level of STIS. The overall level indicator was 47.8 points, which corresponds to an increased level of social anxiety with a tendency to high. In the sample of students with social phobia, a smaller percentage of those close to their father and a large percentage close to their mother, as well as equally close to both parents, were found compared with the sample of students without social phobia. Sociophobic students have a significantly lower status in the parental family, but no intergroup differences in indicators of proximity to their father or mother were found (in both groups there are many students who are close to each parent and alienated from them). The absence of close links between indicators of students' closeness to parents, their importance in the parental family and indicators of social phobia in the combined sample and the sample of students with social phobia was noted, however, their significant correlations were revealed in the sample of students without social phobia.Discussion and Conclusions. The position of students with social phobia in the parental family is high in proximity to both parents, but low in importance in the family, whereas the position of students with a low level of STIS is characterized by a moderate degree of importance in the family and a low equal degree of proximity to both parents. The occurrence of social phobia is less likely in students who have an equal low degree of closeness to both parents and moderate (within 6-7 points) with a tendency to a high degree of importance in the family.

Thermal error prediction and reliability analysis of the main shaft bearing at wind turbines

During the operation of the gearless wind turbine, the phenomenon of heat generation in the main shaft bearing is inevitable and further affects the assembly preload. It is crucial to determine the effect of the thermal error on bearing assembly preload. In this paper, a reliability analysis method for main shaft bearing is proposed. Firstly, a finite element model for the thermal analysis of wind turbines is established based on heat transfer theory, and the thermal error of the preload is calculated. Subsequently, a reliability analysis of the main shaft bearing is conducted through Quasi-Monte Carlo simulation (QMCS) considering the influence of uncertainty factors. To further improve the computational efficiency, a surrogate model based on stochastic configuration network (SCN) is established to analyze the reliability and sensitivity of the bearing. Finally, the numerical example shows that the proposed model has high accuracy and applicability.

The optimization model of the location and number of Warehouses in subsidized fertilizer distribution systems with uncertainty factors

The optimization model of the location and number of Warehouses in subsidized fertilizer distribution systems with uncertainty factors

Uncertain random optimal control model for deteriorating inventory with the finite horizon

This paper examines a deteriorating inventory model with combined pricing and production during a limited selling time in an uncertain random setting. Demand is influenced by market size and price, which are portrayed in terms of random variables. The uncertainty factors affecting the inventory are represented by multiple Liu processes. Inventories deteriorate by a certain percentage and back-orders for inventories are permitted. This study aims to determine the optimal level of production rate and pricing that maximizes the producer’s total expected profit through the application of uncertain random optimal control theory, which is based on uncertain theory and probability theory. Numerical examples are used to compare joint pricing dynamic production model with dynamic production model under an optimal static price, highlighting the benefits of implementing joint pricing dynamic production model. Furthermore, it is evident that an increase in deterioration rate results in decreased on-hand inventory, yet higher production rate and price. It should also be noted that different levels of probability expectation of demand can have an impact on production inventory problem.

Enhanced Rock Slope Stability Analysis: Integrating the Partial Factor Method into the Limit Equilibrium Method

Rock slope stability is crucial for sustainable design. Especially concerning natural or artificial rock-cut slopes. The stability of these slopes depends largely on features of rock mass, particularly discontinuities. Failure modes are determined by these features and are evaluated using kinematics analysis with stereographic projections. Various methods exist for analyzing rock slopes, including the limit equilibrium method (LEM), which assesses stability based on a factor of safety (FS). Conversely, the partial factor method (PFM), predominantly used in Europe, offers a more reliable and probabilistic approach, incorporating uncertainty factors. Although Eurocode, which employs the PFM, is widely utilized, it faces disputes and undergoes updates based on ISRM recommendations. The partial factor method is considered more conservative than the limit equilibrium method due to its comprehensive probabilistic approach. The choice between methods depends on project requirements, data availability, and expertise. This study compares the limit equilibrium and partial factor methods for rock slope analysis, concluding that the partial factor method is more conservative and sustainable for long-term stability assessment. Whereas, the traditional method is often used for short-term assessments.