Uncertainty Variable Research Articles

Intolerance towards Uncertainty and Self-Adjustment of Women Working in Essential and Critical Sectors

Women who work in essential and critical sectors experience increased stress and anxiety during the Covid-19 pandemic. The results of previous research explain that intolerance to uncertainty and adjustment are related to psychological disorders such as stress, conflict, tension, frustration, depression, worry, and anxiety disorders. This research aims to determine the relationship between intolerance of uncertainty and the adjustment of women working in essential and critical sectors. The instruments used were the Personal Adjustment Scale and The Intolerance of Uncertainty Scale-Revised Italian Version (IUS-R Italian). The respondents in this study were 473 female workers in essential and critical sectors who worked in Jakarta, Bogor, Depok, Tangerang, and Bekasi. The research results show that place of work, type of work, and marital status can influence the level of intolerance of uncertainty in working women. The results of the correlation analysis show that the intolerance of uncertainty variable is correlated with the self-adjustment of working women. The results explain that the intolerance of uncertainty contributes to 10.4% of the self-adjustment variable. These results also explain that the self-adjustment variable contributes 10.4% to the intolerance of uncertainty variable.

Neural Output Feedback Control of Automobile Steer-by-Wire System With Predefined Performance and Composite Learning

This article addresses the steering control problem for steer-by-wire (SbW) systems subject to the unknown uncertainty, external disturbance and unavailable variable. Before the controller design, an adaptive neural network-based observer and a disturbance observer are constructed to estimate the angular velocity signal and the compound disturbance, respectively. Then, to guarantee the transient and steady-state performance of steering tracking error within the quantitative boundary, a prescribed performance function is constructed by user-designed tracking accuracy and settling time. Finally, the controller is designed based on the backstepping scheme and the neural network with a composite learning scheme is proposed for the approximation of lumped uncertainty. The Lyapunov stability theory shows that the signals involved in the system are semiglobal uniformly ultimately bounded and the tracking error converges to a preset range at finite time. Different numerical simulations and experiments are implemented to verify the effectiveness of the developed control scheme.

The Effect of Agents' Psychology and Social Environment on the Opinion Formation: C/PA Relative Agreement Model in SW and SF Societies

Opinion dynamics in relative agreement models seen as an extension of bounded confidence ones, involve a new agents’ variable usually called opinion uncertainty and have higher level of complexity than that of bounded confidence models. After revising the meaning of the opinion uncertainty variable we conclude that it has to be interpreted as the agent’s opinion toleration, that changes the type of the variable from the social to the psychological one. Since the convergence rates to the stationary states in dynamics of sociological and psychological variables are in general different, we study the effect of agents’ psychology and social environment interaction on the opinion dynamics, using concord and partial antagonism relative agreement model in small-world and scale-free societies. The model considers agents of two psychological types, concord and partial antagonism, that differs it from other relative agreement models. The analysis of opinion dynamics in particular scenarios was used in this work. Simulation results show the importance of this approach, in particular, the effect of small variations in initial conditions on the final state. We found significant mutual influence of opinion and toleration resulting in a variety of statistically stationary states such as quasi consensus, polarization and fragmentation of society into opinion and toleration groups of different configurations. Consensus was found to be rather rare state in a wide range of model parameters, especially in scale-free societies. The model demonstrates different opinion and toleration dynamics in small-world and scale-free societies.

METODE ARCH/GARCH UNTUK MEMPREDIKSI HUBUNGAN ECONOMIC UNCERTAINTY AKIBAT PANDEMI COVID 19 DAN VOLATILITAS SAHAM

The capital markets of the United States and Indonesia experienced stock market crashes in which stock prices fell sharply and several stock indexes were forced to close prematurely. This study aims to determine the effect of economic uncertainty on the market reaction when the COVID-19 outbreak hit Indonesia. This research is a quantitative research with an event study approach. The time period starts from February to March 2020. The data is time series obtained from www.policyuncertainty.com to calculate the Economic Policy Uncertainty (EPU) Index as a measure of the economic uncertainty variable. Meanwhile, stock returns used the closing stock prices of JCI from February to March 2020 to measure volatility. The data were processed using the Unit Root Test- Augmented Dickey-Fuller to test the stationary and the ARCH/GARCH method to test the effect of the variable. The results show that EPU has no effect on stock volatility in Indonesia in the short term, but has an effect in the long term. This research contributes to the development of the event study literature in the behavior of investors in the capital market as a result of unpredictable events. As well as responses to policies formulated by stakeholders to address future economic uncertainties..

Towards improved analysis of short mesoscale sea level signals from satellite altimetry

Abstract. Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio (SNR), making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free sea level anomaly (SLA) signals (Quilfen, 2021). The method combines empirical mode decomposition (EMD), used to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by discrete wavelet transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local SNR but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3, and SARAL/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions are characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA (Satellite Altimeter Short-scale Signals Analysis, https://doi.org/10.12770/1126742b-a5da-4fe2-b687-e64d585e138c, Quilfen and Piolle, 2021) data set of denoised SLA measurements for three reference altimeter missions has already been shown to yield valuable opportunities to evaluate global small mesoscale kinetic energy distributions.

PENGGUNAAN TEORI BIAYA TRANSAKSI DALAM PERILAKU PEMBELIAN ONLINE

Abstract: The purpose of this research is to prove and analyze the effect of uncertainty variable, trust and frequency buying on transaction cost economics, as well as the implications of transaction cost economics on consumer's willingness to buy online. The sample is 115 people. The data comes from analyzed with SEM AMOS version 24. The results showed that the brand uncertainty of online stores had no effect on transaction cost economics. Meanwhile, performance uncertainty of products has a positive effect on transaction cost economics. Dependability of online store, privacy policy and buying frequency have a negative effect on transaction cost economics. Transaction cost economics has a negative effect on consumer's willingness to buy online.Abstrak: Tujuan peneltian yaitu membuktikan dan menganalisis pengaruh variabel uncertainty, trust dan frequency buying terhadap transaction cost economics, serta implikasi transaction cost economics terhadap consumer’s willingness to buy online. Banyaknya responden di penelitian ini adalah 115 orang. Perolehan data dianalisis dengan menggunakan SEM AMOS versi 24. Hasil penelitian menunjukkan bahwa brand uncertainty of online stores tidak berpengaruh terhadap transaction cost economics. Sedangkan performance uncertainty of products berpengaruh positif terhadap transaction cost economics. Dependability of online store, privacy policy dan buying frequency berpengaruh negatif terhadap transaction cost economics. Transaction cost economics berpengaruh negatif terhadap consumer’s willingness to buy online

Gold, Uncertainty, Macroeconomy, Inflation Hedging and Safe Haven in Indonesia

This study examines the impacts of monetary policy uncertainty and macroeconomic variables on gold price dynamics in Indonesia. Monthly time series data was used for the period of January 2009 till December 2018. Indonesia has the second place after Thailand as a country with the highest gold demand in Southeast Asia. but, there are less studies about role of gold as safe haven in Indonesia and this study is the first one that specifically included the uncertainty variable of US monetary policy in the model using ARDL-ECM approach. The ARDL-ECM approach is applied to find out are the gold price dynamics in Indonesia influenced by macroeconomic shocks or by the movements of gold price itself in the previous period. The empirical results of this study indicate that gold plays an important role as inflation hedging and safe haven in Indonesia. This study proved that in the short-term and long-term, gold price in the previous period, exchange rate, and IHSG have a negative and significant relationship on gold prices dynamics in Indonesia, while the London gold price and inflation have a positive and significant relationship on gold price movement in Indonesia. The high level of US monetary policy uncertainty and uncertainty in global economic conditions has led to an increase in gold prices in Indonesia.

Observer-Based Discrete Adaptive Neural Network Control for Automotive PEMFC Air-Feed Subsystem

Polymer electrolyte membrane fuel cell (PEMFC) air-feed subsystem is usually affected negatively by system uncertainty and unavailable variable. This paper investigates a discrete neural network control with an observer for the oxygen excess ratio (OER) control. The control goal is to avoid oxygen starvation and maintain the optimal net power. Specifically, by utilizing coordinate transformation and Euler approximation, the discrete strict-feedback form is obtained, and the backstepping technique can be applied. To estimate the unavailable variable with the measurable system output, a discrete neural network observer is proposed. Besides, the discrete neural network controller is designed to tackle the system uncertainty and achieve an ideal OER tracking. Finally, the system tracking error is proved to be semi-globally uniformly ultimately bounded by Lyapunov stability theory. Numerical simulations and hardware-in-loop (HIL) experiments are presented to demonstrate the effectiveness and superiorities of the proposed controller.

Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI.

Variations in the vessel radius of segmented surfaces of intracranial aneurysms significantly influence the fluid velocities given by computer simulations. It is important to generate models that capture the effect of these variations in order to have a better interpretation of the numerically predicted hemodynamics. Also, it is highly relevant to develop methods that combine experimental observations with uncertainty modeling to get a closer approximation to the blood flow behavior. This work applies polynomial chaos expansion to model the effect of geometric uncertainties on the simulated fluid velocities of intracranial aneurysms. The radius of the vessel is defined as the uncertainty variable. Proper orthogonal decomposition is applied to characterize the solution space of fluid velocities. Next, a process of projecting the 4D-Flow MRI velocities on the basis vectors followed by coefficient mapping using generalized dynamic mode decomposition enables the merging of 4D-Flow MRI with the uncertainty propagated fluid velocities. Polynomial chaos expansion propagates the fluid velocities with an error of 2% in velocity magnitude relative to computer simulations. Also, the bifurcation region (or impingement location) shows a standard deviation of 0.17 m/s (since an available reported variance in the vessel radius is adopted to model the uncertainty, the expected standard deviation may be different). Numerical phantom experiments indicate that the proposed approach reconstructs the fluid velocities with 0.3% relative error in presence of geometric uncertainties. Polynomial chaos expansion is an effective approach to propagate the effect of the uncertainty variable in the blood flow velocities of intracranial aneurysms. Merging 4D-Flow MRI and uncertainty propagated fluid velocities leads to more realistic flow trends relative to ignoring the uncertainty in the vessel radius.

Financial market efficiency: global and regional financial market perspective

PurposeThis study examines dynamics of global and regional financial market efficiency; and how specific features of the market and other conditions influence variability in such efficiency.Design/methodology/approachThe study employs fixed effects statistical approach in its examination of how specific features of financial markets influence variability in its efficiency.FindingsThis study finds that individual IMF defined economic regions tend to exhibits significantly different financial market efficiency characteristics given specific market features and conditions. In regional level comparative analysis (e.g. Europe, Africa, Asia–Pacific etc.) this study finds that incidence of financial market uncertainty is the dominant condition with significant effect on financial market efficiency across all the IMF regions. In the global level analysis, empirical estimates presented suggest that financial market uncertainty, financial institutional depth and financial institutional efficiency tend to have significant positive influence on global financial market efficiency all things being equal. In the same analysis however, this study finds that financial market and financial institutional access growth has significant negative impact on financial market efficiency.Originality/valueThe uniqueness of this study compared to related ones found in the literature stems from its focus on financial market efficiency at the global, and IMF defined regional block level instead of on a specific economy as often found in the literature. Additionally, in contrast to other related studies, this study further examines the role of global financial market uncertainty in its financial market efficiency analysis. Financial market uncertainty variable may be unique to this study because the variable is derived through an econometric process from a base variable.

주택가격의 불확실성이 주택공급시기 결정에 미치는 영향

This study examined whether the real option is implemented in the housing supply market where houses are available for sale at the time of construction. The Cox Proportional Hazard Model was used to investigate whether real options are implemented between permits and commencements and subsequently between commencements and completions after deriving the uncertainty variable of the housing market through a GARCH model. The study found that builders alter their exercise rate of permits with the latest information on housing-market conditions. The empirical analysis supports what theoretical literature and previous studies have concluded, that real options are essential in justifying irreversible investments such as housing supply. In particular, the time of starts was observed to be a crucial time in investment decision-making in a market where large-scale houses are sold in advance before construction. The results of this study imply that it is necessary for policy responses to minimize the uncertainty of the housing market such that housing builders can provide stable housing.

Oil price uncertainty and macroeconomic variables in Turkey

AbstractThis study analyses the influence of uncertainty in oil prices on real economic activity, inflation and money supply in Turkey for the period 1986:1–2019:07. The uncertainty variable is measured by the generalised autoregressive conditional heteroscedasticity (GARCH) model. To investigate the relationship between the uncertainty in oil prices and our variables, we used the non‐linear cointegrating autoregressive distributed lag (NARL) model, considering the fact that this relationship may not be linear. Thus, we were able to analyse the short‐ and long‐term non‐linear relationship between the variables. Our findings indicate that the impact of oil price uncertainty on real economic activity is asymmetric: that is, real economic activity responds more to decreases in oil price uncertainty than to long‐term increases. Similarly, we confirm the existence of both long‐ and short‐term asymmetric behaviour of inflation, and our findings show that positive shocks in oil price uncertainty are more intensively transmitted to inflation than negative ones. Moreover, we provide evidence that a decline in oil price uncertainty is related to higher money supply in the short term. These findings present useful implications for designing appropriate policies to achieve the goals related to Turkey’s economic activity and inflation level.

Analysis of construction project cost risk in bidding documents preparation phase

Construction projects usually involve high investments. Hence, the project cost risk analysis during the project bids phase is important. In fact, the costs of construction projects nearly always exceed the planned scenario. This research presents the cost risk analysis method during the project bids phase with the input parameter as the uncertainty variable. The uncertainty variable distribution in this research built on the historical data set of 130 projects invested in the period of 2015-2019. The research has shown that the input parameters as the uncertainty variables are the causes affecting the risks encountered when the contractor executes the project. Moreover, this research also has shown that the Monte Carlo simulation and sensitivity analysis is the tool to detect and risk management.

Distributionally robust optimization model of active distribution network considering uncertainties of source and load

To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads, a two-stage distributionally robust optimization model is proposed for the active distribution network (ADN) optimization problem considering the uncertainties of the source and load in this paper. By establishing an ambiguity set to capture the uncertainties of the photovoltaic (PV) power, wind power and load, the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables. The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set. The first-stage expected cost is obtained based on the predicted value of the uncertainty variable. The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision. The generalized linear decision rule approximates the two-stage optimization model, and the affine function is introduced to provide a closer approximation to the second-stage optimization model. Finally, the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods, stochastic programming, and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.

Time-variant reliability modeling based on hybrid non-probability method

In this paper, an innovative time-variant reliability analysis method based on non-probabilistic theory is developed for structures, which combines the interval model with the elliptical model. The coordinate transformation method is adopted to eliminate the correlation between variables for improving the accuracy of the reliability analysis results. An improved multi-dimensional response surface method is selected to solve the reliability problem based on the hybrid model. Finally, taking the cantilever beam structure as a case study, which is used to shed a light on the validity of the presented measure. The results show that in the early and the middle stage of service period, the dispersion of the uncertainty variable has a significant impact on the reliability. At the later stage, the moment degradation is the main factor affecting the reliability.

Uncertainty, budget deficit and economic growth in OPEC member countries

ABSTRACT In this research, the effect of uncertainty and the government budget deficit on economic growth has been investigated in 12 OPEC member countries from 1997 to 2013. Given the structure of OPEC member countries, production uncertainty and oil revenue uncertainty were introduced as the proxy for the uncertainty variable, and the PP-GARCH method was used to estimate the two mentioned uncertainties. To investigate the relationship between research variables, the panel ARDL model was used based on two methods of PMG and MG for without and with uncertainty situations. The results of the research showed that under without uncertainty conditions, the government deficit and tax revenue had a significant negative effect on the economic growth of OPEC member countries. Also, in the uncertainty scenarios, both variables of production uncertainty and oil revenue uncertainty had a significant negative effect on the economic growth of countries in both short and long run.

Robust parameter design based on Kullback-Leibler divergence

Robust parameter design (RPD) is an effective approach used to improve the quality of products and processes. Traditional RPD assumes that the distribution of the uncontrollable factors (noise variables) is known, and then selects the levels of the controllable variables that minimize the variation imposed on the process through the noise variables while optimizing a defined quality characteristic. However, the distribution of the noise variables is often unknown in practice, using improper distribution assumptions may lead to biased optimization results with poor performance. In this paper, a novel methodology is proposed from the perspective of robust optimization, regarding the distribution of the noise factor as an uncertainty variable. Based upon the data, a family of noise distributions can be established via KL divergence, and then a KL-based robust parameter design (KL-RPD) approach is developed. It is shown that the proposed methodology outperforms the existing methods. Our methodology can be applied to any kinds of metamodel, to problems with any high-dimensional noise factors, and has more accurate results than the other approaches. Two examples are utilized to illustrate these advantages.

How Do Members of the Duchenne and Becker Muscular Dystrophy Community Perceive a Discrete-Choice Experiment Incorporating Uncertain Treatment Benefit? An Application of Research as an Event.

Best-worst scaling methods have been used in several Duchenne and Becker muscular dystrophy (DBMD) studies to quantify patient and caregiver priorities and preferences and promote patient-focused drug development (PFDD). We sought to assess the extent to which different members of the DBMD community would accept a discrete-choice experiment (DCE) that incorporates uncertainty regarding individual-level benefit. A community advisory board encouraged the development and testing of a DCE to further examine treatment preferences in DBMD and to facilitate the inclusion of a policy-relevant uncertainty attribute. The DCE assessed preferences across a primary outcome (muscle strength) and several risks (uncertainty regarding treatment benefit, kidney damage risk, and fracture risk). The single instrument was tested among adult patients, caregivers, and professionals at the national Parent Project Muscular Dystrophy annual meeting. The DCE was analyzed using conditional logit. Instrument acceptability was evaluated using a previously developed set of questions assessing ease of understanding and answering, and if answers reflected the respondents' real preferences. We proposed a 75% agreement rate as a threshold of acceptability, and used a Z score to assess if this was met, exceeded, or rejected. A total of 161 people completed the survey including 9 patients, 87 caregivers, and 65 professionals. Patients reported high acceptability across all evaluation items (p values > 0.21). Caregivers and professionals exceeded the benchmark of acceptability on understanding and reflecting real preferences (p < 0.001). Professionals met the benchmark (p = 0.08) for ease of answering, but caregivers did not (p < 0.01). DCE results demonstrated thatall groups made meaningful trade-offs, with patients being less tolerant of risks than either caregivers or professionals (p < 0.001), and with no significant difference between caregivers and professionals (p = 0.46). This study demonstrates the acceptable application of a single instrument across a multi-stakeholder population that used a complex preference method and included a policy-relevant uncertainty variable. Ease of answering was lowest among caregivers, but a post-hoc analysis revealed that it was most difficult for those with children under the age of 10, while those with older children met the threshold. The success of this study has laid the foundation for a global study of DBMD preferences using this method.

Extended Importance Sampling for Reliability Analysis under Evidence Theory

In early engineering practice, the lack of data and information makes uncertainty difficult to deal with. However, evidence theory has been proposed to handle uncertainty with limited information as an alternative way to traditional probability theory. In this contribution, a simulation-based approach, called ‘Extended importance sampling’, is proposed based on evidence theory to handle problems with epistemic uncertainty. The proposed approach stems from the traditional importance sampling for reliability analysis under probability theory, and is developed to handle the problem with epistemic uncertainty. It first introduces a nominal instrumental probability density function (PDF) for every epistemic uncertainty variable, and thus an ‘equivalent’ reliability problem under probability theory is obtained. Then the samples of these variables are generated in a way of importance sampling. Based on these samples, the plausibility and belief (upper and lower bounds of probability) can be estimated. It is more efficient than direct Monte Carlo simulation. Numerical and engineering examples are given to illustrate the efficiency and feasible of the proposed approach.

The Optimization and Analysis of Hydrocarbon Recovery under Injection of Biopolymer, Synthetic Polymer and Gels in a Heterogeneous Reservoir

Water injection is a conventional method which increases the recovery percentage by providing pressure support and displacing oil in the heterogeneous porous medium. In such a displacement process, (low) mobility ratio is important for a more efficient oil displacement by the injected fluid. As such, the mobility ratio can be reduced using the fluids involving gelling agents for increasing in the volumetric sweep. While polymers degrade and break up on experiencing sudden shear stresses and high temperatures, polymer macromolecules are forced to flow into narrow channels and pores where molecular scission processes can take place. Thus, it is of utmost importance to have a strong understanding of the use of the right type and amount of viscosity as a reduction agent.&#x0D; For polymer injection, a comparison of xanthan polymer and synthetic polymer mechanisms was conducted. A commercial full-physics reservoir simulator was coupled with a robust optimization and uncertainty tool to run the model, where a simplified gel kinetics was assumed to form a microgel with no redox catalyst. Water injection continues over all six layers for 450 days, followed by gel system injection for 150 days in the bottom two layers. Water injection was continued to four years. The top four layers have higher horizontal permeabilities, and a high permeability streak is at the bottom of the reservoir to reduce any helpful effects of gravity. Control and uncertainty variables were set to investigate the sensitivity of this process using the coupled optimization and uncertainty tool.&#x0D; Results demonstrate deep penetration of gel and blocking of the high permeability bottom layers. Sensitivity studies indicate the relative merits of biopolymer, xanthan polymer in terms of viscosity effects vs synthetic PAM in terms of resistance factor vs in-situ gelation treatments and their crossflow dependence. Adsorption and retention of polymer and gel are permeability dependent.&#x0D; Considering the potential for application of gel solutions in the U.S. and throughout the world, this study illustrates the relative advantages of different treatments in terms of viscosity reduction in the same model in a comparative way, while outlining the significance of each control and uncertainty variable for better management of reservoirs where displacement efficiency is highly critical.