Electron-Impact Uncertainty Analysis and its Impact on Certain Temperature Diagnostics

In this study we calculate the electron-impact uncertainties in atomic data for direct ionization and recombination and investigate the role of these uncertainties on spectral diagnostics. We outline a systematic approach to assigning meaningful uncertainties that vary with electron temperature. Once these uncertainty parameters have been evaluated, we can then calculate the uncertainties on key diagnostics through a Monte Carlo routine, using the Astrophysical Emission Code (APEC) [Smith et al. 2001]. We incorporate these uncertainties into well known temperature diagnostics, such as the Lyman alpha versus resonance line ratio and the G ratio. We compare these calculations to a study performed by [Testa et al. 2004], where significant discrepancies in the two diagnostic ratios were observed. We conclude that while the atomic physics uncertainties play a noticeable role in the discrepancies observed by Testa, they do not explain all of them. This indicates that there is another physical process occurring in the system that is not being taken into account. This work is supported in part by the National Science Foundation REU and Department of Defense ASSURE programs under NSF Grant no. 1262851 and by the Smithsonian Institution.

Dealing with uncertainty: challenges and possibilities for the early childhood profession

In many countries, strategies to further develop services and institutions for the education and care of young children are linked to a discourse on professionalism. Ambitious policy goals, it is argued, can only be achieved by a skilled and qualified workforce whose practice is guided by a professional body of knowledge. This article argues that the prevailing conceptualisation of the early childhood professional is constructed out of a particular, hierarchical mode of producing and applying expert knowledge that is not necessarily appropriate to professional practice in the field of early childhood education. However, it is highly effective and contributes to forming a professional habitus that contradicts the relational core of early childhood practice. Drawing on the conceptual framework of hermeneutics, the article explores an alternative paradigm of a relational, systemic professionalism that embraces openness and uncertainty, and encourages co‐construction of professional knowledges and practices. Research, in this frame of thinking, is understood as a dialogic activity of asking critical questions and creating understandings across differences, rather than producing evidence to direct practice.

Small area estimation for the price of habitation transaction: comparison of different uncertainty measures of temporal EBLUP

Local level planning requires statistics for small areas, but normally due to cost or logistic constraints, sample surveys are often planned to provide reliable estimates only for large geographical regions and large subgroups of a population.

The Impact of Uncertainty Shocks under Measurement Error: A Proxy SVAR approach

A growing literature considers the impact of uncertainty using SVAR models that include proxies for uncertainty shocks as endogenous variables. In this paper we consider the impact of measurement error in these proxies on the estimated impulse responses. We show via a Monte-Carlo experiment that measurement error can result in attenuation bias in impulse responses. In contrast, the proxy SVAR that uses the uncertainty shock proxy as an instrument does not su¤er from this bias. Applying this latter method to the Bloom (2009) data-set results in impulse responses to uncertainty shocks that are larger in magnitude and more persistent than those obtained from a recursive SVAR.

A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.

Uncertainty assessment approach for composite structures based on global sensitivity indices

The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.

Uncertainty analysis based on sensitivity applied to angle-ply composite structures

This article describes a finite element-based formulation for the statistical analysis of the response of stochastic structural composite systems whose material properties are described by random fields. A first-order technique is used to obtain the second-order statistics for the structural response considering means and variances of the displacement and stress fields of plate or shell composite structures. Propagation of uncertainties depends on sensitivities taken as measurement of variation effects. The adjoint variable method is used to obtain the sensitivity matrix. This method is appropriated for composite structures due to the large number of random input parameters. Dominant effects on the stochastic characteristics are studied analyzing the influence of different random parameters. In particular, a study of the anisotropy influence on uncertainties propagation of angle-ply composites is carried out based on the proposed approach.

From local to global importance measures of uncertainty propagation in composite structures

The influence of uncertainties of input parameters on output response of composite structures is investigated in this paper. In particular, the effects of deviations in mechanical properties, ply angles, ply thickness and on applied loads are studied. The uncertainty propagation and the importance measure of input parameters are analysed using three different approaches: a first-order local method, a Global Sensitivity Analysis (GSA) supported by a variance-based method and an extension of local variance to estimate the global variance over the domain of inputs. Sample results are shown for a shell composite laminated structure built with different composite systems including multi-materials. The importance measures of input parameters on structural response based on numerical results are established and discussed as a function of the anisotropy of composite materials. Needs for global variance methods are discussed by comparing the results obtained from different proposed methodologies. The objective of this paper is to contribute for the use of GSA techniques together with low expensive local importance measures.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

Privatization and entry of a foreign firm with demand uncertainty

We investigate the effects of trade with a foreign firm and privatization of the domestic pubUc firm on an incentive for the domestic firm to reduce costs by undertaking R&D investment, under demand uncertainty. We suppose that the domestic firm is less efficient than the foreign firm. However, the domestic firm can lower its marginal costs by conducting cost-reducing R&D investment. We examine the impacts of entry of a foreign firm, and the effects of demand uncertainty, on decisions upon cost-reducing R&D investment by the domestic firm and how these affect the domestic welfare.

Cournot competition between a non-profit firm and a for-profit firm with uncertainty

In this paper, we consider a Cournot competition between a nonprofit firm and a for-profit firm in a homogeneous goods market, with uncertain demand. Given an asymmetric tax schedule, we compute explicitly the Bayesian-Nash equilibrium. Furthermore, we analyze the effects of the tax rate and the degree of altruistic preference on market equilibrium outcomes.