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The organocatalytic activities of highly substituted proline esters obtained through asymmetric [3+2] cycloadditions of azomethine ylides derived from glycine iminoesters have been analyzed by 19F NMR and through kinetic isotope effects. Kinetic rate constants have been determined for unnatural proline esters incorporating different substituents. It has been found that exo-L and endo-L unnatural proline methyl esters yield opposite enantiomers in aldol reactions between cyclic ketones and aromatic aldehydes. The combined results reported in this study show subtle and remote effects that determine the organocatalytic behavior of these synthetic but readily available amino acid derivatives. These data can be used as design criteria for the development of new pyrrolidine-based organocatalysts.

We present a novel regression framework centered on a coherent and averse measure of risk, the superquantile risk (also called conditional value-at-risk), which yields more conservatively fitted curves than classical least squares and quantile regressions. In contracts to other generalized regression techniques that approximate conditional superquantiles by various combinations of conditional quantiles, we directly and inperfect analog to classical regressional obtain superquantile regression functions as optimal solutions of certain error minimization problems. We show the existence and possible uniqueness of regression functions, discuss the stability of regression functions under perturbations and approximation of the underlying data, and propose an extension of the coefficient of determination R-squared and Cook’s distance for assessing the goodness of fit for both quantile and superquantile regression models. We present two classes of computational methods for solving the superquantile regression problem, compare both methods’ complexity, and illustrate the methodology in eight numerical examples in the areas of military applications, concerning mission employment of U.S. Navy helicopter pilots and Portuguese Navy submarines, reliability engineering, uncertainty quantification, and financial risk management.

We have performed experiments using a EURO 5 light passenger vehicle, operated over the New European Driving Cycle (NEDC). Fuel blends containing 7% (B7) and 20% (B20) of biodiesel (84% soyabean/16% palm) in petroleum-based diesel were tested and compared with a diesel fuel (B0). The exhaust gases emissions were assessed for NO, NO2, SO2and volatile organic compounds (VOC), including a speciation analysis of VOC. The experiment reveals that biodiesel blends improve the combustion efficiency, NO and SO2emissions, and increasing NO2and total VOC emissions. The VOC speciation analysis suggests that the type and fraction of VOC existent in exhausted gases is fuel dependent, changing their presence and concentrations according to the fuel used. Additionally, the concentration of the three main VOC species in exhaust gases from B0 (benzene, toluene and octane) decrease 60-80% if a B20 blend is used. This experimental study contributes to a better characterization of the emission factors of EURO 5 light passenger vehicles using diesel/biodiesel blends and to a better understanding of the impact of the use of biodiesel blends on pollutant emissions.

The paper presents a generalized regression technique centered on a superquantile (also called conditional value-at-risk) that is consistent with that coherent measure of risk and yields more conservatively fitted curves than classical least-squares and quantile regressions. In contrast to other generalized regression techniques that approximate conditional superquantiles by various combinations of conditional quantiles, we directly and in perfect analog to classical regression obtain superquantile regression functions as optimal solutions of certain error minimization problems. We show the existence and possible uniqueness of regression functions, discuss the stability of regression functions under perturbations and approximation of the underlying data and propose an extension of the coefficient of determination R-squared for assessing the goodness of fit. The paper presents two numerical methods for solving the error minimization problems and illustrates the methodology in several numerical examples in the areas of uncertainty quantification reliability engineering, and financial risk management., in review

Five air quality models were applied over Portugal for July 2006 and used as ensemble members. Each model was used, with its original set up in terms of meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and the ensemble of ozone (O-3) and particulate matter (PM) is performed using monitoring data from 22 background sites. The ensemble approach, based on the mean and median of the five models, did not improve significantly the skill scores due to large deviations in each ensemble member. Different bias correction techniques, including a subtraction of the mean bias and a multiplicative ratio adjustment, were implemented and analysed. The obtained datasets were compared against the individual modelled outputs using the bias, the root mean square error (RMSE) and the correlation coefficient. The applied bias correction techniques also improved the skill of the individual models and work equally well over the entire range of observed O-3 and PM values. The obtained results revealed that the best bias correction technique was the ratio adjustment with a 4-day training period, demonstrating significant improvements for both analysed pollutants. The increase in the ensemble skill found comprehends a bias reduction of 88 % for O-3, and 92 % for PM10, and also a decrease in 23 % for O-3 and 43 % for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach was also examined with successful skills comparing to the uncorrected ensemble for both pollutants.

Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

This thesis deals with a type of stochastic optimization problem where the decision maker does not have complete information concerning the objective function. Specifically, we consider a discrete time-and-space search optimization problem where we seek to find a moving target in an area of operations. There are two sources of uncertainty: the target location and the sensor performance. We formulate the objective function for this problem in terms of a risk measure of a parameterized random variable and consider three cases involving various degrees of knowledge about the sensor performance. In all cases, we consider both the expectation and superquantile risk measures. While the expectation results in an objective function representing the probability of missing the target, the superquantile gives rise to more conservative search plans that perform reasonably well even under exceptional circumstances. In the case of incomplete in-formation about the distribution of the sensor performance, we approximate the random variable using a nonstandard regression that minimizes the error induced in some sense. We examine the cases in a series of numerical examples., http://archive.org/details/searchplanningun109455479, Approved for public release; distribution is unlimited.

This thesis deals with a type of stochastic optimization problem where the decision maker does not have complete information concerning the objective function. Specifically, we consider a discrete time-and-space search optimization problem where we seek to nd a moving target in an area of operations. There are two sources of uncertainty: the target location and the sensor performance. We formulate the objective function for this problem in terms of a risk measure of a parameterized random variable and consider three cases involving various degrees of knowledge about the sensor performance. In all cases, we consider both the expectation and superquantile risk measures. While the expectation results in an objective function representing the probability of missing the target, the superquantile gives rise to more conservative search plans that perform reasonably well even under exceptional circumstances. In the case of incomplete in- formation about the distribution of the sensor performance, we approximate the random variable using a nonstandard regression that minimizes the error induced in some sense. We examine the cases in a series of numerical examples., The original document contains color images.

This thesis deals with a type of stochastic optimization problem where the decision maker does not have complete information concerning the objective function. Specifically, we consider a discrete time-and-space search optimization problem where we seek to find a moving target in an area of operations. There are two sources of uncertainty: the target location and the sensor performance. We formulate the objective function for this problem in terms of a risk measure of a parameterized random variable and consider three cases involving various degrees of knowledge about the sensor performance. In all cases, we consider both the expectation and superquantile risk measures. While the expectation results in an objective function representing the probability of missing the target, the superquantile gives rise to more conservative search plans that perform reasonably well even under exceptional circumstances. In the case of incomplete in-formation about the distribution of the sensor performance, we approximate the random variable using a nonstandard regression that minimizes the error induced in some sense. We examine the cases in a series of numerical examples.