Study of the volatile compounds from plum (Prunus domestica L. cv. Horvin) and estimation of their contribution to the fruit aroma

Simultaneous Distillation-Extraction (SDE) and headspace-solid phase microextraction (HS-SPME) combined with GC-FID and GC-MS were used to analyze volatile compounds from plum (Prunus domestica L. cv. Horvin) and to estimate the most odor-active compounds by application of the Odor Activity Values (OAV). The analyses led to the identification of 148 components, including 58 esters, 23 terpenoids, 14 aldehydes, 11 alcohols, 10 ketones, 9 alkanes, 7 acids, 4 lactones, 3 phenols, and other 9 compounds of different structures. According to the results of SDE-GC-MS, SPME-GC-MS and OAV, ethyl 2-methylbutanoate, hexyl acetate, (E)-2-nonenal, ethyl butanoate, (E)-2-decenal, ethyl hexanoate, nonanal, decanal, (E)-β-ionone, Γ-dodecalactone, (Z)-3-hexenyl acetate, pentyl acetate, linalool, Γ-decalactone, butyl acetate, limonene, propyl acetate, Δ-decalactone, diethyl sulfide, (E)-2-hexenyl acetate, ethyl heptanoate, (Z)-3-hexenol, (Z)-3-hexenyl hexanoate, eugenol, (E)-2-hexenal, ethyl pentanoate, hexyl 2-methylbutanoate, isopentyl hexanoate, 1-hexanol, Γ-nonalactone, myrcene, octyl acetate, phenylacetaldehyde, 1-butanol, isobutyl acetate, (E)-2-heptenal, octadecanal, and nerol are characteristic odor active compounds in fresh plums since they showed concentrations far above their odor thresholds.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

The industrial equilibrium exchange rate in Brazil: an estimation

This paper presents a methodology for calculating the industrial equilibrium exchange rate, which is defined as the one enabling exporters of state-of-the-art manufactured goods to be competitive abroad. The first section highlights the causes and problems of overvalued exchange rates, particularly the Dutch disease issue, which is neutralized when the exchange rate strikes the industrial equilibrium level. This level is defined by the ratio between the unit labor cost in the country under consideration and in competing countries. Finally, the evolution of this exchange rate in the Brazilian economy is estimated.

Overvaluation trend of the Brazilian currency in the 2000s: empirical estimation

The aim of this paper is to discuss the trend of overvaluation of the Brazilian currency in the 2000s, presenting an econometric model to estimate the real exchange rate (RER) and which should be a reference level of the RER to guide long-term economic policy. In the econometric model, we consider long-term structural and short-term components, both of which may be responsible for explaining overvaluation trend of the Brazilian currency. Our econometric exercise confirms that the Brazilian currency had been persistently overvalued throughout almost all of the period under analysis, and we suggest that the long-term reference level of the real exchange rate was reached in 2004. In July 2014, the average nominal exchange rate should have been around 2.90 Brazilian reais per dollar (against an observed nominal rate of 2.22 Brazilian reais per dollar) to achieve the 2004 real reference level (average of the year). That is, according to our estimates, in July 2014 the Brazilian real was overvalued at 30.6 per cent in real terms relative to the reference level. Based on these findings we conclude the paper suggesting a mix of policy instruments that should have been used in order to reverse the overvaluation trend of the Brazilian real exchange rate, including a target for reaching a real exchange rate in the medium and the long-run which would favor resource allocation toward more technological intensive sectors.

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Original.

Variational Monte Carlo estimation of the dissociation energy of CuH using correlated sampling

A new approach to treating large Z systems by quantum Monte Carlo has been developed. It naturally leads to notion of the 'valence energy'. Possibilities of the new approach has been explored by optimizing the wave function for CuH and Cu and computing dissociation energy and dipole moment of CuH using variational Monte Carlo. The dissociation energy obtained is about 40% smaller than the experimental value; the method is comparable with SCF and simple pseudopotential calculations. The dipole moment differs from the best theoretical estimate by about 50% what is again comparable with other methods (Complete Active Space SCF and pseudopotential methods).

Exact property estimation from diffusion Monte Carlo with minimal stochastic reconfiguration /

Our objective is to develop a diffusion Monte Carlo (DMC) algorithm to estimate the exact expectation values, ($o|^|^o), of multiplicative operators, such as polarizabilities and high-order hyperpolarizabilities, for isolated atoms and molecules. The existing forward-walking pure diffusion Monte Carlo (FW-PDMC) algorithm which attempts this has a serious bias. On the other hand, the DMC algorithm with minimal stochastic reconfiguration provides unbiased estimates of the energies, but the expectation values ($o|^|^) are contaminated by ^, an user specified, approximate wave function, when A does not commute with the Hamiltonian. We modified the latter algorithm to obtain the exact expectation values for these operators, while at the same time eliminating the bias. To compare the efficiency of FW-PDMC and the modified DMC algorithms we calculated simple properties of the H atom, such as various functions of coordinates and polarizabilities. Using three non-exact wave functions, one of moderate quality and the others very crude, in each case the results are within statistical error of the exact values.