Sensory profile and drivers of liking for grape nectar among smoker and nonsmoker consumers

Decreased gustatory and olfactory capacity is one of the problems caused by tobacco use. The objectives of this study were to determine the sensory profile of six grape nectar samples sweetened with different sweeteners and to verify the drivers of liking in two distinct consumer groups: smokers and nonsmokers. The sensory profile was constructed by twelve trained panelists using quantitative descriptive analysis (QDA). Consumer tests were performed with 112 smokers and 112 nonsmokers. Partial least squares regression analyses was used to identify the drivers of acceptance and rejection of the grape nectars among the two consumer groups. According to the QDA, the samples differed regarding six of the nineteen attributes generated. The absolute averages of the affective test were lower in the group of smokers; possibly because smoking influences acceptance and eating preferences, especially with regard to sweet foods. The results showed that the grape flavor was the major driver of preference for acceptance of the nectar, while astringency, wine aroma, bitterness and sweetness, and bitter aftertaste were drivers of rejection in the two groups of consumers, with some differences between the groups.

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

This work investigates theoretical properties of symmetric and anti-symmetric kernels. First chapters give an overview of the theory of kernels used in supervised machine learning. Central focus is on the regularized least squares algorithm, which is motivated as a problem of function reconstruction through an abstract inverse problem. Brief review of reproducing kernel Hilbert spaces shows how kernels define an implicit hypothesis space with multiple equivalent characterizations and how this space may be modified by incorporating prior knowledge. Mathematical results of the abstract inverse problem, in particular spectral properties, pseudoinverse and regularization are recollected and then specialized to kernels. Symmetric and anti-symmetric kernels are applied in relation learning problems which incorporate prior knowledge that the relation is symmetric or anti-symmetric, respectively. Theoretical properties of these kernels are proved in a draft this thesis is based on and comprehensively referenced here. These proofs show that these kernels can be guaranteed to learn only symmetric or anti-symmetric relations, and they can learn any relations relative to the original kernel modified to learn only symmetric or anti-symmetric parts. Further results prove spectral properties of these kernels, central result being a simple inequality for the the trace of the estimator, also called the effective dimension. This quantity is used in learning bounds to guarantee smaller variance.

Modelling the simultaneous effects of intellectual capital and knowledge management on the organisational performance of Finnish companies

The aim of this study was to contribute to the current knowledge-based theory by focusing on a research gap that exists in the empirically proven determination of the simultaneous but differentiable effects of intellectual capital (IC) assets and knowledge management (KM) practices on organisational performance (OP). The analysis was built on the past research and theoreticised interactions between the latent constructs specified using the survey-based items that were measured from a sample of Finnish companies for IC and KM and the dependent construct for OP determined using information available from financial databases. Two widely used and commonly recommended measures in the literature on management science, i.e. the return on total assets (ROA) and the return on equity (ROE), were calculated for OP. Thus the investigation of the relationship between IC and KM impacting OP in relation to the hypotheses founded was possible to conduct using objectively derived performance indicators. Using financial OP measures also strengthened the dynamic features of data needed in analysing simultaneous and causal dependences between the modelled constructs specified using structural path models. The estimates were obtained for the parameters of structural path models using a partial least squares-based regression estimator. Results showed that the path dependencies between IC and OP or KM and OP were always insignificant when analysed separate to any other interactions or indirect effects caused by simultaneous modelling and regardless of the OP measure used that was either ROA or ROE. The dependency between the constructs for KM and IC appeared to be very strong and was always significant when modelled simultaneously with other possible interactions between the constructs and using either ROA or ROE to define OP. This study, however, did not find statistically unambiguous evidence for proving the hypothesised causal mediation effects suggesting, for instance, that the effects of KM practices on OP are mediated by the IC assets. Due to the fact that some indication about the fluctuations of causal effects was assessed, it was concluded that further studies are needed for verifying the fundamental and likely hidden causal effects between the constructs of interest. Therefore, it was also recommended that complementary modelling and data processing measures be conducted for elucidating whether the mediation effects occur between IC, KM and OP, the verification of which requires further investigations of measured items and can be build on the findings of this study.

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.

Linking of stress offset score (SOS), work satisfaction, and organizational commitment to intentions to quit

The purpose of this study was to identify the impact of stressors and offsetting satistiers, measured in this study with Stress Offset Score (SOS), on intentions to quit and examine the mediating and moderating effects of three facets of work satisfaction (job satisfaction, pay satisfaction, and satisfaction with supervisor) and two facets of organizational commitment (affective and nonnative commitment) on this relationship. The sample was composed of 2990 employees from 21 public and private organizations. The interaction of each type of work satisfaction and organizational commitment, with SOS, was tested using Ordinary Least Squares (OLS) procedures. Intentions to quit was the dependent variable. The research questions were determine: (1) Does SOS predict intentions to quit? (2) Does work satisfaction mediate the predictive relationship of SOS on intentions to quit? (3) Does organizational commitment mediate the predictive relationship of SOS on intent to quit? (4) Does work satisfaction moderate the predictive relationship of SOS on intentions to quit? and (5) Does organizational commitment moderate the predictive relationship of SOS on intentions to quit? The results indicated that SOS was negatively correlated with intentions to quit. Each of the types of work satisfaction and organizational commitment variables showed a partial mediated relationship with SOS and each relationship was highly significant, while normative commitment explained more of the relationship then other mediators. The study also tested for interactions but no statistical significant relationships where established between any of the interaction terms (e.g., SOSxJob Satisfaction and SOSxAffcctive Commitment) and intentions to quit.

Molecular dynamics calculation of mean square displacement in alkali metals and rare gas solids and comparison with lattice dynamics

Molec ul ar dynamics calculations of the mean sq ua re displacement have been carried out for the alkali metals Na, K and Cs and for an fcc nearest neighbour Lennard-Jones model applicable to rare gas solids. The computations for the alkalis were done for several temperatures for temperature vol ume a swell as for the the ze r 0 pressure ze ro zero pressure volume corresponding to each temperature. In the fcc case, results were obtained for a wide range of both the temperature and density. Lattice dynamics calculations of the harmonic and the lowe s t order anharmonic (cubic and quartic) contributions to the mean square displacement were performed for the same potential models as in the molecular dynamics calculations. The Brillouin zone sums arising in the harmonic and the quartic terms were computed for very large numbers of points in q-space, and were extrapolated to obtain results ful converged with respect to the number of points in the Brillouin zone.An excellent agreement between the lattice dynamics results was observed molecular dynamics and in the case of all the alkali metals, e~ept for the zero pressure case of CSt where the difference is about 15 % near the melting temperature. It was concluded that for the alkalis, the lowest order perturbation theory works well even at temperat ures close to the melting temperat ure. For the fcc nearest neighbour model it was found that the number of particles (256) used for the molecular dynamics calculations, produces a result which is somewhere between 10 and 20 % smaller than the value converged with respect to the number of particles. However, the general temperature dependence of the mean square displacement is the same in molecular dynamics and lattice dynamics for all temperatures at the highest densities examined, while at higher volumes and high temperatures the results diverge. This indicates the importance of the higher order (eg. ~* ) perturbation theory contributions in these cases.

The satellite band patterns in the near ultraviolet absorption spectra of isotopic compounds of acetaldehyde

The 3700 A - 3000 A absorption spectra of CH3CHO and its isotopic compounds such as CH3CDO, CD3CHO and CD3CDO were studied in the gas phase at room temperature and low temperatures. The low resolution spectra of the compounds were recorded by a 1.5 m Baush and Lomb grating spectrograph. The high resolution spectra were recorded by a Ebert spectrograph with the Echelle grating and the holographic grating separately. The multiple reflection cells were used to achieve the long path length. The pressure-path length used for the absorption spectrum of CH 3CHO was up to 100 mm Hg )( 91 . 43mo The emission spectrum and the excitation spectrum of CH3CHO were also recorded in this research. The calculated satellite band patterns \vhich were ob-tailied by the method of Lewis were used to compare with the observed near UV absorption spectrum of acetaldehyde. These calculated satellite band patterns belonged to two cases: namely, the barriers-in-phase case and the barriers- out-of-phase case. Each of the calculated patterns corresponded to a stable conformation of acetaldehyde in the excited state . The comparisons showed that the patterns in the observed absorption spectra corresponded to the H-H eclipsed conformations of acetaldehyde in the excited state . The least squares fitting analysis showed that the barrier heights in the excited state were higher than in the ground state. Finally, the isotopic shifts for the isotopic compounds of acetaldehyde were compared to the compounds with the similar deuterium substitution.

An analysis of the methyl rotation dynamics in the So (x' A) and T (a A) states of thioacetaldehyde from Pyrolysis jet spectra /

Jet-cooled, laser-induced phosphorescence excitation spectra (LIP) of thioacetaldehyde CH3CHS, CH3CDS, CD3CHS and CD3CDS have been observed over the region 15800 - 17300 cm"^ in a continuous pyrolysis jet. The vibronic band structure of the singlet-triplet n -* n* transition were attributed to the strong coupling of the methyl torsion and aldehydic hydrogen wagging modes . The vibronic peaks have been assigned in terms of two upper electronic state (T^) vibrations; the methyl torsion mode v^g, and the aldehydic hydrogen wagging mode v^^. The electronic origin O^a^ is unequivocally assigned as follows: CH3CHS (16294.9 cm"'' ), CH3CDS (16360.9 cm"'' ), CD3CHS (16299.7 cm"^ ), and CD3CDS (16367.2 cm"'' ). To obtain structural and dynamical information about the two electronic states, potential surfaces V(e,a) for the 6 (methyl torsion) and a (hydrogen wagging) motions were generated by ab initio quantum mechanical calculations with a 6-3 IG* basis in which the structural parameters were fully relaxed. The kinetic energy coefficients BQ(a,e) , B^(a,G) , and the cross coupling term B^(a,e) , were accurately represented as functions of the two active coordinates, a and 9. The calculations reveal that the molecule adopts an eclipsed conformation for the lower Sq electronic state (a=0°,e=0"') with a barrier height to internal rotation of 541.5 cm"^ which is to be compared to 549.8 cm"^ obtained from the microwave experiment. The conformation of the upper T^ electronic state was found to be staggered (a=24 . 68° ,e=-45. 66° ) . The saddle point in the path traced out by the aldehyde wagging motion was calculated to be 175 cm"^ above the equilibrium configuration. The corresponding maxima in the path taken by methyl torsion was found to be 322 cm'\ The small amplitude normal vibrational modes were also calculated to aid in the assignment of the spectra. Torsional-wagging energy manifolds for the two states were derived from the Hamiltonian H(a,e) which was solved variationally using an extended two dimensional Fourier expansion as a basis set. A torsionalinversion band spectrum was derived from the calculated energy levels and Franck-Condon factors, and was compared with the experimental supersonic-jet spectra. Most of the anomalies which were associated with the interpretation of the observed spectrum could be accounted for by the band profiles derived from ab initio SCF calculations. A model describing the jet spectra was derived by scaling the ab initio potential functions. The global least squares fitting generates a triplet state potential which has a minimum at (a=22.38° ,e=-41.08°) . The flatter potential in the scaled model yielded excellent agreement between the observed and calculated frequency intervals.

Development and characterization of concentric capillary nebulizer used in inductively coupled plasma analysis /

A simple, low-cost concentric capillary nebulizer (CCN) was developed and evaluated for ICP spectrometry. The CCN could be operated at sample uptake rates of 0.050-1.00 ml min'^ and under oscillating and non-oscillating conditions. Aerosol characteristics for the CCN were studied using a laser Fraunhofter diffraction analyzer. Solvent transport efficiencies and transport rates, detection limits, and short- and long-term stabilities were evaluated for the CCN with a modified cyclonic spray chamber at different sample uptake rates. The Mg II (280.2nm)/l\/lg 1(285.2nm) ratio was used for matrix effect studies. Results were compared to those with conventional nebulizers, a cross-flow nebulizer with a Scott-type spray chamber, a GemCone nebulizer with a cyclonic spray chamber, and a Meinhard TR-30-K3 concentric nebulizer with a cyclonic spray chamber. Transport efficiencies of up to 57% were obtained for the CCN. For the elements tested, short- and long-term precisions and detection limits obtained with the CCN at 0.050-0.500 ml min'^ are similar to, or better than, those obtained on the same instrument using the conventional nebulizers (at 1.0 ml min'^). The depressive and enhancement effects of easily ionizable element Na, sulfuric acid, and dodecylamine surfactant on analyte signals with the CCN are similar to, or better than, those obtained with the conventional nebulizers. However, capillary clog was observed when the sample solution with high dissolved solids was nebulized for more than 40 min. The effects of data acquisition and data processing on detection limits were studied using inductively coupled plasma-atomic emission spectrometry. The study examined the effects of different detection limit approaches, the effects of data integration modes, the effects of regression modes, the effects of the standard concentration range and the number of standards, the effects of sample uptake rate, and the effect of Integration time. All the experiments followed the same protocols. Three detection limit approaches were examined, lUPAC method, the residual standard deviation (RSD), and the signal-to-background ratio and relative standard deviation of the background (SBR-RSDB). The study demonstrated that the different approaches, the integration modes, the regression methods, and the sample uptake rates can have an effect on detection limits. The study also showed that the different approaches give different detection limits and some methods (for example, RSD) are susceptible to the quality of calibration curves. Multicomponents spectral fitting (MSF) gave the best results among these three integration modes, peak height, peak area, and MSF. Weighted least squares method showed the ability to obtain better quality calibration curves. Although an effect of the number of standards on detection limits was not observed, multiple standards are recommended because they provide more reliable calibration curves. An increase of sample uptake rate and integration time could improve detection limits. However, an improvement with increased integration time on detection limits was not observed because the auto integration mode was used.

ASSESSING THE PERFORMANCE OF EMERGING MARKET MUTUAL FUNDS IN CANADA

Emerging markets have received wide attention from investors around the globe because of their return potential and risk diversification. This research examines the selection and timing performance of Canadian mutual funds which invest in fixed-income and equity securities in emerging markets. We use (un)conditional two- and five-factor benchmark models that accommodate the dynamics of returns in emerging markets. We also adopt the cross-sectional bootstrap methodology to distinguish between ‘skill’ and ‘luck’ for individual funds. All the tests are conducted using a comprehensive data set of bond and equity emerging funds over the period of 1989-2011. The risk-adjusted measures of performance are estimated using the least squares method with the Newey-West adjustment for standard errors that are robust to conditional heteroskedasticity and autocorrelation. The performance statistics of the emerging funds before (after) management-related costs are insignificantly positive (significantly negative). They are sensitive to the chosen benchmark model and conditional information improves selection performance. The timing statistics are largely insignificant throughout the sample period and are not sensitive to the benchmark model. Evidence of timing and selecting abilities is obtained in a small number of funds which is not sensitive to the fees structure. We also find evidence that a majority of individual funds provide zero (very few provide positive) abnormal return before fees and a significantly negative return after fees. At the negative end of the tail of performance distribution, our resampling tests fail to reject the role of bad luck in the poor performance of funds and we conclude that most of them are merely ‘unlucky’.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.

The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Asymptotic Approximations in the Near-Integrated Model with a Non-Zero Initial Condition

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of the relevant joint moment generating function in this more general framework. We also consider, as alternative approximations, the stochastic expansion of Phillips (1987c) and the continuous time approximation of Perron (1991). We assess how these alternative methods provide or not an adequate approximation to the finite-sample distribution of the least-squares estimator in a first-order autoregressive model. The results show that, when the initial condition is non-zero, Perron's (1991) continuous time approximation performs very well while the others only offer improvements when the initial condition is zero.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.