Modified statistical methods to calculate rare gas interaction potentials

New density functionals representing the exchange and correlation energies (per electron) are employed, based on the electron gas model, to calculate interaction potentials of noble gas systems X2 and XY, where X (and Y) are He,Ne,Ar and Kr, and of hydrogen atomrare gas systems H-X. The exchange energy density functional is that recommended by Handler and the correlation energy density functional is a rational function involving two parameters which were optimized to reproduce the correlation energy of He atom. Application of the two parameter function to other rare gas atoms shows that it is "universal"; i. e. ,accurate for the systems considered. The potentials obtained in this work compare well with recent experimental results and are a significant improvement over those from competing statistical modelS.

Application of Reptation Quantum Monte Carlo and Related Methods to LiH

This work investigates mathematical details and computational aspects of Metropolis-Hastings reptation quantum Monte Carlo and its variants, in addition to the Bounce method and its variants. The issues that concern us include the sensitivity of these algorithms' target densities to the position of the trial electron density along the reptile, time-reversal symmetry of the propagators, and the length of the reptile. We calculate the ground-state energy and one-electron properties of LiH at its equilibrium geometry for all these algorithms. The importance sampling is performed with a single-determinant large Slater-type orbitals (STO) basis set. The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software. Using the Bounce method in the calculation of non-energy-related properties, those represented by operators that do not commute with the Hamiltonian, is a novel work. We found that the unmodified Bounce gives good ground state energy and very good one-electron properties. We attribute this to its favourable time-reversal symmetry in its target density's Green's functions. Breaking this symmetry gives poorer results. Use of a short reptile in the Bounce method does not alter the quality of the results. This suggests that in future applications one can use a shorter reptile to cut down the computational time dramatically.

The effect of pH and ethanol on the astringent sub-qualities of red wine, and the implications for optimum grape maturity /

The first objective of this study was to identify appropriate sensory descriptors to assess the astringent sub-qualities of red wine. The influence of pH and ethanol on the sensation of astringency in red wine was evaluated, using a de-alcoholized red wine. A portion of the wine was adjusted to the pH values of 3.2, 3.4, 3.6 and 3.8, and another portion was adjusted to ethanol concentrations of 0%, 6%, 12%, and 15%. In addition, the pH 3.4 and 3.6 treatments were adjusted to an ethanol concentration of 12% and 15% all wines were then assessed sensorially and seventeen terms were identified, through panel discussion, to describe the mouth-feel and taste qualities: velvet, aggressive, silk/satin, dry, fleshy, unripe, pucker viscosity, abrasive, heat, chewy, acidity, grippy/adhesive, bitter, balance, overall astringency, and mouth-coat. Descriptive analysis profiling techniques were used to train the panel and measure the intensity of these attributes. It was found that decreasing pH values (averaged across all ethanol concentrations) showed an increase in the overall astringency of the wine. The combined treatments of ethanol and pH, real wine parameters (pH 3.4 and 3.6; 12% and 15% ethanol) did not have an effect on the perception of the astringent sub-qualities of the wine. A time intensity study was also included using the pH and ethanol adjusted wines, which showed that as the ethanol level of the wines increased so did the time to maximum intensity. The second objective was to identify appropriate sensory descriptors to evaluate the influence of grape maturity and maceration technique (grape skin contact) on the astringency sub-qualities of red vinifera wines from Niagara. The grapes were harvested across two dates, representing an early harvest and a late harvest. A portion of the Cabernet Sauvignon grapes wine was divided into three maceration treatments of oneweek maceration, standard two-week maceration, three-week maceration, and MCM. Another portion of both the early and late harvest Cabernet Sauvignon grapes were chaptalized to yield a final ethanol concentration of 14.5%. The wines were assessed sensorially and thirteen terms were identified, through panel discussion, to describe the mouth-feel and taste qualities: carbon dioxide, pucker, acidity, silk/chamois, dusty/chalky/powdery, sandpaper, numbing, grippy/adhesive, dry, mouthcoat, bitter, balance and, overall astringency. Descriptive analysis techniques were used to train the panel and measure the intensity of these attributes. The data revealed few significant differences in the mouth-feel of the wines with respect to maturity; which included differences in overall astringency and balance. There were varietal differences between Cabernet Sauvignon, Cabernet Franc, and Pinot Noir and differences for Cabernet Sauvignon wines due to the length and manner of maceration and as a result of chaptalization. Statistical analysis revealed a more complex mouth-feel for the Pinot Noir wines; and an increase in the intensity of the astringent sub-qualities as a result of the addition of sugar to the wines. These findings have implications for how processing decisions, such as optimum grape maturity and vinification methods may affect red wine quality.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.