Synthesis of isotope-labelled methoxypyrazine compounds as internal standards and quantitative determination of aroma methoxypyrazines in water and wines by solid-phase extraction with isotope dilution-GC-MS /

An efficient way of synthesizing the deuterium labelled analogues of three methoxypyrazine compounds: 2-d3-methoxy-3-isopropylpyrazine, 2-d3-methoxy-3- isobutylpyrazine, and 2-d3-methoxy-3-secbutylpyrazine, has been developed. To confirm that the deuterium labels had been incorporated into the expected positions in the molecules synthesized, the relevant characterization by NMR, HRMS and GC/MS analysis was conducted. Another part of this work involved quantitative determination of methoxypyrazines in water and wines. Solid-phase extraction (SPE) proved to be a suitable means for the sample separation and concentration prior to GC/MS analysis.Such factors as the presence of ethanol, salt, and acid have been investigated which can influence the recovery by SPE for the pyrazines from the water matrix. Significantly, in this work comparatively simple fractional distillation was attempted to replace the conventional steam distillation for pre-concentrating a sample with a relatively large volume prior to SPE. Finally, a real wine sample spiked with the relevant isotope-labelled methoxypyrazines was quantitatively analyzed, revealing that the wine with 10 beetles per litre contained 138 ppt of 2-methoxy-3-isopropylpyrazine. Interestingly, we have also found that 2-methoxy-3-secbutylpyrazine exhibits an extremely low detection limit in GC/MS analysis compared with the detection limit of the other two methoxypyrazines: 2- methoxy-3-isopropylpyrazine and 2-methoxy-3-isobutylpyrazine.

Report of the Committee appointed to inquire into the Present condition and distribution of the flags, standards and colors, which have been taken by the forces of the United States from their enemies, and whether it would be expedient to make any provision in relation to them.--

Adam Seybet, Chairman.

A case study of the Ottawa Valley whitewater rafting industry : standards and risks

This qualitative case study identifies and discusses the standards and risk management practices of the Ottawa Valley whitewater rafting industry and the impacts of the government enforced Special-purpose Vessels Regulations are discussed. Data collection occurred using a single case study design, which included interviews and document analysis. This study found that internal, industry, and actual standards are influenced through a variety of sources. These standards were found to affect the risk management practices of commercial whitewater rafting providers. In general, these standards promoted a high level of risk management within the Ottawa Valley rafting industry. The Special-purpose Vessels Regulations were found to be non-influential in raising the risk management standards of the Ottawa Valley whitewater rafting industry.

Fitness to stand trial : how discourses of rationality, reasonableness and culpability inform the law and psychiatry to create standards of normal

The objective of this thesis is to demonstrate the importance of the concepts of rationality, reasonableness, culpability and autonomy that inform and support our conception of both the person and the punishable subject. A critical discourse analysis tracing these concepts through both the law and psychological tools used to evaluate the fitness of a person reveals that these concepts and their implied values are inconsistently applied to the mentally disordered who come into conflict with the law. I argue that the result of this inconsistency compromises a person's autonomy which is a contradiction to this concept as a foundational principle of the law. Ultimately, this thesis does not provide a solution to be employed in policy making, but its analysis leaves open possibilities for further exploration into the ways legal and social justice can be reconciled.

The Flags, Standards and Colors Taken by the Forces of the United States

Full Title: Report of the Committee appointed to inquire into the Present condition and distribution of the flags, standards and colors, which have been taken by the forces of the United States from their enemies, and whether it would be expedient to make any provision in relation to them Adam Seybet, Chairman. Exhibit folded at end of text. February 4, 1814. Read, and committed to a committee of the whole House on Monday next. Printed by A and G Way

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Ethics, tourists and the environmental practices of the North American cruise ship industry: a comparison study of the ethical standards of Alaskan and Caribbean cruise ship tourists

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999 R43 S54 2005

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.