Mass spectral studies of some pentafluorophenyl derivatives of group V /|nby Andrew Tanner Rake. -- 260 St. Catharines, Ont. : [s. n.],

The mass spectra of compounds of t he series (C6F5 )3-n MP~ (n = 1,2,3, M = P and As ), (C6F5>3Sb, Ph) Sb and (C6F5 )2SbPh have been studied in detail and the important modes of fragmentation were e1ucidated, a ided by metastable ions. Various trends attributed to the central atom and or the . substituent groups have been noted and, where applicable, compared to recent studies on related phenyl and pentafluorophenyl compounds of groups IV and V. The mass spectra of fluorine containing organometallic compounds exhibit characteristic migrations of fluorine to t he central atom, giving an increasing abundance of MF+, MF2+' and RMF+ (R = Ph or C6F5) ions on descending the group_ The mass spectra of pentafluorophenyl , antimony, and arsenic compounds show a greater fragmentation of the aromatic ring than those of phosphorus. The mixed phenyl pentafluorophenyl derivatives show a characteristic pattern depending on the number of phenyl grm.lps present but show t he general characteristics of both the tris(phenyl) and tris(pentafluorophenyl) compounds. The diphenyl pentafluorophenyl der ivatives show the loss of biphenyl ion as the most import ant step, the los s of phenyl t o give the i on PhMC6F5 + being of secondary importance. The ,bis(pentafluorophenyl) phenyl derivatives fragment primarily by loss of PhC6F5 to give C6F5M+ ions, the abundance of t hese increasing r apidly from phosphorus to arsenic. This species then, exhibits a characteristic fragmentation observed in the tris(penta- fluorophenyl ) compounds. However, the abundance of (C6F5)2M+ species in these compounds i s small. I ons of the type C6H4MC6F4 + and tetrafluorobiphenylene ions C6H4C6F4 + also are observed on substitution of a phenyl group for a penta- fluorophenyl group. The fully fluorinated species (C6F4)2M+ is not observed, although octafluorobiphenylene ions , (C6F4)2+' are evident in several spectra . The appearance potentials of the major ions were obtatned from the ionisation efficiency curves. Attempts were made to correlate these to the effect of the central atom in substituent groups, but the large errors involved prevented the reaching of quantitative conclusions, although it would appear that the electron is removed from the ligand in the ionisation of t he parent molecule .

Synthesis and spectral studies of methyl heterocylic systems

2-Carboxy-2?-methyldiphenyl sulfide was prepared by the Ullmann reaction and cyclodehydrated by sulfuric acid to afford 4-methylthioxanthone. 1-Methylthioxanthone was separated from the reaction mixture obtained upon cyclodehydration of 2-carboxy-3f-methyldiphenyl sulfide. In addition, 1-, 2-, 3- and 4-methylthioxanthone 10,10-dioxides were synthesized by oxidation of the corresponding thioxanthones. o-, m- and p-N-Tolylanthranilic acids were prepared by the Ullmann reaction and used as precursors for the preparation of 1-, 2- and 4- methyl-9-chloroacridine and finally 1-, 2-, 3- and 4-methylacridone. High resolution, 60 MHz PMR spectra were obtained on the four monomethyl isomers of xanthone, thioxanthone, thioxanthone 10,10-dioxide and acridone, and on 1-, 2- and 4-methyl-9-chloroacridine. For some compounds, coupling of all three different aromatic protons to the methyl was observed, two of the couplings typically being smaller than the third. With the large (ortho) coupling being on the order of 0.5 to 1.0 Hz, it was necessary to decouple the aromatic part of the spectrum. The magnitude of the ortho benzylic constant may be related to an incomplete Tr-bond delocalization in the molecules.

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.

Spectral Reflectance Characteristics of Kimberlite Indicator Minerals and Other Selected Mineral Grains

High chromium content in kimberlite indicator minerals such as pyrope garnet and diopside is often correlated with the presence of diamonds. In this study, kimberlite indicator minerals were examined using visible light reflectance spectroscopy to determine if chromium content can be correlated with spectral absorption features. The depth of absorption features in the visible spectral region were correlated with the molecular percentage of chromium and other first series transition metal elements obtained by electron microprobe data. In the visible part of the spectrum, chromium is evident by 3 absorption features in the pyrope reflectance spectrum; one isolated and narrow feature at the wavelength 689 nm was used to correlate with the chromium mol %. The isolation of this feature in the pyrope spectra is advantageous since it is not directly affected by other proximal absorption bands that could be caused by other transition metals. Analysis of the feature indicates that as grain volume increases the depth of the absorption feature will also increase. Clustering grain volumes into fractions yields better correlation between absorption depth and mol % chromium. Other types of garnet (almandine, grossular, spessartine) and kimberlite indicator minerals (olivine, diopside, chromite, ilmenite) were analyzed to determine if other absorption features could be used to predict the proportion of specific transition metal elements. Diopside in particular illustrates the same isolated chromium absorption feature as pyrope and may indicate mol percent but needs further study with larger sample sets.

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.