The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

Habitat loss and fragmentation are the main drivers of biodiversity loss, especially in the tropics, where the transformation of forested areas into agriculture is predicted to increase dramatically in the next five decades. Although several studies have elucidated the negative impacts of agriculture on biodiversity, recent work suggests that some agro-ecosystems, such as coffee plantations, are potential key environments for maintaining biodiversity and ecosystem services. This study evaluated the role of different coffee production types (sun-exposed, semi-shade and shade in polycultures or monocultures) on the bird communities associated with these agro-ecosystems in the tropical Andes of Colombia. It used a guild-specific approach and nonparametric statistical methods to identify the influence of particular environmental, ecological and landscape variables on the bird community assemblage and to assess potential changes in the species composition among management type. The potential responses of avifauna to fragmentation were studied from three different perspectives: i) from a patch-level point of view, evaluating the effect of local habitat factors (e.g. canopy cover, type of crop and crop management type); ii) from a species point of view, evaluating the role of species ecological traits (e.g. feeding habitat); and iii) from a landscape point of view, evaluating the effect of landscape configuration variables (e.g. patch area and perimeter length). The results indicated that polyculture and shade coffee crops host the most diverse avian communities and that guild representativeness varied among different coffee crop types. The type of coffee production type and the habitat characteristics associated with them seemed to have the greatest influences on families such as flycatchers, hummingbirds and wrens. Finally, coffee plantations can potentially contribute to the maintenance of bird diversity in anthropogenic landscapes; however these benefits are strongly influenced by the type of crop management. The maintenance of traditional coffee production (shade polyculture coffee) is recommended, and should be economically and socially encouraged. / text

Thèse (M.Sc.)--Université Laval, 2004. / Bibliogr.: f. 87-101. Publié aussi en version électronique.

Abstract Patterns of plant species composition and their relationships to environmental factors were investigated in Jhuokou River basin. 102, 20 ¡Ñ 20 m plots with woody stems ¡Ù1.0 cm diameter at breast height (DBH) data and 12 environmental variables were analysed by Two Way Indicator Species Analysis (TWINSPAN), Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA) to classify the vegetation types and determine the significant environmental variables that affect the distribution of vegetation. Classification and regression tree (CART) were then used to perform vegetation classification tree based on these significant variables. The vegetation classification result showed that 102 sampling plots can be classified into 9 vegetation types : 1. Daphniphyllum hlaucescens subsp. oldhamii - Cyclobalanopsis morii vegetation type ; 2. Neolitsea acuminatissima - Cyclobalanopsis morii vegetation type ; 3. Adinandra formosana - Lithocarpus lepidocarpus - Machilus thunbergii vegetation type ; 4. Elaeocarpus japonicus - Castanopsis cuspidate - Machilus thunbergii vegetation type ; 5. Ardisia quinquegona - Tricalysia dubia - Beilschmiedia erythrophloia vegetation type ; 6. Schefflera octophylla - Helicia formosana - Beilschmiedia erythrophloia vegetation type ; 7. Castanopsis formosana - Mallotus paniculatus - Schefflera octophylla vegetation type ; 8. Cyclobalanopsis glauca - Glochidion rubrum - Sapindus mukorossii vegetation type ; 9. Champereia manillana - Kleinhovia hospita - Murraya vegetation type. DCA and CCA distinguished 8 significant environmental variables from 12 measured variables. Altitude and warmth index were the most important variables in 8 significant environmental variables, but were highly correlated. When used vegetation classification tree to predict the position of the reference vegetation alliance, average accuracy was 56.9 %. The results indicated that the current data was still insufficient to predict the vegetation type at alliances level with environmental variables.

Cocaine- and amphetamine-regulated transcript (CART), as its name implies, was initially identified as an upregulated transcript in response to psychostimulant administration. Consequently, it has been posited to play a role in psychostimulant abuse and dependence. Spurred on by the finding that a polymorphism in the CART gene was associated with alcoholism, we initiated studies designed to elucidate the role of CART peptide in alcohol dependence. We first investigated the functional significance of CART peptide in alcohol dependence in vivo using a CART KO mouse. We found that CART KO mice had a significant decrease in ethanol consumption that could not be attributed to differences in total intake, taste perception, metabolism, or sensitivity to ethanol. In vitro we found that CART peptide facilitated NMDA receptor-mediated currents in central amygdala neurons. Given the emerging role of CART peptide in anxiety and stress, we decided to examine basal and stress-induced anxiety behaviors in CART KO mice. Under basal and acute stress conditions, CART KO mice did not differ in anxiety-like behaviors from WT mice; however, in response to a stressor, CART KO mice exhibited a potentiated corticosterone response. Using chronic intermittent ethanol exposure (CIE), we tested CART KO and WT mice for common signs of ethanol dependence including an escalation of volitional consumption and the presence of withdrawal-induced anxiety. We further investigated glutamatergic neuroadaptations within the central amygdala of CART KO and WT mice following CIE exposure and early withdrawal. CIE increased ethanol consumption and anxiety-like behaviors in mice of both genotypes but to a lower extent in CART KO mice. Electrophysiologically, CIE enhanced spontaneous excitatory postsynaptic currents in both genotypes and decreased the probability of presynaptic release in WT mice only. We believe that these electrophysiological neuroadaptations contribute to the development of ethanol dependence and may mediate withdrawal-induced anxiety behaviors. Overall, these studies indicate a role for CART peptide in alcohol dependence and specifically in modulating ethanol withdrawal-induced anxiety. / text

High Throughput Screening (HTS) is used in drug discovery to screen large numbers of compounds against a biological target. Data on activity against the target are collected for a representative sample of compounds selected from a large library. The goal of drug discovery is to relate the activity of a compound to its chemical structure, which is quantified by various explanatory variables, and hence to identify further active compounds. Often, this application has a very unbalanced class distribution, with a rare active class. <br /><br /> Classification methods are commonly proposed as solutions to this problem. However, regarding drug discovery, researchers are more interested in ranking compounds by predicted activity than in the classification itself. This feature makes my approach distinct from common classification techniques. <br /><br /> In this thesis, two AIDS data sets from the National Cancer Institute (NCI) are mainly used. Local methods, namely K-nearest neighbours (KNN) and classification and regression trees (CART), perform very well on these data in comparison with linear/logistic regression, neural networks, and Multivariate Adaptive Regression Splines (MARS) models, which assume more smoothness. One reason for the superiority of local methods is the local behaviour of the data. Indeed, I argue that conventional classification criteria such as misclassification rate or deviance tend to select too small a tree or too large a value of <em>k</em> (the number of nearest neighbours). A more local model (bigger tree or smaller <em>k</em>) gives a better performance in terms of drug discovery. <br /><br /> Because off-the-shelf KNN works relatively well, this thesis takes this promising method and makes several novel modifications, which further improve its performance. The choice of <em>k</em> is optimized for each test point to be predicted. The empirically observed superiority of allowing <em>k</em> to vary is investigated. The nature of the problem, ranking of objects rather than estimating the probability of activity, enables the <em>k</em>-varying algorithm to stand out. Similarly, KNN combined with a kernel weight function (weighted KNN) is proposed and demonstrated to be superior to the regular KNN method. <br /><br /> High dimensionality of the explanatory variables is known to cause problems for KNN and many other classifiers. I propose a novel method (subset KNN) of averaging across multiple classifiers based on building classifiers on subspaces (subsets of variables). It improves the performance of KNN for HTS data. When applied to CART, it also performs as well as or even better than the popular methods of bagging and boosting. Part of this improvement is due to the discovery that classifiers based on irrelevant subspaces (unimportant explanatory variables) do little damage when averaged with good classifiers based on relevant subspaces (important variables). This result is particular to the ranking of objects rather than estimating the probability of activity. A theoretical justification is proposed. The thesis also suggests diagnostics for identifying important subsets of variables and hence further reducing the impact of the curse of dimensionality. <br /><br /> In order to have a broader evaluation of these methods, subset KNN and weighted KNN are applied to three other data sets: the NCI AIDS data with Constitutional descriptors, Mutagenicity data with BCUT descriptors and Mutagenicity data with Constitutional descriptors. The <em>k</em>-varying algorithm as a method for unbalanced data is also applied to NCI AIDS data with Constitutional descriptors. As a baseline, the performance of KNN on such data sets is reported. Although different methods are best for the different data sets, some of the proposed methods are always amongst the best. <br /><br /> Finally, methods are described for estimating activity rates and error rates in HTS data. By combining auxiliary information about repeat tests of the same compound, likelihood methods can extract interesting information about the magnitudes of the measurement errors made in the assay process. These estimates can be used to assess model performance, which sheds new light on how various models handle the large random or systematic assay errors often present in HTS data.

High Throughput Screening (HTS) is used in drug discovery to screen large numbers of compounds against a biological target. Data on activity against the target are collected for a representative sample of compounds selected from a large library. The goal of drug discovery is to relate the activity of a compound to its chemical structure, which is quantified by various explanatory variables, and hence to identify further active compounds. Often, this application has a very unbalanced class distribution, with a rare active class. <br /><br /> Classification methods are commonly proposed as solutions to this problem. However, regarding drug discovery, researchers are more interested in ranking compounds by predicted activity than in the classification itself. This feature makes my approach distinct from common classification techniques. <br /><br /> In this thesis, two AIDS data sets from the National Cancer Institute (NCI) are mainly used. Local methods, namely K-nearest neighbours (KNN) and classification and regression trees (CART), perform very well on these data in comparison with linear/logistic regression, neural networks, and Multivariate Adaptive Regression Splines (MARS) models, which assume more smoothness. One reason for the superiority of local methods is the local behaviour of the data. Indeed, I argue that conventional classification criteria such as misclassification rate or deviance tend to select too small a tree or too large a value of <em>k</em> (the number of nearest neighbours). A more local model (bigger tree or smaller <em>k</em>) gives a better performance in terms of drug discovery. <br /><br /> Because off-the-shelf KNN works relatively well, this thesis takes this promising method and makes several novel modifications, which further improve its performance. The choice of <em>k</em> is optimized for each test point to be predicted. The empirically observed superiority of allowing <em>k</em> to vary is investigated. The nature of the problem, ranking of objects rather than estimating the probability of activity, enables the <em>k</em>-varying algorithm to stand out. Similarly, KNN combined with a kernel weight function (weighted KNN) is proposed and demonstrated to be superior to the regular KNN method. <br /><br /> High dimensionality of the explanatory variables is known to cause problems for KNN and many other classifiers. I propose a novel method (subset KNN) of averaging across multiple classifiers based on building classifiers on subspaces (subsets of variables). It improves the performance of KNN for HTS data. When applied to CART, it also performs as well as or even better than the popular methods of bagging and boosting. Part of this improvement is due to the discovery that classifiers based on irrelevant subspaces (unimportant explanatory variables) do little damage when averaged with good classifiers based on relevant subspaces (important variables). This result is particular to the ranking of objects rather than estimating the probability of activity. A theoretical justification is proposed. The thesis also suggests diagnostics for identifying important subsets of variables and hence further reducing the impact of the curse of dimensionality. <br /><br /> In order to have a broader evaluation of these methods, subset KNN and weighted KNN are applied to three other data sets: the NCI AIDS data with Constitutional descriptors, Mutagenicity data with BCUT descriptors and Mutagenicity data with Constitutional descriptors. The <em>k</em>-varying algorithm as a method for unbalanced data is also applied to NCI AIDS data with Constitutional descriptors. As a baseline, the performance of KNN on such data sets is reported. Although different methods are best for the different data sets, some of the proposed methods are always amongst the best. <br /><br /> Finally, methods are described for estimating activity rates and error rates in HTS data. By combining auxiliary information about repeat tests of the same compound, likelihood methods can extract interesting information about the magnitudes of the measurement errors made in the assay process. These estimates can be used to assess model performance, which sheds new light on how various models handle the large random or systematic assay errors often present in HTS data.

Electrical mobile cart,one of low-speed small four-wheel vehicles, is becoming unsubstituted in the aged society recently. As we know, wheel Steering System plays an important role in riding comforts, stability of handling, and safety. The purpose of this study is to develop a systematic design procedure for the steering system of low-speed small four-wheel vehicles. First, to investigate the basic requirements and characteristics of low-speed small four-wheel vehicles. Second, to generalize the essential characteristics and establish the requirement book of steering systems. Third, kinematic design the original steering system by design procedure. Finally, kinematic design the single-A suspension steering system by design procedure.

The White Lady (WL) is a mobile fast food takeaway eatery. The WL has been trading in Auckland City’s central business district for almost fifty years. The WL opens in the early evening and remains open until the early morning hours. At closing, the WL is towed to a storage area where it remains until this process is repeated. This daily pattern has occurred since the WL opened in 1948. Because of its longevity, the WL, and many of its stakeholders have experienced ongoing change as Auckland City has grown, and competition within fast food has increased. Thus, for many stakeholders, the WL is representative of their lives, a mirror of their reality and life experiences. Obviously, these realities and experiences are different for different stakeholders. In this thesis, I examine the contested “White Lady” (WL): the perceptions and the social meanings that its stakeholder groups attribute to it. This thesis illuminates differences and similarities within stakeholder viewpoints and in doing so defines that pie carts like the WL are a valid part of New Zealand’s culinary and social cultures Ultimately, this thesis provides a platform of knowledge from which stakeholders and others can come to understand and know the differing and similar views that other stakeholder groups hold. With this in mind, this research ranges in scope from the examination of city administration to the symbolism associated with the (WL) by some of its stakeholders. Therefore, this research is founded within socio-historic constructs: the history of fast food and, the similarities that this history holds to today’s WL operation. The contextualisation of hospitality within “three domains” (Lashley, 2004, p.13) aids in defining the WL as well as recognising the competitive growth of New Zealand’s fast food industry. This research suggests that fast food growth and subsequent competition have had negative impacts upon many small fast food outlets including the WL. viii The growth of fast food has facilitated a “slow food” (Jones, Shears, Hiller Comfort and Lowell, 2003, p. 298) movement. This movement coupled with the hierarchy of food typologies, adds a Saussurian overlay and sociological discourse to this work. This overlay clarifies for the reader Bourdieu’s (1984) position that all food is reflective of class status. Within postmodernist constructs and the rise of the individual, (and the consequent opportunity to hear ‘voices from the margins’), movement within class and individuality within New Zealand’s wider culture has occurred. Social change therefore, has facilitated some of the issues within WL contestation. In highlighting Bourdieu’s (1984) concept, the “binary opposition” (Levi-Strauss, 1981, as cited in Adamenko, 2007, p.27) inherent within food hierarchies and, as often expressed within the media, is examined. This examination reveals that while the media inform, this information often contributes to the polarisation of opinion that facilitates the formation of contested viewpoints by WL stakeholders. It is against a backdrop of compliance need, the absence of an official street trading policy, the differing views of stakeholders, and the intensification of competition in fast food, coupled with a lacuna in the knowledge base of younger Auckland residents regarding the WL that this research finds its voice.

The White Lady (WL) is a mobile fast food takeaway eatery. The WL has been trading in Auckland City’s central business district for almost fifty years. The WL opens in the early evening and remains open until the early morning hours. At closing, the WL is towed to a storage area where it remains until this process is repeated. This daily pattern has occurred since the WL opened in 1948. Because of its longevity, the WL, and many of its stakeholders have experienced ongoing change as Auckland City has grown, and competition within fast food has increased. Thus, for many stakeholders, the WL is representative of their lives, a mirror of their reality and life experiences. Obviously, these realities and experiences are different for different stakeholders. In this thesis, I examine the contested “White Lady” (WL): the perceptions and the social meanings that its stakeholder groups attribute to it. This thesis illuminates differences and similarities within stakeholder viewpoints and in doing so defines that pie carts like the WL are a valid part of New Zealand’s culinary and social cultures Ultimately, this thesis provides a platform of knowledge from which stakeholders and others can come to understand and know the differing and similar views that other stakeholder groups hold. With this in mind, this research ranges in scope from the examination of city administration to the symbolism associated with the (WL) by some of its stakeholders. Therefore, this research is founded within socio-historic constructs: the history of fast food and, the similarities that this history holds to today’s WL operation. The contextualisation of hospitality within “three domains” (Lashley, 2004, p.13) aids in defining the WL as well as recognising the competitive growth of New Zealand’s fast food industry. This research suggests that fast food growth and subsequent competition have had negative impacts upon many small fast food outlets including the WL. viii The growth of fast food has facilitated a “slow food” (Jones, Shears, Hiller Comfort and Lowell, 2003, p. 298) movement. This movement coupled with the hierarchy of food typologies, adds a Saussurian overlay and sociological discourse to this work. This overlay clarifies for the reader Bourdieu’s (1984) position that all food is reflective of class status. Within postmodernist constructs and the rise of the individual, (and the consequent opportunity to hear ‘voices from the margins’), movement within class and individuality within New Zealand’s wider culture has occurred. Social change therefore, has facilitated some of the issues within WL contestation. In highlighting Bourdieu’s (1984) concept, the “binary opposition” (Levi-Strauss, 1981, as cited in Adamenko, 2007, p.27) inherent within food hierarchies and, as often expressed within the media, is examined. This examination reveals that while the media inform, this information often contributes to the polarisation of opinion that facilitates the formation of contested viewpoints by WL stakeholders. It is against a backdrop of compliance need, the absence of an official street trading policy, the differing views of stakeholders, and the intensification of competition in fast food, coupled with a lacuna in the knowledge base of younger Auckland residents regarding the WL that this research finds its voice.