Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise.

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise.

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

<p>We consider one transaction costs model which was suggested by Cetin, Jarrow and Protter (2004) for an illiquid market. In this case the hedging strategy of programming traders can affect the assets prises. We study the corresponding partial differential equation (PDE) which is a non-linear Black-Scholes equation for illiquid markets. We use the Lie group analysis to determine the symmetry group of this equations. We present the Lie algebra of the Lie point transformations, the complete symmetry group and invariants. For different subgroups of the main symmetry group we describe the corresponding invariants. We use these invariants to reduce the PDE under investigation to ordinary differential equations (ODE). Solutions of these ODE's are subgroup-invariant solutions of the non-linear Black-Scholes equation. For some classes of those ODE's we find exact solutions and studied their properties.</p><p>% reduce non-linear PDE to ODE's. To some ODE's we find exact solutions.</p><p>%In this work we are studying one model for pricing derivatives in illiquid market. We discuss it structure and properties. Make a symmetry reduction for the PDE corresponding our model.</p>

We consider one transaction costs model which was suggested by Cetin, Jarrow and Protter (2004) for an illiquid market. In this case the hedging strategy of programming traders can affect the assets prises. We study the corresponding partial differential equation (PDE) which is a non-linear Black-Scholes equation for illiquid markets. We use the Lie group analysis to determine the symmetry group of this equations. We present the Lie algebra of the Lie point transformations, the complete symmetry group and invariants. For different subgroups of the main symmetry group we describe the corresponding invariants. We use these invariants to reduce the PDE under investigation to ordinary differential equations (ODE). Solutions of these ODE's are subgroup-invariant solutions of the non-linear Black-Scholes equation. For some classes of those ODE's we find exact solutions and studied their properties. % reduce non-linear PDE to ODE's. To some ODE's we find exact solutions. %In this work we are studying one model for pricing derivatives in illiquid market. We discuss it structure and properties. Make a symmetry reduction for the PDE corresponding our model.

Educators and researchers who study college alcohol use have explored shaping the campus environment as a method to positively influence college students' decisions regarding alcohol use. Existing literature has suggested that the interaction between the college students and the campus environment affects students' behavior (Goree & Szalay, 1996; Strange & Banning, 2001). The purpose of this study was to analyze how college and university viewbooks appeal to the different underlying dispositions of college students (non drinker vs. frequent drinker dispositions). The present study was also intended to identify whether college and university viewbooks are a part of the campus environment that affects students' behavioral decisions. The purposive sample included 51 college and university viewbooks from four different regions, six Carnegie Classifications, and an over-sampling of historically Black institutions. The findings revealed that college and university viewbooks appeal more to the non drinker dispositions than to the frequent drinker disposition. The findings also call into question previous scholarship suggesting that university recruitment materials have an influence on college student behaviors. / Master of Arts

In functional magnetic resonance imaging (fMRI) studies, inter-subject anatomical variability of the human brain has been a major challenge in finding reliable functional/anatomical correspondences. Assessment of brain-behavior relations involves a series of geometrical/statistical operations on brain images to minimize such inter-subject variability, so that group maps of brain activity relative to brain anatomy can be developed. Various methods of image registration, segmentation, and analysis have been proposed for mapping functional activity on to anatomical atlases of the brain. The two most common techniques that have been widely accepted and used by neuroimaging scientists are volume-based (VB) analysis using group registration methods and region-of-interest (ROI)-based methods using automated segmentation algorithms or macro/microanatomical probabilistic atlases for labeling. Nevertheless, the analysis results based on these techniques are significantly affected by the accuracy of the selected segmentation and/or registration methods. Furthermore, conventional fMRI data analysis techniques (VB, and ROI-based methods) mainly rely on the assumption that brain processes are common and universal among individual humans; however, besides anatomical differences, there also exist cognitive and behavioral variability among individuals due to differential engagement of brain networks even when performing an identical cognitive task. In this thesis, I have assessed the impact of anatomy-based alignment techniques (VB, and ROI-based methods) on sensitivity of fMRI data group analysis. I evaluated the effect of the type of inter-subject registration used and related factors on sensitivity of group-level fMRI data analysis. Furthermore, I have also assessed the goodness of fit of probabilistic maps by proposing an evidence-based framework for evaluation of probabilistic maps. As a test model, I have selected the human auditory cortex. Auditory cortex is an interesting yet challenging case with substantial inter-individual functional/anatomical variability. For the sake of ROI-based method of analysis, I have proposed a novel approach for automatic segmentation of Heschl's gyrus, which is the landmark for primary auditory cortex. Finally, in order to assess the impact of inter-subject variability in anatomy on functional organization, I analyze data from an fMRI study, which demonstrates that the degree to which anatomical registration compensates for functional variability depends on the brain region activated. / Thesis (Ph.D, Computing) -- Queen's University, 2010-04-29 07:07:55.77

In der aktuellen Performance-Management-Forschung wurden bereits eine Vielzahl von Einflussfaktoren untersucht, die eine zielgerichtete Verwendung von Kennzahlen beeinflussen. Verwaltungskultur spielte hierbei nur eine nachgeordnete Rolle. Die vorliegende Untersuchung verwendet die Daten einer Umfrage in allen kreisfreien Städten Deutschlands, um den Zusammenhang zwischen verschiedenen Kulturtypen und der Verwendung von Kennzahlen zu untersuchen. Als Analyseschema für Verwaltungskultur wird die Grid/Group-Analysis verwendet. Die Ergebnisse sind zum Teil überraschend. Individualistische Kulturen scheinen einen negativen, hierarchistische Kulturen einen positiven Einfluss zu haben. Dennoch wird das Fehlen eines geeigneten Operationalisierungsschemas bemängelt. / The current research on performance management considered several factors influencing the purposeful usage of performance data. Administrative culture is a rather neglected one. This work uses the data from a German-wide survey of all cities with county-status to show the relation between different groups of culture and the usage of performance data. Therefore, the Grid/Group-Analysis is used to categorize administrative culture. The results are partly surprising as individual culture is negatively and hierarchical culture is positively related to the dependent variable. Nevertheless, the missing of a useful operationalization scheme is criticized.

The problem of this study was to determine whether group interaction can bring about change in sociometric status, self-concept, and perceived group characteristics with respect to nursing personnel.

The modelling power of Itˆo integrals has a far reaching impact on a spectrum of diverse fields. For example, in mathematics of finance, its use has given insights into the relationship between call options and their non-deterministic underlying stock prices; in the study of blood clotting dynamics, its utility has helped provide an understanding of the behaviour of platelets in the blood stream; and in the investigation of experimental psychology, it has been used to build random fluctuations into deterministic models which model the dynamics of repetitive movements in humans. Finding the quadrature for these integrals using continuous groups or Lie groups has to take families of time indexed random variables, known as Wiener processes, into consideration. Adaptations of Sophus Lie’s work to stochastic ordinary differential equations (SODEs) have been done by Gaeta and Quintero [1], Wafo Soh and Mahomed [2], ¨Unal [3], Meleshko et al. [4], Fredericks and Mahomed [5], and Fredericks and Mahomed [6]. The seminal work [1] was extended in Gaeta [7]; the differential methodology of [2] and [3] were reconciled in [5]; and the integral methodology of [4] was corrected and reconciled in [5] via [6]. Symmetries of SODEs are analysed. This work focuses on maintaining the properties of the Weiner processes after the application of infinitesimal transformations. The determining equations for first-order SODEs are derived in an Itˆo calculus context. These determining equations are non-stochastic. Many methods of deriving Lie point-symmetries for Itˆo SODEs have surfaced. In the Itˆo calculus context both the formal and intuitive understanding of how to construct these symmetries has led to seemingly disparate results. The impact of Lie point-symmetries on the stock market, population growth and weather SODE models, for example, will not be understood until these different results are reconciled as has been attempted here. Extending the symmetry generator to include the infinitesimal transformation of the Wiener process for Itˆo stochastic differential equations (SDEs), has successfully been done in this thesis. The impact of this work leads to an intuitive understanding of the random time change formulae in the context of Lie point symmetries without having to consult much of the intense Itˆo calculus theory needed to derive it formerly (see Øksendal [8, 9]). Symmetries of nth-order SODEs are studied. The determining equations of these SODEs are derived in an Itˆo calculus context. These determining equations are not stochastic in nature. SODEs of this nature are normally used to model nature (e.g. earthquakes) or for testing the safety and reliability of models in construction engineering when looking at the impact of random perturbations. The symmetries of high-order multi-dimensional SODEs are found using form invariance arguments on both the instantaneous drift and diffusion properties of the SODEs. We then apply this to a generalised approximation analysis algorithm. The determining equations of SODEs are derived in an It¨o calculus context. A methodology for constructing conserved quantities with Lie symmetry infinitesimals in an Itˆo integral context is pursued as well. The basis of this construction relies on Lie bracket relations on both the instantaneous drift and diffusion operators.