Dissertations / Theses on the topic 'Factor Analysi'

Lo strumento “Analisi di Rischio” per la valutazione dei siti contaminati è in uso da alcune decine di anni ed ha ricevuto un forte impulso negli USA con il Programma Superfund ed in Europa con l’emergere del problema del risanamento di un numero molto ampio di siti e con l’avvio di programmi di collaborazione internazionale. La procedura di analisi di rischio può essere condotta in modalità diretta (forward mode) o inversa (backward mode). La modalità diretta permette di stimare il rischio sanitario per il recettore esposto, sia posto in prossimità del sito (on-site) che ad una certa distanza (off-site), conoscendo la concentrazione in corrispondenza della sorgente di contaminazione. Avendo invece fissato il livello di rischio per la salute ritenuto accettabile per il recettore esposto, la modalità inversa permette il calcolo della massima concentrazione in sorgente compatibile con la condizione di accettabilità del rischio. L’Agenzia Italiana per la Protezione dell’Ambiente ed i Servizi Tecnici (ora ISPRA, già APAT) ha recentemente sviluppato delle linee guida che forniscono una procedura standard per l’applicazione dell’analisi di rischio ai siti contaminati. In particolare sono fornite delle indicazioni tecniche per l’esecuzione di un livello 2 di analisi di rischio; tale livello di analisi si basa su alcune assunzioni semplificative: • il trasporto dei contaminanti viene descritto attraverso equazioni analitiche che però non sono state validate né attraverso un confronto con dati sperimentali né attraverso un confronto con i risultati forniti da modelli numerici. • Non viene considerata l’attenuazione della contaminazione attraverso il fenomeno biodegradativo. • La catena alimentare non viene considerata come possibile via di esposizione alla contaminazione. Inoltre, quando il lavoro di dottorato è stato iniziato, la prima revisione delle linee guida fornite dall’APAT comprendeva unicamente la procedura per l’applicazione dell’analisi di rischio in modalità diretta. Lo scopo del presenta lavoro di dottorato, che è stato sovvenzionato dall’APAT, è stato quello di implementare la procedura di analisi di rischio proposta nelle linee guida andando a sviluppare le tematiche viste precedentemente. Nel capitolo 1, dopo un breve introduzione sui fondamenti dell’analisi di rischio, è stata sviluppata una procedura per il calcole delle concentrazioni soglia di rischio, cosi come richiesto dalla nuova legislazione italiana (D.M. 152/06) Nel capito 2 sono state analizzate le procedure e le assunzioni sulla catena alimentare vegetale proposte da alcuni documenti tecnici e software, è stata quindi proposta una procedura standard da implementare nella struttura dell’analisi di rischio. Nel capitolo 3 sono stati confrontati i risultati ottenuti dall’applicazione delle equazioni analitiche per il trasporto dei contaminanti, utilizzate nel livello 2 di analisi, con quelli forniti dai modelli numerici di trasporto. in particolare sono stati analizzati due fattori di trasporto: • il fattore di lisciviazione, LF, che descrive il trasporto dei contaminanti nella zona insatura; • il fattore di attenuazione laterale in falda, DAF, che descrive il trasporto dei contaminanti nella zona satura. Nel capitolo 4 è stata studiata l’influenza del fenomeno biodegradativo in zona insatura sull’intrusione indoor dei contaminanti, in modo da capire quanto tale fenomeno sia influente nella volatilizzazione in ambienti confinati. Infine, durante le attività di dottorato, è stato sviluppato un software di analisi di rischio basato sulle linee guida APAT integrato con un database contenente le caratteristiche chimico-fisiche e tossicologiche dei contaminanti.
The instrument 'Risk Analysis' for the assessment of contaminated sites has been used for several years and has received a strong boost in the U.S. in the framework of the Superfund Program and in Italy has gained wider relevance after the introduction of new environmental legislation. Risk analysis is currently the most advanced procedure for the evaluation of the degree of contamination of an area and to define the priorities and modalities of intervention in the site itself. The risk analysis procedure can be conducted in forward mode or backward mode. The forward mode allows estimating the health risk for the exposed receptor, whether located near the site (on-site) or at some distance (off-site), given the concentration at the source of contamination. Having instead set the level of risk to health considered acceptable to the exposed receptor, the backward mode allows the calculation of the highest concentration at the source compatible with the condition of acceptability of the risk, i.e. the so-called site-specific target level. The Italian Agency for Environmental Protection (now ISPRA, formerly APAT) has recently issued a guideline document that provides a standard procedure for application of risk analysis to contaminated sites. This guideline document is based on a Tier 2 risk analysis approach, which is based on a series of simplifying assumptions: • Contaminant transport is described through the analytical equations which are not validated with experimental data or with numerical model results. • Attenuation of contaminant through biodegradation phenomena is not accounted for; • Exposure to contaminants through the food chain is neglected. Besides, when this thesis was started, the document included only the procedure for applying the risk analysis in forward mode. The work performed within this PhD thesis, supported by APAT, was to upgrade the procedure for application of risk analysis to contaminated sites, including also the unresolved issues listed above. In chapter 1, after a short background on the fundamentals of risk analysis, a procedure to obtain the threshold risk concentration, as requested by the new Italian legislation (D.M. 152/06) was developed. In chapter 2 the assumption and the procedure suggested by several technical documents and software on the exposure through the food chain were analyzed and a standard procedure was suggested for implementation in the risk analysis framework. In chapter 3 the results of the analytical equation selected for tier II risk analysis were compared with those provided by numerical transport models. The validation of the equations for Tier 2 risk analysis was performed on two key transport factors: • The Leaching Factor, LF, which describes the contaminant transport through the vadose zone; • The Dilution Attenuation Factor, DAF, which describes the contaminant transport through the acquifer. In chapter 4 the influence of the vadose zone biodegradation related to the indoor vapour intrusion was studied in order to understand how this phenomena can influence the chemical volatilization to indoor environment. Besides, in the framework of the PhD activities, a risk analysis software, based on the APAT guidelines, was developed and linked to a database of the chemical and toxicological properties of the contaminants, properly developed.

L’obiettivo principale di questa tesi è di introdurre un modello non lineare, “Feedforward Neural Network-Dynamic Factor” (FNN-DF), per la previsione di serie macroeconomiche utilizzando un numero elevato di variabili. La tecnica usata per riassumere le variabili in un piccolo numero di fattori è il “Generalized Dynamic Factor Model” (GDFM), mentre le reti neurali di tipo “Feedforward” sono utilizzate per rappresentare la non-linearità. Comunemente nella letteratura del GDFM, le previsioni sono effettuate con modelli lineari. Tuttavia tali tecniche spesso non sono correttamente specificate e le previsioni risultanti forniscono soltanto un’approssimazione alla migliore previsione possibile. Nel tentativo di ottener previsioni più accurate, il modello FNN-DF è stato introdotto. Per determinare l'utilità pratica del modello, sono stati svolti diversi esercizi di previsione per otto variabili mensili dell'economia degli Stati Uniti per differenti orizzonti previsivi a 1-, 3 -, 6 -, 9 e 12 mesi. I fattori sono stati stimati utilizzando un data set di 131 variabili mensili dell’economia degli Stati Uniti. Lo studio empirico mostra che il modello FNN-DF ha buone capacità di prevedere le variabili oggetto di studio soprattutto nel periodo antecedente l’inizio della “Great Moderation”, cioè il 1984. In seguito il FNN-DF ha la stessa accuratezza in previsione rispetto al modello di riferimento.
This dissertation aims to introduce a nonlinear model to forecast macroeconomic time series using a large number of predictors, namely the Feedforward Neural Network - Dynamic Factor Model (FNN-DF). The technique used to summarize the predictors in a small number of factors is Generalized Dynamic Factor Model, while the method used to capture nonlinearity is artificial neural networks, specifically Feedforward Neural Network. Commonly in GDFM literature, forecasts are made using linear models. However linear techniques are often misspecified and the resulting forecasts provide only a poor approximation to the best possible forecast. In an effort to address this issue, the technique we propose is FNN-DF. To determine the practical usefulness of the model, we conducted several pseudo forecasting exercises on 8 series of the United States economy. The series we were interested in forecasting were grouped in real and nominal categories. This method was used to construct the forecasts at 1-, 3-, 6-, 9 and 12-month horizons for monthly U.S. economic variables using 131 predictors. The empirical study shows that FNN-DF has good ability to predict the variables under study in the period before the start of the "Great Moderation", namely 1984. After 1984, FNN-DF has the same accuracy in forecasting with respect to the benchmark.

In ambito psicometrico, un tratto latente è una caratteristica non osservabile di un individuo. Per misurare tale tratto si ricorre comunemente all’uso di questionari che attraverso le risposte ad una serie di domande produce un punteggio totale indicativo di tale tratto latente. Il punto di partenza di questo lavoro è stata l’analisi di dati raccolti tra operatori sanitari di un grosso ospedale di Milano che hanno affrontato la pandemia COVID-19 e il cui benessere psicologico è stato valutato attraverso scale validate (GHQ-12, IES-r, GAD-7). In questo contesto, la Teoria Classica dei Test (TCT) e l’Item Response Theory (IRT) rappresentano le due teorie principali per analizzare in modo opportuno test basati su questionari. In psicometria l’uso della TCT è molto diffuso, mentre l’IRT sta avendo un crescente utilizzo solo recentemente. Partendo da una serie di quesiti clinici nati all’interno del contesto sanitario in cui tale lavoro è stato svolto, abbiamo analizzato preliminarmente i dati relativi a questionari di 990 operatori sanitari arruolati tra Luglio 2020 e Luglio 2021 attraverso un’analisi dei fattori di rischio per la compromissione del benessere psicologico. Abbiamo poi analizzato le scale psicometriche intendendole come strumenti di misurazione di un tratto latente. Abbiamo mostrato l’utilità e i benefici della teoria IRT applicandola ad una delle scale somministrate nei questionari, la General Health Questionnaire-12 (GHQ-12), un test ampiamente usato per valutare la salute mentale di un individuo. Attraverso l’IRT abbiamo indagato tre principali aree di interesse. Tramite la versione multidimensionale dei modelli IRR abbiamo indagato la dimensionalità, mostrando che la salute mentale degli operatori sanitari è stata affetta principalmente da due fattori misurati dalla GHQ-12, che hanno impattato in modo diverso il benessere psicologico. Da un lato è emerso un chiaro e diffuso stato di stress e preoccupazione, dall’altro molti individui hanno provato una sensazione di non avere un ruolo utile tramite proprio lavoro e di non essere in grado di prendere decisioni. Tramite l’analisi del DIF (differential item functioning) abbiamo evidenziato le differenze nelle risposte al questionario in sottogruppi: tra chi ha lavorato direttamente o indirettamente a contatto con pazienti COVID-19 e tra chi è stato arruolato prima o dopo la campagna vaccinale. Attraverso la versione di modelli IRT basata su classi latenti abbiamo mostrato come certe tipologie di risposta riescono a prevedere l’esito finale dello screening psicologico dei soggetti, che prevede in tutto tre fasi. Infine, nell’ambito della statistica robusta, abbiamo proposto un algoritmo basato sul metodo forward search per l’individuazione di risposte atipiche nei questionari e lo abbiamo testato ad un caso particolare di modelli IRT, il modello di Rasch.
A latent trait is an unobservable psychological attribute of an individual. A common way to measure it relies on the use of questionnaires, which collect person’s responses to test items and calculate a score representing the underlying latent trait. We analysed data collected among health care workers (HCWs) who faced COVID-19 pandemic and whose psychological wellbeing was evaluated through psychometric scales (GHQ-12, IES-r, GAD-7). Classical test theory (CTT) and item response theory (IRT) represent two different measurement frameworks. Starting from a series of clinical questions arising from a specific medical environment, objectives of this work were to explore the clinical implication of the tests’ outcome, performed through risk factor analysis for psychological impairment on a sample of 990 HCWs, and to evaluate the structure of the scales intended as measurement tools. We provided demonstration of the utility of IRT as compared with CTT-based method through the analysis of General Health Questionnaire – 12 (GHQ-12 questionnaire), a valid and reliable tool widely used to measure mental health. IRT-based techniques enable us to delve in several areas of interest of GHQ-12 questionnaire. Through the multidimensional version of IRT models we assess its dimensionality, finding that HCWs' psychological wellbeing was affected by general discomfort and stress together with a strong feeling of uselessness and inability to make decisions. Differential item functioning (DIF) detection performed with IRT investigated possible differences in responses between subjected directly or indirectly involved with COVID-19 patients and between workers enrolled before or after vaccination campaign, showing how their psychological status was affected by such circumstances. The discrete version of IRT (based on latent class analysis) allowed to determine how some response pattern may predict individual’s outcome of psychological wellbeing evaluation which consisted in three steps. Lastly, we explored the issue of atypical response pattern detection applying Forward Search algorithm to Rasch model.

Il presente lavoro ha l’obiettivo di fornire una base di informazioni sulle relazioni tra la distribuzione spaziale e la variazione genetica in popolazioni del lepidottero Parnassius mnemosyne. Un totale di 172 individui provenienti da 58 località, rappresentative dell’intero areale della specie, sono stati caratterizzati per le sequenze di due geni mitocondriali (COI e ND5) e un gene nucleare (EF-1α). Un campione di 397 individui da 16 località italiane (Alpi centro-orientali, Appennino Centrale e Sicilia) è stato analizzato impiegando 5 loci microsatellite appositamente sviluppati. I risultati hanno messo in luce evidenze genetiche della lunga e complessa storia geografica di questa specie e l’analisi della distribuzione geografica degli aplotipi mitocondriali ha permesso di ricostruirne con una certa accuratezza le tappe più significative. È chiaro che P. mnemosyne aveva raggiunto una distribuzione Eurasiatica simile all’attuale già alla fine del Pleistocene inferiore. La presenza di aplogruppi fortemente differenziati in aree geografiche distinte indica chiaramente che periodi di grande frammentazione dell’areale hanno contraddistinto il Pleistocene medio e superiore. In particolare, le popolazioni dell’Europa sud-occidentale (Pirenei, penisola Italiana, Sicilia, Alpi Occidentali) e quelle dell’Europa nord-orientale (dalle Alpi Orientali, e la Penisola Balcanica fino agli Urali) appaiono caratterizzate da aplotipi mitocondriali altamente divergenti (l’inizio della divergenza è databile a oltre 1 milione di anni fa) e da sequenze nucleari distinte. I due gruppi meritano senz’altro di essere considerati come differenti Unità Evolutivamente Significative (ESUs) e rappresentano probabilmente specie distinte sensu Mayr. Popolazioni portatrici dei due diversi aplogruppi sono state osservate a poche decine di chilometri di distanza nelle Alpi centro-orientali italiane e non mostrano traccia di introgressione mitocondriale e nucleare. L’impiego del DNA microsatellite ha confermato il completo differenziamento tra i profili genetici di individui portatori dei differenti aplotipi mitocondriali. I marcatori microsatellite hanno permesso di individuare l’esistenza di una debole struttura genetica su scala locale nelle popolazioni del Parco Regionale dei Monti Simbruini (Lazio) e hanno evidenziato una significativa riduzione della variabilità genetica nelle popolazioni marginali dei Monti Aurunci (Lazio) e soprattutto della Sicilia. I risultati ottenuti intendono costituire una base di conoscenze genetiche utile alla pianificazione di future misure volte alla conservazione della specie.
Present work is addressed at providing basic information about relationships between patterns of geographic distribution and genetic variation in populations of the montane butterfly Parnassius mnemosyne. A total of 172 individuals from 58 localities representing most of the species range have been sequenced at part of two mitochondrial genes (COI and ND5) and one nuclear gene (EF-1α). A sample of 397 individuals from 16 italian localities (Central-Eastern Alps, Central Apennine and Sicily) has been characterized at 5 newly developed microsatellite loci. Phylogeographic analysis of mitochondrial and nuclear sequences showed that P. mnemosyne populations bear genetic traces of a geographic history about one million years long. Highly divergent mitochondrial lineages mark at least two evolutionarily significant units in Europe, which may indeed represent different species. Nested Clade Analysis was employed in a reconstruction of the main stages of the history of the species during the Pleistocene, including possible evidences of survival in Northern refugia during last glacial episodes. Novel microsatellite markers have been tested on some critical areas and provided evidences of genetic structuring at the scale of a few kilometers. Marked reduction of genetic variability was evidenced in “marginal” populations from Monti Aurunci (Lazio) and Sicily. Results are meant to offer a genetically based framework for planning future conservation effort.

Thunderstorms are destructive phenomena at the mesoscale with extension of few kilometres and short duration, potentially dangerous for mid-low structures. The nonstationary nature of the wind field generated by thunderstorm outflows makes most of the theory and models developed for extra-tropical cyclones unsuitable and their small extension make them difficult to be detected by one single anemometer. These circumstances prevent the collection of precious data over which research can be carried out and the development of robust models for rapid engineering calculations shared by the scientific community. Therefore, a unified and reliable analytical model for the assessment of the maximum dynamic response to thunderstorms coherent with the techniques commonly adopted in wind engineering is not yet available. In this framework, the thesis introduces an Evolutionary Power Spectral Density (EPSD) model of the wind velocity related of thunderstorm outflows, consistent with full-scale records, and studies its application to calculate the alongwind dynamic response of structures and its maximum from an operative perspective. The EPSD model is derived starting from the analysis of 129 full-scale thunderstorm records, assuming the turbulent fluctuations uniformly modulated and the turbulence intensity constant. The reliability of the assumptions are verified on the basis of the data available. Three analytical models for the modulating function of the slowly-varying mean wind velocity are proposed. The models are based on the functions extracted from the records and include parameters of physical meaning for the thunderstorm outflow. Moreover, the possibility of adopting the classical spectral models of synoptic winds to model the stationary part of the turbulence is verified. Successively, the EPSD model is adopted to calculate the dynamic response of a set of linear elastic point-like SDOF systems with variable fundamental frequency and damping ratio, both accounting and neglecting the effects of the transient dynamics. In this framework a closed-form solution of the Evolutionary Frequency Response Function (EFRF) is derived. The mean value of the maximum response is estimated based on an Equivalent Parameter Technique (EPT) from literature, generalizing the Davenport’s gust factor technique. The effects of the Poisson hypothesis are investigated and mitigated introducing an equivalent expected frequency. The results are validated with the ones obtained in the time domain starting from the real thunderstorm records available. Successively, a sensitivity analysis is carried out to assess the influence on the maximum dynamic response of the parameters that shape the modulating function of the velocity. A closed-form solution for the equivalent parameters and the gust factor is introduced. The comparison with alternative formulations proposed in the literature demonstrates the improved accuracy of the proposed one. Finally, the formulation is extended to the analysis of slender vertical structures, adopting a vertical profile for the mean wind velocity from the literature and the equivalent wind spectrum technique. Two case studies of vertical slender structures are analysed and a comparison with synoptic wind loading conditions is outlined, showing that the proposed model constitutes a valid and handy tool for the evaluation of the wind loading on structures provided by thunderstorm outflows.

A human liver cDNA library was screened by colony hybridization with two mixtures of synthetic oligodeoxyribonucleotides as probes. These oligonucleotides encoded regions of β-factor Xlla as predicted from the amino acid sequence. Four positive clones were isolated that contained DNA coding for most of factor XII mRNA. A second human liver cDNA library was screened by colony hybridization with ³²P-labeled cDNA clones obtained from the first screen and two identical clones were isolated. DNA sequence analysis of these overlapping clones showed that they contained DNA coding for the signal peptide sequence, the complete amino acid sequence of plasma factor XII, a TGA stop codon, a 3' untranslated region of 150 nucleotides, and a poly A⁺ tail. The cDNA sequence predicts that plasma factor XII consists of 596 amino acid residues. Within the predicted amino acid sequence of factor XII, were identified three peptide bonds that are cleaved by kallikrein during the formation of β-factor Xlla. Comparison of the structure of factor XII with other proteins revealed extensive sequence identity with regions of tissue-type plasminogen activator (the epidermal growth factor-like region and the kringle region) and fibronectin (type I and type II homologies). As the type II region of fibronectin contains a collagen-binding site, the homologous region in factor XII may be responsible for the binding of factor XII to collagen. The carboxyl-terminal region of factor XII shares considerable amino acid sequence homology with other serine proteases including trypsin and many clotting factors. A human genomic phage library was screened by using a human factor XII cDNA as ahybridization probe. Two overlapping phage clones were isolated which contain the entire human factor XII gene. DNA sequence and restriction enzyme analysis of the clones indicate that the gene is approximately 12 kbp in size and is comprised of 13 introns and 14 exons. Exons 3 through 14 are contained in a genomic region of only 4.2 kbp with introns ranging in size from 80 to 554 bp. The multiple regions found in the coding sequence of FXII that are homologous to putative domains in fibronectin and tissue-type plasminogen activator are contained on separate exons in the factor XII gene. The intron/exon gene organization is similar to the serine protease gene family of plasminogen activators and not to the clotting factor family. Analysis of the 5' flanking region of the gene shows that it does not contain the typical TATA and CAAT sequences found in other genes. This is consistent with the finding that transcription of the gene is initiated at multiple start sites.
Medicine, Faculty of
Biochemistry and Molecular Biology, Department of
Graduate

The global reduction in the use of PBDEs and HBCD as flame retardants has opened the way for the introduction of “Novel” BFRs (NBFRs) in place of the banned formulations, indicating those BFRs that are new in the market or newly/recently observed in the environment in respect to PBDEs and HBCD. Consequently, consumption and production of these NBFRs will keep rising, and increasing environmental levels of these chemicals are expected in the near future. Important representatives of this group are decabromodiphenyl ethane (DBDPE), 1,2-bis(2,4,6-tribromophenoxy) ethane (BTBPE), hexabromobenzene (HBB), and pentabromoethylbenzene (PBEB). In Italy, previous studies have shown that some BFRs (PBDEs) were measured at high concentrations in the Varese province due to the presence of a great number of textile and plastic industries, and particularly in the sediments of Lake Maggiore, where those facilities wastewaters are finally collected mainly through two lake tributaries (Bardello and Boesio). For these reasons, the present thesis has the aim to evaluate the presence, and the potential bioaccumulation and biomagnification processes of six different classes of BFRs (PBDEs, HBCD, DBDPE, BTBPE, HBB and PBEB) in the Lake Maggiore ecosystem, with particular regard to zebra mussels (Dreissena polymorpha), zooplankton, one littoral fish species (common roach - Rutilus rutilus), and two different pelagic species (twaite shad – Alosa agone and European whitefish – Coregonus lavaretus). Finally, the study has also considered the BFR contamination in the lake sediments with the aim of characterizing in detail the possible presence of temporal trends and/or identifying potential sources of contamination. Moreover, it is plausible that the BFR uptake by benthic organisms, followed by fish predation, might be a significant source of bioaccumulation.

[Truncated abstract] Tumor Necrosis Factor (TNF) is a potent pro-inflammatory cytokine involved in a range of biological functions including the differentiation, proliferation and survival of many cell types. The TNF gene lies in the class III region of the major histocompatibility complex (MHC), approximately 250 Kbp centromeric of the HLA-B locus and 850 Kbp telomeric of HLA-DR. Due to the genomic location and biological relevance of TNF, it is thought that genetic heterogeneity at this locus may be associated with autoimmune and infectious diseases. A G-to-A single nucleotide polymorphism (SNP) at position -308 (relative to the transcriptional start site) in the TNF promoter has been well described. The less common -308A variant has been shown to be linked with the HLA-A1, B8, DR3 haplotype which in turn has been associated to a high TNF producing phenotype. Determining whether the -308 polymorphism contributes to elevated levels of expression has therefore been a priority for many research groups. Some investigators have shown differences in transcription between the -308G and -308A alleles while others could not. These contradicting results have led to conflicting views regarding the functional relevance of the -308 SNP. In this study, statistical analysis of 18 independent transient transfections of -308 biallelic TNF reporter constructs have provided evidence for a functional consequence of the polymorphism. ... In addition, chromatin accessibility of this region was maximal at greater levels of transcription suggesting a role for both chromatin structure and YY1 binding in -308G regulation. Surprisingly, chromatin structure did not seem to play a role in -308A regulation nor was there any significant binding of YY1, suggesting the -308 region does not affect transcriptional control of TNF. Taken as a whole, the G-to-A SNP relieves YY1 binding and demonstrates an allele-specific regulatory mechanism controlling expression. A growing list of promoter polymorphisms exists in the human genome having associations with certain diseases. Determining the functional consequence of these SNPs has proven difficult and utilized mainly in vitro approaches. In this thesis, a unique approach to investigating the functionality of promoter polymoprhisms has been developed, utilizing in vivo techniques which test their effects in a more natural system. It is hoped that the identification of the allele-specific YY1-mediated control of the -308 region of the TNF promoter may provide insight into overexpression as a consequence of the polymorphism and its role in the genetic susceptibility to MHC-associated autoimmune disease.

In the past years Väderstad Verken has been developing rapidly and expanding the sales of its machines in new markets, in particular in Eastern Europe. Due to the growing sales and some risks related to possible increases in the import duties on some agricultural equipment the company is considering the possibility of building a factory in the Eastern Europe to serve that market.

For this reason the company was interested in a pre-study on the important factors for the factory start-up in Eastern Europe, in particular, Russia and Ukraine. Thus the scope of the thesis included the research and identification of the factors important particularly for Väderstad Verken as well as identifying some general steps necessary for the factory start-up. In order to identify and analyse location factors critical for Väderstad Verken, literature on location analysis was analysed in order to identify a list of possible factors as well as tools which could be used to evaluate them.

Center of Gravity and Analytic Hierarchy Process (AHP) have been selected to evaluate the performance of several regions on these factors to identify the suitable location of the factory. Expert Choice software was applied in order to simplify the comparison of various factors following the AHP steps.

A specification for the potential factory has been defined given the desired production capacity and requirements. Two factory layouts have been suggested taking into consideration the possibility of the factory expansion.

In some way this master thesis could be used as a guideline for the selection of suitable manufacturing location as well as factory start-up.

Traffic volume data are input to many transportation analyses including planning, roadway design, pavement design, air quality, roadway maintenance, funding allocation, etc. Annual Average Daily Traffic (AADT) is one of the most often used measures of traffic volume. Acquiring the actual AADT data requires the collection of traffic counts continuously throughout a year, which is expensive, thus, can only be conducted at a very limited number of locations. Typically, AADTs are estimated by applying seasonal factors (SFs) to short-term counts collected at portable traffic monitoring sites (PTMSs). Statewide in Florida, the Florida Department of Transportation (FDOT) operates about 300 permanent traffic monitoring sites (TTMSs) to collect traffic counts at these sites continuously. TTMSs are first manually classified into different groups (known as seasonal factor categories) based on both engineering judgment and similarities in the traffic and roadway characteristics. A seasonal factor category is then assigned to each PTMS according to the site’s functional classification and geographical location. The SFs of the assigned category are then used to adjust traffic counts collected at PTMSs to estimate the final AADTs. This dissertation research aims to develop a more objective and data-driven method to improve the accuracy of SFs for adjusting PTMSs. A statewide investigation was first conducted to identify potential influential factors that contribute to seasonal fluctuations in traffic volumes in both urban and rural areas in Florida. The influential factors considered include roadway functional classification, demographic, socioeconomic, land use, etc. Based on these factors, a methodology was developed for assigning seasonal factors from one or more TTMSs to each PTMS. The assigned seasonal factors were validated with data from existing TTMSs. The results show that the average errors of the estimated seasonal factors are, on average, about 4 percent. Nearly 95 percent of the estimated monthly SFs contain errors of no more than 10 percent. It was concluded that the method could be applied to improve the accuracy in AADT estimation for both urban and rural areas in Florida.

Le facteur VIII (FVIII) est une glycoprotéine de la coagulation plasmatique, co-facteur du facteur IX activé (FIXa). Son métabolisme dépend de multiples facteurs limitant ou favorisant sa survie ou sa fonction. L’objectif de ce travail était d’analyser les différents paramètres pouvant influencer le taux de FVIII circulant et sa fonction pro-coagulante. Il a compris 4 volets : 1) Nous avons montré que la rétention intra-cellulaire connue du FVIII était en grande partie liée à son agrégation et sa dégradation par les 2 voies protéasomale et lysosomale. 2) Nous avons analysé les mutations du gène F8 responsables de l’hémophilie A dans une large cohorte de patients. Leur analyse bio-informatique a permis de démontrer leur caractère délétère pour le FVIII. L’influence de ces mutations sur la survenue d’allo-anticorps anti-FVIII a été étudiée et stratifiée, en association avec l’origine ethnique et la recherche d’antécédents familiaux d’nticorps. 3) La recherche des facteurs influençant les taux de FVIII chez les conductrices d’hémophilie A a mis en évidence des déterminants majeurs : l’existence d’une maladie génétique additionnelle responsable d’un déficit en FVIII, le taux de facteur Willebrand et l’inactivation non aléatoire du chromosome X ; et des déterminants secondaires : l’âge, la sévérité de l’hémophilie, le polymorphisme D1241E du gène F8 et 5 nouveaux polymorphismes de la LRP1 localisés dans ses sites de liaison pour le FVIII. 4) Nous avons analysé 8 FVIII recombinant mutés in vitro en alanine dans la région 1808-1818 du FVIII. Les études antérieures n’analysant que la chaîne légère, ont montré que cette région était la plus affine pour le FIXa. Nous démontrons ici que la région 1808-1818 ne paraît pas si essentielle à la fonction du FVIIIcar au sein de la molécule entière, son affinité diminue et ses mutations n’altèrent que très modérément l’activité du FVIII
The factor VIII (FVIII) is a glucoprotein of the coagulation, being the cofactor of the activated factor IX (FIXa). Its metabolism depends on various limiting factors or enhancing its survey or function. The objective of this research’s work was to analyse different parameters that could influence the plasma FVIII level and its pro-coagulant function. It included 4 parts : 1) We showed that the known intra-cellular retention of FVIII was mainly due to its aggregation and degradation following both proteasomal and lysosomal pathways. 2) We analysed FVIII gene mutations responsible for hemophilia A in a large patients cohort. The bio-informatic analysis demonstrated its deleterious consequence. The influence of these mutations on the anti-FVIII antibodies occurrence was stratified, in association with ethnicity and familial antecedent of inhibitor. 3) The research of factors influencing FVIII levels in hemophilia A carriers showed : i) major determinants such as the presence of an additional genetic disease characterised by a FVIII deficiency, the factor Willebrad’s level and the non-random inactivation of the X chromosome; and ii) minor determinants : age, severity of hemophilia, the polymorphism D1241E of FVIII gene, and 5 new polymorphisms of LRP1 located in its binding site for FVIII.4) We analysed 8 recombinant FVIII with in vitro created mutations in its 1808-1818 region. Previous studies that analysed only the FVIII light chain, have shown that this region constituted the more affine binding site of FVIII for FIXa. We demonstrated here that the 1808-1818 region is not as essential as it was reported because within the entire molecule, its affinity decreases and mutations affecting it do alter mildly the FVIII activity

The entrepreneur as an economic agent is highlighted as being important by several researchers, mainly through their contribution to innovation and economic development. Yet there is no universal definition of what is characterized as being an entrepreneur. The elements affecting success of entrepreneurs has previously not been outlined extensively, the aim of this study is therefore to determine the factors of entrepreneurial success in Sweden. This is made possible by adopting a statistical prosopography and reproducing the method used by Nuvolari, Toninelli & Vasta (2015). The method involves collecting qualitative information found in the Dictionary of Swedish National Biography and from a list of the most prominent entrepreneurs in Sweden, published by the newspaper Svenska Dagbladet. The qualitative information is then rewritten into quantitative data, which enables the use of factor analysis along with ordinary least square regressions. The standardized research allows for comparison of other studies involving entrepreneurs active in different countries. In addition, the model is expanded using a cluster analysis, allowing for identifying structures and patterns within the sample of entrepreneurs. The study concludes that entrepreneurial success appears to be a multi-dimensional phenomenon, also shown in other case studies adopting similar method. The level of innovation intensity proves to enhance success of all dimensions. While an entrepreneur’s educational level is shown to have a positive relationship with the economic success, it appeared to be negatively related to the aspect of social mobility. Moreover, involvement in politics is also shown to have a negative impact on economic success. Finally, the cluster analysis proved the existence of six different types of entrepreneurs within the sample.

Mestrado em Contabilidade, Fiscalidade e Finanças Empresariais
A adopção de sistemas ERP (Enterprise Resource Planning) tem vindo a proliferar-se em Portugal. Este tipo de software implica disponibilidade de tempo e custos elevados por parte das empresas que os adoptam. Torna-se significativo estudar e apurar os factores críticos que contribuem para o sucesso da implementação na realidade portuguesa, dada a diferenciação cultural que caracteriza o tecido empresarial actual no que diz respeito à abordagem deste tipo de implementação. Este estudo faz uma análise factorial exploratória aos factores que se destacam como sendo mais importantes de acordo com a amostra inquirida, procedendo posteriormente à sua categorização. Foram encontrados cinco factores críticos de sucesso, estando o maior número de itens condensado nos dois primeiros.
Enterprise resource planning adoption is proliferating in Portugal. This type of software demands high availability and costs from companies who adopt them. It is relevant to study and determine the critical factors that contribute for a successful implementation in the Portuguese reality given the cultural differentiation that characterizes the current business environment regarding the approach for this type of implementation. This study is an exploratory factor analysis to those factors, which stand out as being most significant according to the sample surveyed, proceeding to their categorization. Five critical success factors were found, where the largest number of items were condensed in the first two.

Every modern organization relies on information systems and has a need formeasuring the performance of those systems and the IT department. It is furthermorewanted to be able to compare oneself to others. Municipalities struggle with theabsence of an instrument designed for their specific needs and circumstances becausemost instruments are designed for profit organizations, focus heavily on technical andeconomical criteria and do not involve criteria that are easy to compare. In this projectthe success factor analysis, a performance measurement instrument, is selected,modified and finally applied in two Swedish municipalities to investigate thesignificance of this instrument and its ability to identify significant factors of success.With interpreting the data gathered using on-line surveys, similarities and comparablecriteria as well as eight significant factors could be identified that are genericthroughout all surveys done.

This dissertation introduces a new method, Pratt's measure matrix, for interpreting multidimensional oblique factor models in both exploratory and confirmatory contexts. Overall, my thesis, supported by empirical evidence, refutes the currently recommended and practiced methods for understanding an oblique factor model; that is, interpreting the pattern matrix or structure matrix alone or juxtaposing both without integrating the information. Chapter Two reviews the complexities of interpreting a multidimensional factor solution due to factor correlation (i.e., obliquity). Three major complexities highlighted are (1) the inconsistency between the pattern and structure coefficients, (2) the distortion of additive properties, and (3) the inappropriateness of the traditional cut-off rules as being "meaningful". Chapter Three provides the theoretical rationale for adapting Pratt's importance measures from their use in multiple regression to that of factor analysis. The new method is demonstrated and tested with both continuous and categorical data in exploratory factor analysis. The results show that Pratt's measures are applicable to factor analysis and are able to resolve three interpretational complexities arising from factor obliquity. In the context of confirmatory factor analysis, Chapter Four warns researchers that a structure coefficient could be entirely spurious due to factor obliquity as well as zero constraint on its corresponding pattern coefficient. Interpreting such structure coefficients as Graham et al. (2003) suggested can be problematic. The mathematically more justified method is to transform the pattern and structure coefficients into Pratt's measures. The last chapter describes eight novel contributions in this dissertation. The new method is the first attempt ever at ordering the importance of latent variables for multivariate data. It is also the first attempt at demonstrating and explicating the existence, mechanism, and implications of the suppression effect in factor analyses. Specifically, the new method resolves the three interpretational problems due to factor obliquity, assists in identifying a better-fitting exploratory factor model, proves that a structure coefficient in a confirmatory factor analysis with a zero pattern constraint is entirely spurious, avoids the debate over the choice of oblique and orthogonal factor rotation, and last but not least, provides a tool for consolidating the role off actors as the underlying causes.

Over the last decade or more, chronic illness research has consistently found that the lineaer relationship between knowledge and behaviour or between behaviour change and improved health outcomes does not exist. Furthermore, the link between behaviour and health status is not as strong as the link between illness-specific efficacy belier and health status. Strategies to increase confidence in illness-specific behaviours have gradually assumed more importance in improving health outcomes. Strategies to improve behaviour-specific efficacy belief can assist individuals to change their behaviour by influencing behavioural choices, effort and persistence with task man demands. Concomitantly, it has been suggested that there is a positive relationship between efficacy belief and psychosocial functioning. It is unclear as to whether this empirical evidence also applies to chronic illness conditions with a complex self-care regimen. The degree to which a more general level of confidence, or efficacy belief, can also contribute to psychosocial functioning is unknown. The focus of this study was to examine the relative impact of general and illness-specific efficacy expectations on psychosocial adaptation to illness over nine months. The study measured illness-specific efficacy beliefs when it was expected that they were still developing.The illness-specific beliefs were compared to the purportedly more stable general efficacy belief. This longitudinal study employed an exploratory predictive design to measure efficacy beliefs in the natural setting. Data were collected at entry to the study, at three and nine months Participants included adults from three chronic illness groups: Arthritis (n= = ), diabetes type 1 (n = 104) and type 2 (n = 122). The self-report questionnaires used collect the data were three illness-specific efficacy belief measures, general self-efficacy and the Psychosocial Adjustment to Illness Scale. The dependent variable of interest was psychosocial adaptation to illness. Multiple regression analysis provided evidence of between-group differences in the positive contribution of general and illness-specific efficacy beliefs to psychosocial adaptation for chronic illness groups with different regimen attributes. The variables best able to predict psychosocial adaptation to illness over time, after being adjusted for perceived level of stress and general self-efficacy (belief in abilities in general), were illness-specific efficacy beliefs. A general efficacy belief contributed to the illness adaptation process initially but its influence reduced as the influence of illness-specific beliefs increased. Repeated measures MANOVA confirmed the stability of general efficacy belief. The contribution of this study to current knowledge of self- -efficacy theory is its application to self-management programs for chronic illness groups. The findings suggest that the more stable general efficacy belief has a role in psychosocial adaptation to chronic illness during the period when illness-specific efficacy beliefs, targeted by self-management programs, are still developing.

The main objective of this work is to propose new procedures for the general dynamic factor analysis

introduced by Forni et al. (2000). First, we develop an identification method for determining the number of common shocks in the general dynamic factor model. Sufficient conditions for consistency of the criterion are provided for large n (number of series) and T (the series length). We believe that our procedure can shed

light on the ongoing debate on the number of factors driving the US or Eurozone economy. Second, we show how the dynamic factor analysis method proposed in Forni et al. (2000), combined with our identification method, allows for identifying and estimating joint and block-specific common factors. This leads to a more

sophisticated analysis of the structures of dynamic interrelations within and between the blocks in suchdatasets.

Besides the framework of the general dynamic factor model we also propose a consistent lag window spectral density estimator based on multivariate M-estimators by Maronna (1976) when the underlying data are coming from the alpha mixing stationary Gaussian process.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

The dispersion analysis is used to determine the relationship between the indicators, quantitative assessment of risk parameters. In the analysis of enterprises can different methods and models of analysis are used. Their number and the breadth of application depends on the specific objectives of the analysis and is determined by it tasks in each individual case. When collecting compulsory statistical information, it is quite easy to conduct a dispersion analysis of domestic activities to ensure financial and economic security.

This thesis examines motivation in English L1 learners of Japanese as a Foreign Language (JFL) by analyzing the students' responses to a questionnaire modeled after Dörnyei's research and an observational instrument called the Motivation Behaviors of Language Learning (MBLL) modeled after Guilloteaux and Dörnyei's (2008) research on motivational teaching strategies on behaviors. The data from the questionnaires were subjected to a discriminative analysis of students answers in relation to a high grade (receiving a B- or higher) or lower (C+ or lower) in their Japanese classes. A Factor Analysis of the data was also carried out. The discriminative results could predict students' receiving higher or lower grades with 13-16% error. In addition, behaviors examined in the 200-level Japanese classes seemed to match students' responses to a questionnaire, suggesting they are not highly motivated. However, students who passed this class reported similar study habits outside of class in preparation as students from a 400-level class. The Factor Analysis also found that students with higher self-perception of their motivation and language aptitude tended to receive higher grades in the class. The results suggest that motivation can be further understood by understanding behaviors in addition to traditional questionnaires, and students' self perceptions of their language abilities may affect their grades in the language classroom. In the future, motivation research should include triangulating questionnaire data with other data, such as observational instruments. This thesis is a step in that direction.

The Kaufman Assessment Battery for Children is a relatively new instrument designed to measure the cognitive abilities of children ages 2 1/2 to 12 1/2. The battery was predominantly based upon Luria's theory of cognitive processing which proposes a simultaneous and sequential dichotomy for the analysis of information. Exploratory and confirmatory factor analyses have generally supported this theory. However, across different age groups and with special populations, analyses have been inconsistent.Few alternative structures for the battery have been tested, especially with special populations.The purpose of the present study was to assess the viability of a two factor structure based upon the modality of input for a group of students referred for learning difficulties. One hundred and twelve students, ages 7 to 12 1/2, who had been referred by teachers because of learning difficulties were used in the study. The thirteen subtests of the K-ABC were specified as being associated with a visual input factor, an auditory input factor, or both. Confirmatory factor analysis, as performed by the LISREL VI computer program, was then used to test this hypothesis. Unreasonable parameter estimates led to the rejection of the model for this sample. This lack of confirmation is discussed in terms of high correlations among estimates, misspecification of the model, sample homogeneity, and lack of independence among the subtests in terms of input modality. It was concluded that input modality was not a major factor in performance on the battery for this particular sample.

Dynamic panel data models are widely used by econometricians to study over time the economics of, for example, people, firms, regions, or countries, by pooling information over the cross-section. Though much of the panel research concerns inference in stationary models, macroeconomic data such as GDP, prices, and interest rates are typically trending over time and require in one way or another a nonstationary analysis. In time series analysis it is well-established how autoregressive unit roots give rise to stochastic trends, implying that random shocks to a dynamic process are persistent rather than transitory. Because the implications of, say, government policy actions are fundamentally different if shocks to the economy are lasting than if they are temporary, there are now a vast number of univariate time series unit root tests available. Similarly, panel unit root tests have been designed to test for the presence of stochastic trends within a panel data set and to what degree they are shared by the panel individuals. Today, growing data certainly offer new possibilities for panel data analysis, but also pose new problems concerning double-indexed limit theory, unobserved heterogeneity, and cross-sectional dependencies. For example, economic shocks, such as technological innovations, are many times global and make national aggregates cross-country dependent and related in international business cycles. Imposing a strong cross-sectional dependence, panel unit root tests often assume that the unobserved panel errors follow a dynamic factor model. The errors will then contain one part which is shared by the panel individuals, a common component, and one part which is individual-specific, an idiosyncratic component. This is appealing from the perspective of economic theory, because unobserved heterogeneity may be driven by global common shocks, which are well captured by dynamic factor models. Yet, only a handful of tests have been derived to test for unit roots in the common and in the idiosyncratic components separately. More importantly, likelihood-based methods, which are commonly used in classical factor analysis, have been ruled out for large dynamic factor models due to the considerable number of parameters. This thesis consists of four papers where we consider the exact factor model, in which the idiosyncratic components are mutually independent, and so any cross-sectional dependence is through the common factors only. Within this framework we derive some likelihood-based tests for common and idiosyncratic unit roots. In doing so we address an important issue for dynamic factor models, because likelihood-based tests, such as the Wald test, the likelihood ratio test, and the Lagrange multiplier test, are well-known to be asymptotically most powerful against local alternatives. Our approach is specific-to-general, meaning that we start with restrictions on the parameter space that allow us to use explicit maximum likelihood estimators. We then proceed with relaxing some of the assumptions, and consider a more general framework requiring numerical maximum likelihood estimation. By simulation we compare size and power of our tests with some established panel unit root tests. The simulations suggest that the likelihood-based tests are locally powerful and in some cases more robust in terms of size.
Solving Macroeconomic Problems Using Non-Stationary Panel Data

Today's computerized processes generate

massive amounts of streaming data.

In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The samples often form a long temporal sequence called a multivariate time series where any one sample is influenced by the others.

We wish to build a model that will ensure robust generation, reviewing, and representation of new multivariate time series that are consistent with the underlying process.

In this thesis, we adopt a modeling framework to extract characteristics from multivariate time series that correspond to dynamic variation-covariation common to the measured variables across all the samples. Those characteristics of a multivariate time series are named its 'commonalities' and a suitable measure for them is defined. What makes the multivariate time series model versatile is the assumption regarding the existence of a latent time series of known or presumed characteristics and much lower dimensionality than the measured time series; the result is the well-known 'dynamic factor model'.

Original variants of existing methods for estimating the dynamic factor model are developed: The estimation is performed using the frequency-domain equivalent of the dynamic factor model named the 'spectral factor model'. To estimate the spectral factor model, ideas are sought from the asymptotic theory of spectral estimates. This theory is used to attain a probabilistic formulation, which provides maximum likelihood estimates for the spectral factor model parameters. Then, maximum likelihood parameters are developed with all the analysis entirely in the spectral-domain such that the dynamically transformed latent time series inherits the commonalities maximally.

The main contribution of this thesis is a learning framework using the spectral factor model. We term learning as the ability of a computational model of a process to robustly characterize the data the process generates for purposes of pattern matching, classification and prediction. Hence, the spectral factor model could be claimed to have learned a multivariate time series if the latent time series when dynamically transformed extracts the commonalities reliably and maximally. The spectral factor model will be used for mainly two multivariate time series learning applications: First, real-world streaming datasets obtained from various processes are to be classified; in this exercise, human brain magnetoencephalography signals obtained during various cognitive and physical tasks are classified. Second, the commonalities are put to test by asking for reliable prediction of a multivariate time series given its past evolution; share prices in a portfolio are forecasted as part of this challenge.

For both spectral factor modeling and learning, an analytical solution as well as an iterative solution are developed. While the analytical solution is based on low-rank approximation of the spectral density function, the iterative solution is based on the expectation-maximization algorithm. For the human brain signal classification exercise, a strategy for comparing similarities between the commonalities for various classes of multivariate time series processes is developed. For the share price prediction problem, a vector autoregressive model whose parameters are enriched with the maximum likelihood commonalities is designed. In both these learning problems, the spectral factor model gives commendable performance with respect to competing approaches.

Les processus informatisés actuels génèrent des quantités massives de flux de données. Dans nombre d'applications, ces flux de données sont collectées en vue de modéliser les processus. Les modèles de processus obtenus ont pour but la réalisation d'objectifs tels que l'aide à la décision, la visualisation de données, l'informatique décisionnelle, l'automatisation et le contrôle, la reconnaissance de formes et la classification, etc. La modélisation de processus sur la base de données implique cependant de faire face à d’importants défis. Outre les erreurs, les données aberrantes et le bruit, le principal défi provient de la large dimensionnalité, i.e. du nombre de variables dans chaque échantillon de données mesurées. Les échantillons forment souvent une longue séquence temporelle appelée série temporelle multivariée, où chaque échantillon est influencé par les autres. Notre objectif est de construire un modèle robuste qui garantisse la génération, la révision et la représentation de nouvelles séries temporelles multivariées cohérentes avec le processus sous-jacent.

Dans cette thèse, nous adoptons un cadre de modélisation capable d’extraire, à partir de séries temporelles multivariées, des caractéristiques correspondant à des variations - covariations dynamiques communes aux variables mesurées dans tous les échantillons. Ces caractéristiques sont appelées «points communs» et une mesure qui leur est appropriée est définie. Ce qui rend le modèle de séries temporelles multivariées polyvalent est l'hypothèse relative à l'existence de séries temporelles latentes de caractéristiques connues ou présumées et de dimensionnalité beaucoup plus faible que les séries temporelles mesurées; le résultat est le bien connu «modèle factoriel dynamique». Des variantes originales de méthodes existantes pour estimer le modèle factoriel dynamique sont développées :l'estimation est réalisée en utilisant l'équivalent du modèle factoriel dynamique au niveau du domaine de fréquence, désigné comme le «modèle factoriel spectral». Pour estimer le modèle factoriel spectral, nous nous basons sur des idées relatives à la théorie des estimations spectrales. Cette théorie est utilisée pour aboutir à une formulation probabiliste, qui fournit des estimations de probabilité maximale pour les paramètres du modèle factoriel spectral. Des paramètres de probabilité maximale sont alors développés, en plaçant notre analyse entièrement dans le domaine spectral, de façon à ce que les séries temporelles latentes transformées dynamiquement héritent au maximum des points communs.

La principale contribution de cette thèse consiste en un cadre d'apprentissage utilisant le modèle factoriel spectral. Nous désignons par apprentissage la capacité d'un modèle de processus à caractériser de façon robuste les données générées par le processus à des fins de filtrage par motif, classification et prédiction. Dans ce contexte, le modèle factoriel spectral est considéré comme ayant appris une série temporelle multivariée si la série temporelle latente, une fois dynamiquement transformée, permet d'extraire les points communs de façon fiable et maximale. Le modèle factoriel spectral sera utilisé principalement pour deux applications d'apprentissage de séries multivariées :en premier lieu, des ensembles de données sous forme de flux venant de différents processus du monde réel doivent être classifiés; lors de cet exercice, la classification porte sur des signaux magnétoencéphalographiques obtenus chez l'homme au cours de différentes tâches physiques et cognitives; en second lieu, les points communs obtenus sont testés en demandant une prédiction fiable d'une série temporelle multivariée étant donnée l'évolution passée; les prix d'un portefeuille d'actions sont prédits dans le cadre de ce défi.

À la fois pour la modélisation et pour l'apprentissage factoriel spectral, une solution analytique aussi bien qu'une solution itérative sont développées. Tandis que la solution analytique est basée sur une approximation de rang inférieur de la fonction de densité spectrale, la solution itérative est basée, quant à elle, sur l'algorithme de maximisation des attentes. Pour l'exercice de classification des signaux magnétoencéphalographiques humains, une stratégie de comparaison des similitudes entre les points communs des différentes classes de processus de séries temporelles multivariées est développée. Pour le problème de prédiction des prix des actions, un modèle vectoriel autorégressif dont les paramètres sont enrichis avec les points communs de probabilité maximale est conçu. Dans ces deux problèmes d’apprentissage, le modèle factoriel spectral atteint des performances louables en regard d’approches concurrentes.
Doctorat en Sciences
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La Maladie Thrombo-Embolique Veineuse (MTEV) est une maladie dont les facteurs de risque sont à la fois environnementaux et génétiques. Les facteurs de risque génétiques bien établis sont les déficits en anti-thrombine, en protéine S, en protéine C, la mutation du Facteur V de Leiden (FVL), la mutation du Facteur (F) II G20210A, ainsi que le gène ABO dont les allèles A1 et B augmentent le risque de MTEV par rapport aux allèles A2 et O. Alors qu’une part importante de l’héritabilité de la MTEV reste inexpliquée, les études contemporaines se heurtent à un manque de puissance pour découvrir de nouveaux facteurs génétiques dont les effets sont de plus en plus faibles. En vue d’augmenter la puissance de détection de nouveaux gènes de susceptibilité à la MTEV, j’ai recherché les déterminismes génétiques de deux de ses phénotypes intermédiaires : les taux d’activité plasmatique du FVIII et les taux d’antigénémie de sa protéine de transport, le Facteur de von Willebrand (vWF). Dans un premier temps, j’ai réalisé une analyse de liaison des taux de FVIII et de vWF à partir d’un échantillon de cinq grandes familles franco-canadiennes (totalisant 255 personnes) recrutées via un cas de MTEV avec mutation FVL. Quatre régions liées aux taux de FVIII et/ou vWF ont été identifiées. L’une de ces régions correspondait au locus du gène ABO déjà connu pour influencer les taux de FVIII et vWF. La recherche de gènes candidats au sein des autres signaux de liaison s’est effectuée par l’étude in silico d’une analyse d’association pangénomique de la MTEV incluant 419 cas et 1228 témoins. Deux gènes candidats ont été identifiés : STAB2 et BAI3. J’ai ensuite réalisé des études d’associations de cinq polymorphismes de BAI3. L’un d’entre eux était d’une part associé à une élévation des taux de vWF (résultat obtenu dans un échantillon de 108 familles nucléaires en bonne santé et reproduit dans un échantillon de 916 patients non apparentés atteints de MTEV), et d’autre part associé au risque de survenue de MTEV parmi les sujets non porteurs de mutations FVL et FII de deux échantillons cas-témoins (respectivement 916 cas et 801 témoins, et 250 cas et 607 témoins). Quant à STAB2, durant le courant de ma thèse, deux de ces polymorphismes ont été décrits comme associés aux taux de FVIII et vWF au cours d’une vaste étude d’association pangénomique (GWAS) menée par le consortium CHARGE rassemblant 23 600 personnes. Dans un second temps, j’ai réalisé une méta-analyse de trois GWAS des taux de FVIII et vWF. Ces analyses avaient été conduites avec l’échantillon des cinq grandes familles franco-canadiennes et deux échantillons de 972 et 570 patients atteints de MTEV. Elles étaient ajustées sur les polymorphismes du gène ABO permettant de distinguer les allèles A1, A2, B et O, dans l’optique d’augmenter la puissance des analyses en diminuant la variance résiduelle des phénotypes. Aucun polymorphisme n’était associé ni aux taux de vWF ni à ceux de FVIII après prise en compte de la correction de Bonferroni pour tests multiples (p<10-7). Cependant, parmi les onze gènes qui présentaient des polymorphismes associés aux taux de vWF ou de FVIII avec une significativité p<10-5, de manière intéressante se trouvait STAB2. Cette étude a de plus permis de confirmer les associations nouvellement découvertes de polymorphismes situés dans les gènes VWF, STXBP5 et STX2
The Venous Thromboembolism (VTE) risk factors are environmental and genetic. The well established risk factors are anti-thrombin, protein C, protein S deficiency, Factor V Leiden and factor II mutation and ABO gene, with A1 and B allele increasing the risk of VTE. While an important part of VTE heritability remains unexplained, contemporary studies fail to discover new susceptibility genes with weaker effects. In order to increase the discovery power, I searched for genetic geterminism of two intermediary phenotypes of VTE : Factor VIII plasmatic activity (FVIII) and von Willebrand factor antigenemia (vWF)First, I performed a linkage study of FVIII and vWF from a sample of 5 large pedigrees (N=255). Four loci have been identified. One included ABO gene. I searched for candidate genes located in the others loci by studying in silico results from o Genome Wide Association Study (GWAS) of the VTE including 419 cases and and 1228 controls. témoins. Two candidate genes were identified : STAB2 et BAI3. Then I performed association studies of five SNPs in BAI3 with FVIII and vWF. One of them was associated to vWF (in a sample of 108 nuclear families and 916 VTE patients), and associated to VTE in two case-controls samples (respectively 916 cases and 801 controls, and 250 cases et 607 controls).Second, I performed a meta-analysis of three GWAS of FVIII and vWF from the same 5 pedigrees and two samples of VTE (N=972 and 570) adjusted on ABO blood group. No polymorphisms were significant after Bonferoni correction (p<10-7). Nevertheless, among 11 genes carrying polymorphisms with a p<10-5, interestingly was STAB2. Futhermore, this study allowed to confirm newly discoverd association with VWF, STXBP5 et STX2

In this study, an improved correlational procedure for factor-analyzing dichotomously-scored item response data is described and tested. The procedure involves (a) replacing the dichotomous input values with continuous probability values obtained through Rasch analysis; (b) calculating interitem product-moment correlations among the probabilities; and (c) subjecting the correlations to unweighted least-squares factor analysis. Two simulated data sets and an empirical data set (Kentucky Comprehensive Listening Test responses) were used to compare the new procedure with two more traditional techniques, using (a) phi and (b) tetrachoric correlations calculated directly from the dichotomous item-response values. The three methods were compared on three criterion measures: (a) maximum internal correlation; (b) product of the two largest factor loadings; and (c) proportion of variance accounted for. The Rasch-based procedure is recommended for subjecting dichotomous item response data to latent-variable analysis.

This study investigated the item parameter recovery of two methods of factor analysis. The methods researched were a traditional factor analysis of tetrachoric correlation coefficients and an IRT approach to factor analysis which utilizes marginal maximum likelihood estimation using an EM algorithm (MMLE-EM). Dichotomous item response data was generated under the 2-parameter normal ogive model (2PNOM) using PARDSIM software. Examinee abilities were sampled from both the standard normal and uniform distributions. True item discrimination, a, was normal with a mean of .75 and a standard deviation of .10. True b, item difficulty, was specified as uniform [-2, 2]. The two distributions of abilities were completely crossed with three test lengths (n= 30, 60, and 100) and three sample sizes (N = 50, 500, and 1000). Each of the 18 conditions was replicated 5 times, resulting in 90 datasets. PRELIS software was used to conduct a traditional factor analysis on the tetrachoric correlations. The IRT approach to factor analysis was conducted using BILOG 3 software. Parameter recovery was evaluated in terms of root mean square error, average signed bias, and Pearson correlations between estimated and true item parameters. ANOVAs were conducted to identify systematic differences in error indices. Based on many of the indices, it appears the IRT approach to factor analysis recovers item parameters better than the traditional approach studied. Future research should compare other methods of factor analysis to MMLE-EM under various non-normal distributions of abilities.

In this thesis we survey several interpolation methods that are used to construct the yield curves. We also review the bootstrapping and show that the bootstrap is closely connected to the interpolation in the case of bootstrapping yield curve. The most effort is dedicated, in this thesis, on the monotone convex method and on investigation of the difficulties to get accurate yield curves.

Dans la partie theorique, nous presentons la contribution de la triangulation de delaunay a la classification ascendante hierarchique; nous montrons ici l'unicite de la decomposition de delaunay dans le cas generique ou les facettes de l'enveloppe convexe de l'ensemble a classer sont toutes des simplexes. Grace a cette recherche theorique, nous ponvons dire que la methode de triangulation de delaunay est a la base de l'algorithme accelere de la cah. Dans la partie appliquee, on presente la contribution de l'analyse des donnees aux etudes marketing sur les medicaments en appliquant l'afc sur le tableau de donnees qui contient 99 medicaments et qui a pour objectif: la recherche du medicament le plus utilise et le plus apprecie par les patientes interrogees, leurs conjoints et leurs enfants. Grace a la technique du regroupement des individus en categories semblables par combinaison lineaire des modalites du signalement, nous avons pu determiner clairement les medicaments les plus utilises et les plus apprecies par les patientes, leurs conjoints et leurs enfants en fonction des maux, de la region, de la classe d'age et de la classe sociale

Le caoutchouc naturel (CN), a cis-1,4-polyisoprene, est produit principalement par Hevea brasiliensis. Le CN est un matériau très important pour l’industrie du transport et médicale. La demande en CN augmente d’année en année. Le CN est obtenu à partir du latex. Le latex s’écoule des laticifères après saignée de l’écorce des hévéas. L’éthéphon, un libérateur d’éthylène, peut être appliqué sur certains clones d’hévéa pour stimuler la production de latex. La saignée et la stimulation à l’éthéphon sont des stress de récolte conduisant à la production de métabolites secondaires et par conséquence au caoutchouc. La biosynthèse et la signalisation de l’éthylène (ET) et de l’acide jasmonique (JA) jouent un rôle crucial dans la réponse aux stress de récolte. Deux gènes codant des facteurs de réponse à l’éthylène (ethylene response factor, ERF), HbERF-IXc4 et HbERF-IXc5, ont été prédits être orthologue à ERF1 d’Arabidopsis. ERF1 est considéré comme un facteur clé de la réponse de défense à travers l’intégration des voies de signalisation de l’éthylène et du jasmonate. Les transcrits de HbERF-IXc4 et HbERF-IXc5 s’accumulent dramatiquement en réponse à des traitements combinant la blessure, le méthyl jasmonate, et l’éthylène. Ces facteurs sont ainsi supposés être des régulateurs clés au croisement des voies de signalisation de l’éthylène et du jasmonate dans les laticifères. HbERF-IXc4 et HbERF-IXc5 ont plusieurs caractéristiques des facteurs de transcription révélés respectivement lors des expériences de trans-activation et de localisation subcellulaire : ils peuvent activer des éléments GCC agissant en cis des promoteurs des gènes cibles et ils sont présents au niveau du noyau.Dans cette étude, l’analyse fonctionnelle des gènes HbERF-IXc4 et HbERF-IXc5 a été effectuées par sur-expression de ces gènes sous le contrôle de deux promoteurs, 35S CaMV et HEV2.1 dans des lignées transgéniques d’Hevea obtenues par transformation génétique via Agrobacterium tumefaciens. Cette sur-expression a conduit à augmenter les effets des gènes natifs HbERF-IXc4 et HbERF-IXc5. Vingt-neuf lignées à activité GFP ont été sélectionnées sur un milieu contenant de la paromomycine. Douze lignées ont permis régénérées des plantes mais seulement dix ont produit un nombre suffisant de plantes pour réaliser les observations de phénotypage avec au total 1622 plantes transgéniques acclimatées en serre. Ces dix lignées transgéniques ont été confirmées par hybridation moléculaire de type Southern. L’observation morphologique des plants jusqu’à un an montre que les deux gènes (HbERF-IXc4 and HbERF-IXc5) favorisent une meilleure croissance, en termes de hauteur des plants, du diamètre des tiges, et du poids frais et sec des parties aériennes et racinaires, avec une plus forte vigueur et tolérance aux stress abiotiques. Les plants sur-exprimant HbERF-IXc5 ont aussi une meilleure performance que ceux sur-exprimant HbERF-IXc4. Ces résultats montrent aussi un système racinaire plus vigoureux et bien équilibré par rapport à la plante entière. Les analyses de RT-PCR en temps réel révèlent que l’expression des gènes HbERF-IXc4 et HbERF-IXc5 est plus forte dans les lignées transgéniques que la lignée sauvage. L’analyse fine des lignées HbERF-IXc5 montre aussi des modifications anatomiques (activité cambiale, nombre de cellules laticifères, amidon, et largeur du xylème).Ce travail est la première analyse fonctionnelle de facteurs de transcription chez Hevea. Des différences ont été observées entre les lignées HbERF-IXc4 et HbERF-IXc5. Comme ERF1, HbERF-IXc4 et HbERF-IXc5 doivent diriger la réponse à certains stress. HbERF-IXc5 serait un régulateur de la différentiation des laticifers. Cette étude pourrait être complétée par des analyses dans des lignées éteintes pour ces gènes, une comparaison des transcriptomes et métabolome de lignées sauvages et transgéniques, et l’identification des gènes cibles contrôlés par HbERF-IXc4 et HbERF-IXc5
Natural rubber (NR) (cis-1,4-polyisoprene) is the main production from Hevea brasiliensis. NR is a very important industrial material for transportation, consumer, and medical. The demand for NR is increasing from year to year. NR is obtained from latex. The latex flows out from laticifers after tapping the bark. Ethephon, an ethylene releaser, can be applied on certain clones to stimulate the latex production. Tapping and ethephon stimulation are sources of harvesting stresses conducing to the production of secondary metabolites and consequent rubber. Ethylene (ET) and jasmonic acid (JA) biosynthesis and signalling pathways play a crucial role in the response to latex harvesting stress. Two Hevea ethylene response factor genes, HbERF-IXc4 and HbERF-IXc5, were predicted to be orthologue to ERF1 from Arabidopsis. ERF1 was suggested to be a key component of defence responses through the integration of ethylene and jasmonic acid signalling pathways. Transcripts of HbERF-IXc4 and HbERF-IXc5 were dramatically accumulated by combining wounding, methyl jasmonate, and ethylene treatment. These factors were assumed to be a key regulator at the crosstalk of ethylene and jasmonate signalling pathways in latex cells. HbERF-IXc4 and HbERF-IXc5 have several features of transcription factor revealed by transactivation experiment and subcellular localization, respectively: they can activate the GCC cis-acting element of promoters of target genes and are localized in nucleus, In this study, functional analysis of HbERF-IXc4 and HbERF-IXc5 genes have been carried out by overexpression under control of two promoters, 35S CaMV and HEV2.1 in transgenic Hevea lines obtained by Agrobacterium tumefaciens-mediated genetic transformation. This overexpression led to emphasize the effect of native HbERF-IXc4 and HbERF-IXc5 genes. Twenty-nine GFP-positive lines were established on paromomycin selection medium. Twelve lines regenerated plants but only ten led to produce a sufficient number of plants for further phenotyping with totally 1,622 transgenic plants in greenhouse. These ten ines were confirmed as transgenic by Southern blot hybridization. Observation of morphology until one year showed both genes (HbERF-IXc4 and HbERF-IXc5) promoted a better growth in terms of plant height, stem diameter, and weight of aerial and root system with higher vigour and better tolerance to some abiotic stresses. Plants overexpressing HbERF-IXc5 have also a better performance than HbERF-IXc4. Data also showed a vigorous root system well balanced with regard to the whole plant. Real-time RT-PCR analyses revealed that expression of HbERF-IXc4 and HbERF-IXc5 genes was higher in transgenic lines compared to wild-type . Analysis in details of HbERF-IXc5 lines also showed some changes in anatomy (cambium activity, number of latex cells, starch, and width of xylem).This work is the first successful functional analysis of transcription factors in Hevea. Some differences have been observed between HbERF-IXc4 and HbERF-IXc5. As ERF1, HbERF-IXc4 and HbERF-IXc5 should drive the response to some stresses. HbERF-IXc5 might be a regulator of laticifer differentiation. This study could be completed with analysis of silenced transgenic lines, comparison of transcriptome, metabolome of wild-type and transgenic lines, and identification of target genes controlled by HbERF-IXc4 and HbERF-IXc5

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. (authors' abstract)

Présentation mathématique des méthodes d'analyse des données. Schéma des traitements et analyses globales des dossiers. Appréciation des demandes de crédit à court terme, à moyen terme et à long terme. Evaluation du prix des biens durables d'une espèce donnée.

The overall purpose of this dissertation is to investigate how outliers affect the decisions about the number of factors in exploratory factor analysis (EFA) as determined by four widely used and/or highly recommended methods. Very few studies have looked into this issue in the literature and the conclusions are contradictory— i.e., with studies disagreeing as to whether outliers result in extra factors or a reduced number of factors. For this dissertation I systematically studied the impact of outliers arising from different sources and matched outlier simulation models with different type of outliers. Chapter 1 provides an overview of the gap between statistical theory regarding outliers and researchers’ day-to-day practice and their understanding of the effects of outliers. Chapter 2 presents a review of EFA with an emphasis on the four commonly used or highly recommended decision methods on the number of factors as well as a review of outliers which includes the sources of outliers and problems of outliers in factor analysis. Chapter 3 examines the effects of outliers arising from errors using the deterministic and slippage models. The results revealed that outliers can inflate, deflate, or have no effects on the decisions about the number of factors, which depends on the decision method used and the magnitude and number of outliers. Chapter 4 investigates the effects of outliers arising from an unintended and unknowingly included subpopulation using the mixture contamination model. The general conclusions are similar to chapter 3, but chapter 4 also reveals that symmetric and asymmetric contamination has different effects on different decision methods and the effects of outliers do not depend on sample size. Chapter 5 provides a general discussion of the findings of this dissertation, describes four novel contributions, and points out the limitations of the present research as well as the future research directions. This dissertation aims to bridge the gap from day-to-day researchers’ practice and understanding of the effects of outliers to current outlier research that emphasizes robust statistics. The findings of this dissertation address the contradictory conclusions made in previous studies.

In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.

In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables is a simpler approach for dimensionality reduction. Dimensionality reduction is useful in speech recognition, data compression, visualization and exploratory data analysis. Some of the techniques which can be used for dimensionality reduction are; Factor Analysis (FA), Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA). Factor Analysis can be considered as an extension of Principal Component Analysis. The EM (expectation maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation, conditioned upon the obervations. The maximization step then provides a new estimate of the parameters. This research work compares the techniques; Factor Analysis (Expectation-Maximization algorithm based), Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction and investigates Local Factor Analysis (EM algorithm based) and Local Principal Component Analysis using Vector Quantization.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Engineering
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Les facteurs transcriptionnels (TF) sont des protéines qui contrôlent l'expression des gènes. Leurs motifs de liaison (TFBM, également appelés motifs) sont généralement représentés sous forme de matrices de scores spécifiques de positions (PSSM). L'analyse de motifs est utilisée en routine afin de découvrir des facteurs candidats pour la régulation d'un jeu de séquences d'intérêt. L'avénement des méthodes à haut débit a permis de détecter des centaines de motifs, qui sont disponibles dans des bases de données. Durant ma thèse, j'ai développé deux nouvelles méthodes et implémenté des outils logiciels pour le traitement de collections massives de motifs: matrix-clustering regroupe les motifs par similarité; position-scan détecte les motifs présentant des préférences de position relativement à une coordonnée de référence. Les méthodes que j'ai développées ont été évaluées sur base de cas d'études, et utilisées pour extraire de l'information interprétable à partir de différents jeux de données de Drosophila melanogaster et Homo sapiens. Les résultats démontrent la pertinence de ces méthodes pour l'analyse de données à haut débit, et l'intérêt de les intégrer dans des pipelines d'analyse de motifs
Transcription Factors (TFs) are DNA-binding proteins that control gene expression. TF binding motifs (TFBMs, simply called “motifs”) are usually represented as Position Specific Scoring Matrices (PSSMs), which can be visualized as sequence logos. The advent of high-throughput methods has allowed the detection of thousands of motifs which are usually stored in databases. In this work I developed two novel methods and implemented software tools to handle large collection of motifs in order to extract interpretable information from high-throughput data: (i) matrix-clustering regroups motifs by similarity and offers a dynamic interface; (2) position-scan detects TFBMs with positional preferences relative to a given reference location (e.g. ChIP-seq peaks, transcription start sites). The methods I developed have been evaluated based on control cases, and applied to extract meaningful information from different datasets from Drosophila melanogaster and Homo sapiens. The results show that these methods enable to analyse motifs in high-throughput datasets, and can be integrated in motif analysis workflows

The event tourism industry is one of the fastest growing tourism industries worldwide. One type of event that is growing immensely is festivals, especially music festivals such as the Cape Town International Jazz Festival. As a result of the fast growing pace of festivals, it has become crucial for a festival to sustain itself in the market place to stay competitive. The Cape Town International Jazz Festival (the Jazz Festival) is a fast growing music festival and hosts numerous well–known local and international jazz artists, as well as young up–and–coming artists. For this exciting Jazz Festival to keep growing, it needs to be sustainable. To achieve this, the organisers and managers of the Jazz Festival need to know what is important to the visitors of the Jazz Festival, so they can fulfil their needs. This in turn leads to satisfied visitors that will return to the Jazz Festival and keep the festival sustainable. Generally speaking, music festivals have a more professional management approach than other tourism events and thus are more likely to be more successful. Key Success Factors (KSFs) are a precondition for the success of any event and will influence the competitiveness of the event in the market place. It is imperative for organisers to identify the KSFs that are important to the visitors so as to provide them with a satisfactory experience. This will also assist in measuring the achievement of the event’s goals and objectives. The main purpose of this study was to determine the KSFs in managing the visitor experience at the Cape Town International Jazz Festival. To reach this goal, the study is divided into two articles. Research for both articles was conducted at the Cape Town International Jazz Festival through distributing 400 questionnaires randomly throughout the two days of the festival, which was held on 3 and 4 April 2010. Article 1 is titled: “Key aspects for efficient and effective management of the Cape Town International Jazz Festival: a visitor’s perspective”. The main purpose of this article was to identify the Key Success Factors in managing the Cape Town International Jazz Festival, to determine what visitors deemed as important when attending the Jazz Festival. A factor analysis was done to achieve this goal. Results indicated that Hospitality Factors, Quality Venues, Information Dissemination, Marketing and Sales, and Value and Quality are the KSFs that are of importance when managing the Jazz Festival. The results of this article provided festival managers with valuable information when organising an event such as the Cape Town International Jazz Festival. Article 2 is titled: “The importance of different Key Success Factors to different target markets of the Cape Town International Jazz Festival based on travel motives”. The main purpose of this article was to determine whether different target markets that are visiting the Jazz Festival, deemed different KSFs as important, depending on their travel motives. An analysis of variance (ANOVA) was done to determine if there were statistically significant differences between the three clusters and the KSFs that they deemed important. Results showed that the three clusters, namely, Escapists, Culture Seekers and Jazz Lovers, deemed different KSFs as important when they are visiting the Jazz Festival. The results of this article gave festival organisers and marketing managers insight as to which markets to focus scarce marketing resources on and which markets to keep growing, as they will sustain the festival in the long term. Therefore, this research revealed the KSFs that are of utmost importance when managing the Cape Town International Jazz Festival, and that these aspects differ for certain markets. Organisers therefore need to assess the KSFs to provide products that will satisfy the visitor in order for him/her to return each year and keep the festival competitive and sustainable.
Thesis (M.Com. (Tourism))--North-West University, Potchefstroom Campus, 2012.

Cette thèse décrit l'utilisation d'aptamères peptidiques (PA) comme outil d'étude de la signalisation de Ras dans des modèles de lignées cellulaires. Elle décrit ensuite les analyses initiales de l'effet biologique de petites molécules sélectionnées pour leur capacité à inhiber l'interaction entre PAs et H-RasV12. Finalement elle présente la caractérisation de précurseurs de neurones de DRG et que nous espérons pouvoir utiliser pour de futures études. Afin d'analyser les interactions entre Ras et différentes protéines et les perturbations de la voie de signalisation, PAs dirigés contre la protéine Ras sauvage et Ras oncogène avaient été sélectionnés. PA 105R présente un phénotype d'interaction en double hybride de levure (Y2H) avec H-, K-, et N-Ras. PA HR3C présente un phénotype d'interaction avec la protéine Ras sauvage et H-RasV12. Dans les cellules PC12, HR3C bloque aussi bien la signalisation de Ras sauvage que celle de H-Ras oncogène. Par contre dans les cellules AsPC1, la protéine oncogène K-RasD12 endogène est inhibée par 105R, alors que HR3C est bien moins efficace. Des mutations dirigées contre les acides aminés localisés à la surface de HrasV12, suivi d'un test d'interaction en Y2H, ont permis de cartographier les sites de liaison des PAs biologiquement actifs sur H-RasV12. L'activité biologique d'un certain nombre de petites molécules sélectionnées pour leur capacité à dissocier l'un des PAs spécifique de Ras. Ces études ont abouti à l'identification d’une molécules qui pourrait être biologiquement pertinente. Des études supplémentaires seront requises afin d'obtenir, à partir de ces molécules, de possibles candidats aux études pré-cliniques. Finalement, nous avons mis en place la technologie des PAs pour une utilisation dans des cellules primaires dérivées rat DRG, et développé une lignée précurseur de cellules neuronales de DRG que nous avons maintenue en culture 2 ans. Nous espérons ainsi utiliser ces précurseurs pour des études futures dans le PNS
This thesis describes the use of peptide aptamers (PA) for the study of Ras signaling in model cell lines. It further describes the initial analysis of biological effects of small molecules identified for their capacity to displace a biologicallyactive PA from H-RasV12. Finally, it offers characterization of DRGn precursors that we hope to be able to use for future studies. To analyze Ras interactions with other proteins and perturbation of signaling pathways, PAs directed towards wt Ras and oncogenic Ras had been selected. PA 105R elicited an interaction phenotype in yeast two–hybrid (Y2H) with H-, K-, and N-Ras. PA HR3C gave an interaction phenotype with H-Ras wt and HrasV12. In PC12 cells, it blocked both wt and oncogenic H-Ras signaling, whereas 105R had little effect on NGF-induced signaling, but inhibited that stimulated by oncogenic Ras. In contrast, in AsPC-1, endogenous oncogenic K-RasD12 is inhibited by 105R, while HR3C is much less effective. Targeted mutation of amino acids located on the surface of H-RasV12, followed by Y2H interaction mating assays, offered the possibility to map the binding sites of the biologically-active Pas. We evaluated biological activity of a number of small molecules selected for their capacity to displace one of the Ras-specific PAs from the target. These studies resulted in the identification of one molecule that may be biologically pertinent. Further studies will be required to develop possible pre-clinical candidates based on these molecules. Finally, we were to implement the PA technology in primary cells derived from rat DRG, and we developed a DRG precursor line that we have maintained in culture for over two years. It is hoped that we use thes precursors for future studies in the PNS