It is becoming increasingly important to understand biological networks in order to understand complex diseases, identify novel, safer protein targets for therapies and design efficient drugs. 'Systems biology' has emerged as a discipline to uncover biological networks through genomic data. Computational methods for identifying these networks become immensely important and have been growing in number in parallel to increasing amount of genomic data under the discipline of 'Systems Biology'. In this thesis we introduced novel computational methods for identifying topological and dynamic properties of biological networks. Biological data is available in various forms. Experimental data on the interactions between biological components provides a connectivity map of the system as a network of interactions and time series or steady state experiments on concentrations or activity levels of biological constituents will give a dynamic picture of the web of these interactions. Biological data is scarce usually relative to the number of components in the networks and subject to high levels of noise. The data is available from various resources however it can have missing information and inconsistencies. Hence it is critical to design intelligent computational methods that can incorporate data from different resources while considering noise component. This thesis is organized as follows; Chapter 1 and 2 will introduce the basic concepts for biological network types. Chapter 2 will give a background on biochemical network identification data types and computational approaches for reverse engineering of these networks. Chapter 3 will introduce our novel constrained total least squares approach for recovering network topology and dynamics through noisy measurements. We proved our method to be superior over existing reverse engineering methods. Chapter 4 is an extension of chapter 3 where a Bayesian parameter estimation algorithm is presented that is capable of incorporating noisy time series and prior information for the connectivity of network. The quality of prior information is critical to be able to infer dynamics of the networks. The major drawback of prior connectivity data is the presence of false negatives, missing links. Hence, powerful link prediction methods are necessary to be able to identify missing links. At this junction a novel link prediction method is introduced in Chapter 5. This method is capable of predicting missing links in a connectivity data. An application of this method on protein-protein association data from a literature mining database will be demonstrated. In chapter 6 a further extension into link prediction applications will be given. An interesting application of these methods is the drug adverse effect prediction. Adverse effects are the major reason for the failure of drugs in pharmaceutical industry, therefore it is very important to identify potential toxicity risks in the early drug development process. Motivated by this chapter 6 introduces our computational framework that integrates drug-target, drug-side effect, pathway-target and mouse phenotype-mouse genes data to predict side effects. Chapter 7 will give the significant findings and overall achievements of the thesis. Subsequent steps will be suggested that can follow the work presented here to improve network prediction methods.

<p>This thesis consists of four independent papers concerningdifferent aspects of interior methods for optimization. Threeof the papers focus on theoretical aspects while the fourth oneconcerns some computational experiments.</p><p>The systems of equations solved within an interior methodapplied to a convex quadratic program can be viewed as weightedlinear least-squares problems. In the first paper, it is shownthat the sequence of solutions to such problems is uniformlybounded. Further, boundedness of the solution to weightedlinear least-squares problems for more general classes ofweight matrices than the one in the convex quadraticprogramming application are obtained as a byproduct.</p><p>In many linesearch interior methods for nonconvex nonlinearprogramming, the iterates can "falsely" converge to theboundary of the region defined by the inequality constraints insuch a way that the search directions do not converge to zero,but the step lengths do. In the sec ond paper, it is shown thatthe multiplier search directions then diverge. Furthermore, thedirection of divergence is characterized in terms of thegradients of the equality constraints along with theasymptotically active inequality constraints.</p><p>The third paper gives a modification of the analytic centerproblem for the set of optimal solutions in linear semidefiniteprogramming. Unlike the normal analytic center problem, thesolution of the modified problem is the limit point of thecentral path, without any strict complementarity assumption.For the strict complementarity case, the modified problem isshown to coincide with the normal analytic center problem,which is known to give a correct characterization of the limitpoint of the central path in that case.</p><p>The final paper describes of some computational experimentsconcerning possibilities of reusing previous information whensolving system of equations arising in interior methods forlinear programming.</p><p><b>Keywords:</b>Interior method, primal-dual interior method,linear programming, quadratic programming, nonlinearprogramming, semidefinite programming, weighted least-squaresproblems, central path.</p><p><b>Mathematics Subject Classification (2000):</b>Primary90C51, 90C22, 65F20, 90C26, 90C05; Secondary 65K05, 90C20,90C25, 90C30.</p>

This paper is concerned with the numerical analysis of the autoconvolution equation $x*x=y$ restricted to the interval [0,1]. We present a discrete constrained least squares approach and prove its convergence in $L^p(0,1),1<p<\infinite$ , where the regularization is based on a prescribed bound for the total variation of admissible solutions. This approach includes the case of non-smooth solutions possessing jumps. Moreover, an adaption to the Sobolev space $H^1(0,1)$ and some remarks on monotone functions are added. The paper is completed by a numerical case study concerning the determination of non-monotone smooth and non-smooth functions x from the autoconvolution equation with noisy data y.

This paper deals with optimisation of food overloading at long distance flights. It is described how in case of two offered meals and two several passenger groups reserve meals are to distribute to both meals such that the probability that each passenger will get the meal of its choice is maximised. A statistical procedure is presented for estimation of needed demand probabilities.

Recent advances in travel demand modeling have concentrated on adding behavioral realism by focusing on an individual’s activity participation. And, to account for trip-chaining, tour-based methods are largely replacing trip-based methods. Alongside these advances and innovations in dynamic traffic assignment (DTA) techniques, however, time-of-day (TOD) modeling remains an Achilles’ heel. As congestion worsens and operators turn to variable road pricing, sensors are added to networks, cell phones are GPS-enabled, and DTA techniques become practical, accurate time-of-day forecasts become critical. In addition, most models highlight tradeoffs between travel time and cost, while neglecting variations in travel time. Research into stated and revealed choices suggests that travel time variability can be highly consequential. This dissertation introduces a method for imputing travel time variability information as a continuous function of time-of-day, while utilizing an existing method for imputing average travel times (by TOD). The methods employ ordinary least squares (OLS) regression techniques, and rely on reported travel time information from survey data (typically available to researchers), as well as travel time and distance estimates by origin-destination (OD) pair for free-flow and peak-period conditions from network data. This dissertation also develops two models of activity timing that recognize the imputed average travel times and travel time variability. Both models are based in random utility theory and both recognize potential correlations across time-of-day alternatives. In addition, both models are estimated in a Bayesian framework using Gibbs sampling and Metropolis-Hastings (MH) algorithms, and model estimation relies on San Francisco Bay Area data collected in 2000. The first model is the continuous cross-nested logit (CCNL) and represents tour outbound departure time choice in a continuous context (rather than discretizing time) over an entire day. The model is formulated as a generalization of the discrete cross-nested logit (CNL) for continuous choice and represents the first random utility maximization model to incorporate the ability to capture correlations across alternatives in a continuous choice context. The model is then compared to the continuous logit, which represents a generalization of the multinomial logit (MNL) for continuous choice. Empirical results suggest that the CCNL out-performs the continuous logit in terms of predictive accuracy and reasonableness of predictions for three tolling policy simulations. Moreover, while this dissertation focuses on time-of-day modeling, the CCNL could be used in a number of other continuous choice contexts (e.g., location/destination, vehicle usage, trip durations, and profit-maximizing production). The second model is a bivariate multinomial probit (BVMNP) model. While the model relies on discretization of time (into 30-minute intervals), it captures both key dimensions of a tour’s timing (rather than just one, as in this dissertation’s application of the CCNL model), which is important for tour- and activity-based models of travel demand. The BVMNP’s ability to capture correlations across scheduling alternatives is something no existing two-dimensional choice models of tour timing can claim. Both models represent substantial contributions for continuous choice modeling in transportation, business, biology, and various other fields. In addition, the empirical results of the models evaluated here enhance our understanding of individuals’ time-of-day decisions. For instance, average travel time and its variance are estimated to have a negative effect on workers’ utilities, as expected, but are not found to be that practically relevant here, probably because most workers are rather constrained in their activity scheduling and/or work hours. However, correlations are found to be rather strong in both models, particularly for home-to-work journeys, suggesting that if models fail to accommodate such correlations, biased application results may emerge. / text

Ein zusammengesetzter Index ist eine aggregierte Variable, die aus individuellen Indikatoren und Gewichten besteht, wobei die Gewichte die relative Wichtigkeit jedes Indikators darstellen. Zusammengesetzte Indizes werden oft benutzt um latente Phänomene zu schreiben oder komplexe Informationen zu einer geringen Anzahl an Variablen zusammenzufassen. Es ist von großer Bedeutung richtige Gewichte für die Variablen, die einen zusammengesetzten Index bilden, zu wählen. Hauptkomponentenanalyse (PCA) ist ein populärer Ansatz um Gewichte abzuleiten, aber es ist ungeeignet, wenn informative Variationen nur kleine Varianzen der Variablen in einem zusammengesetzten Index haben. Deshalb schlägt diese Studie vor, Partial Least Squares (PLS) anzuwenden, welches die Beziehung zwischen Zielvariablen and den Variablen in einem zusammengesetzten Index ausnutzt. Unsere Simulationsstudie zeigt, dass PLS so gut wie PCA funktioniert oder erheblich es übertrifft. Zusätzlich sind in der Praxis die Variablen in einem zusammengesetzten Index häufig nicht-metrisch. Solche Variablen benötigen spezielle Verfahren, um PCA oder PLS anzuwenden. Diese Studie untersucht mehrere PCA und PLS Algorithmen für nicht-metrische Variablen in der vorliegenden Literatur und vergleicht sie durch umfangreiche Simulationsstudien, um Empfehlungen für die Praxis abzugeben. Dummy coding zeigt häufig zufriedenstellende Leistung im Vergleich zu komplizierteren Methoden. Als unsere Anwendungen betrachten wir Vermögen, Globalisierung, Geschlechtergleichheit und Korruption, indem PCA- und PLS-basierte zusammengesetzte Indizes angewendet werden. PLS erzeugt für die jeweiligen Zielvariablen massgeschnittene zusammengesetzte Indizes, die häufig bessere Leistung als PCA zeigten. Ein Vergleich zwischen PCA und PLS Gewichten und Koeffizienten zeigt, welche Variablen für die jeweiligen Zielvariablen besonders relevant sind.

Training is a powerful and required method to equip human resources with tools to keep their organizations competitive in the markets. Typically at the end of class, trainees are asked to give their feelings about or satisfaction with the training. Although there are various reasons for conducting training evaluations, the common theme is the need to continuously improve a training program in the future. Among training evaluation methods, post-training surveys or questionnaires are the most commonly used way to get trainees’ reaction about the training program and “the forms will tell you to what extent you’ve been successful.” (Kirkpatrick 2006) A higher satisfaction score means more trainees were satisfied with the training. A total of 40 prediction models grouped into 10-GIQs prediction models and 6-GIQs prediction models were built in this work to predict the total training satisfaction based on trainees’ general information which included a trainee’s desire to take training, a trainee’s attitude in training class and other information related to the trainee’s work environment and other characteristics. The best models selected from 10-GIQs and 6-GIQs prediction models performed the prediction work with the prediction quality of PRED (0.15) >= 99% and PRED (0.15) >= 98%, separately. An interesting observation discovered in this work is that the training satisfaction could be predicted based on trainees information that was not related to any training experience at all. The dominant factors on training satisfaction were the trainee’s attitude in training class and the trainee’s desire to take the training which was found in 10-GIQs prediction models and 6-GIQs prediction models, separately. / text

Goal and Scope of this thesisThe curing process of epoxy resin directly affects the properties of the final polymer, so it is of great interest to develop analytical methods that allows knowing the pathway of the curing processes. There have been numerous research studies about the evolution of the curing and on the quantification of the corresponding kinetic parameters using several techniques such as: Differential Scanning Calorimetry (DSC), Differential Scanning Calorimetry with Temperature Modulation (MTDSC), Thermogravimetric analysis (TGA), Fluorescence, Raman spectroscopy, Nuclear Magnetic Resonance (NMR), High-Resolution Liquid Chromatography (HPLC), Infrared spectroscopy Fourier Transform (FTIR) and Near infrared spectroscopy (NIR). Usually, those studies are done in model reactions due to the fact that is very difficult, and sometimes even impossible, to isolate the intermediate products that are involved in the curing process. In that sense, the goal of the present thesis is to explore the possibilities of the multidimensional correlation spectroscopy for the quantitative monitorization of curing processes by means of infrared spectroscopy and curve resolution methods. The thesis is focused in a complex reaction in which several and side reactions might take place, most or all of them almost at the same time. This main goal is structured in the following items: 1. Analysis of Generalized two-dimensional correlation spectroscopy and Perturbation-correlation mowing-windows two-dimensional correlation spectroscopy as a tool to obtain information about the reaction pathway.2. Analysis of sample-sample two-dimensional correlation spectroscopy as a tool to obtain concentration profiles of the chemical species involved in the curing process.3. Quantitative resolution of the curing process by means of multivariate curve resolution methods - alternating least squares (MCR-ALS) taking into account the information coming from multidimensional correlation spectroscopy analysis. StructureThe thesis is structured in different chapters each one containing the following information.Chapter 1: This chapter presents the background of the thesis, so it is highlighted the interest of the study of resins epoxy. A brief review of the theory of the multidimensional spectroscopy and the chemometrics tools (multivariate curve resolution methods) used is presented focusing on the novelties introduced in the thesis and offering the proper references for the basic concepts already known.Chapter 2: This chapter concerns the experimental work done. It has been included a brief description of the instrumental analytical techniques used to monitor the curing process. Two main curing reactions are described: experimental conditions and scheme of the reaction between the phenylglycidylether (PGE) and &#61543;-butyrolactone monitored by NIR and of the copolymerization between the diglycidyl ether of bisphenol A (DGEBA) and &#61543;-valerolactone by FTIR/ATR. Also the conditions of the DGEBA homopolymerization are presented. And finally, the 1H and 13C NMR experimental condition to obtain the spectrum of the final product in the first reaction between PGE and &#61543;-butyrolactone is described.Chapter 3: This chapter is addressed to the results obtained as a consequence of the studies done. It is articulated in five published works and each one is introduced by a brief description of the main goal and the content of it. The five articles are presented in a sequential order according the main goal of the thesis.Chapter 4: Finally, the thesis ends with a conclusion chapter in which the achieved goals are outlined.As each paper presented in chapter 3, contains its specific conclusion section, in this chapter there are emphasized the thesis conclusions according to the goals formulated in the scope. In that sense the first and general conclusion is that Multidimensional correlation spectroscopy and multivariate curve resolution method are useful spectroscopic and chemometric methods to quantitative monitoring a curing process using infrared spectroscopy.Concretely it can be also stated that: - Generalized and perturbation-correlation moving-windows two-dimensional correlation spectroscopy, are valuable methods to obtain information about the reaction pathway in the case studied which is representative of a curing process.- Sample-sample two-dimensional correlation spectroscopy is very useful method to obtain concentration profiles of the chemical species involved in the curing process.- And finally, MCR-ALS is a very useful method for the quantitative resolution of the curing process. / El proceso de curado la resina epoxi afecta directamente las propiedades finales del polímero, por lo que es de gran interés para el desarrollo de métodos de análisis que permite conocer la vía de los procesos de polimerización. Por lo general, esos estudios se hacen en reacciones modelo, debido al hecho, que es muy difícil, a veces o incluso imposible, aislar los productos intermedios que intervienen en el proceso de curado. En ese sentido, el objetivo de la tesis es explorar las posibilidades de la espectroscopia de correlación multidimensional para la monitorización cuantitativa de los procesos de curado por medio de espectroscopia infrarrojo y métodos de resolución de la curva. La tesis se centra en una reacción compleja en la que varias reacciones secundarias y puede tener lugar, la mayoría o la totalidad de ellos casi al mismo tiempo.Las conclusiones de la tesis de acuerdo a los objetivos son formulados en el ámbito de aplicación, métodos espectroscópicos y quimiométricos. En ese sentido, la primera conclusión general y es que la espectroscopia de correlación multidimensional y método de resolución de curva multivariante son útiles para el seguimiento de un proceso de curado mediante espectroscopía de infrarrojo.Concretamente, puede ser también señaló que: - La espectroscopia generalizada de correlación bidimensional y espectroscopia de correlación de la perturbación por ventana móvil, son métodos valiosos para obtener información sobre el camino de reacción en el caso estudiado, que es representativa de un proceso de curado.- La espectroscopia de correlación bidimensional muestra-muestra es un metodo útil para obtener perfiles de concentración de las especies químicas involucradas en el proceso de curado.- Por último, MCR-ALS es un método muy útil para la resolución cuantitativa del proceso de curado.

The purpose of this analysis is to evaluate the usefulness of Fourier Transform Infrared (FTIR) spectroscopy in the detection of Herpes Simplex Virus 1 (hsv1) infection at an early stage. The raw absorption values were standardized to eliminate inter-sampling error. Wilcoxon-Mann-Whitney (WMW) statistic's Z score was calculated to select significant spectral regions. Partial least squares modeling was performed because of multicollinearity. Kolmogorov-Smirnov statistic showed models for healthy tissues from different time groups were not from same distribution. The additional 24 hour dataset was evaluated using the following methods. Variables were selected by WMW Z score. Difference of Composites statistic, DC, was created as a disease indicator and evaluated using area under the ROC curve, specificities, and confidence intervals using bootstrap algorithm. The specificity of DC was high, however the confidence intervals were large. Future studies are required with larger sample sizes to test this statistic's usefulness.

Darbe atlikta eksponentinių-sinusinių modelių įvertinimo specialiu mažiausių kvadratų metodu analizė. Apžvelgti pagrindiniai signalo parametrai. Aprašyti signalo modeliai bei jų formos. Išnagrinėtas visuminių mažiausių kvadratų metodas bei jam alternatyvūs metodai: kovariacinis, Tufts-Kumaresan ir Pisarenko. Pateikti šių metodų matematiniai aprašymai. Signalų modelių parametrų analizei sukurtos MATLAB programos bei pateikti jų programiniai kodai. Skaitiniais eksperimentais ištirta, kaip kiekvienas iš metodų veikia, esant skirtingam signalo-triukšmo santykiui. Gauti rezultatai iliustruoti grafiškai. Remiantis sumodeliuotais rezultatais, suformuluotos išvados apie nagrinėjamų metodų galimybes. / The aim of this study is to explore exponential-sinusoidal signal model estimation by a special least squares method. The main signal parameters are considered. Signal models and their forms are described. The total least squares method as well as its alternatives – the covariance, Tufts-Kumaresan and Pisarenko methods – are analysed. The mathematical description of these methods is given. MATLAB–based programs to analyse signal model parameters are developed and their codes are presented. We investigated the performance of each of these methods for different signal noise ratio values. The results obtained are illustrated graphically. Conclusions about the method properties are drawn on the basis of simulation experiments.