<p>New techniques and approaches for organic synthesis, purification and biological testing are enabling pharmaceutical industries to produce and test increasing numbers of compounds every year. Surprisingly, this has not led to more new drugs reaching the market, prompting two questions – why is there not a better correlation between their efforts and output, and can it be improved? One possible way to make the drug discovery process more efficient is to ensure, at an early stage, that the tested compounds are diverse, representative and of high quality. In addition the biological evaluation systems have to be relevant and reliable. The diversity of the tested compounds could be ensured and the reliability of the biological assays improved by using Design Of Experiments (DOE) more frequently and effectively. However, DOE currently offers insufficient options for these purposes, so there is a need for new, tailor-made DOE strategies. The aim of the work underlying this thesis was to develop and evaluate DOE approaches for diverse compound selection and efficient assay optimisation. This resulted in the publication of two new DOE strategies; D-optimal Onion Design (DOOD) and Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP), both of which are extensions to established experimental designs.</p><p>D-Optimal Onion Design (DOOD) is an extension to D-optimal design. The set of possible objects that could be selected is divided into layers and D-optimal selection is applied to each layer. DOOD enables model-based, but not model-dependent, selections in discrete spaces to be made, since the selections are not only based on the D-optimality criterion, but are also biased by the experimenter’s prior knowledge and specific needs. Hence, DOOD selections provide controlled diversity.</p><p>Assay development and optimisation can be a major bottleneck restricting the progress of a project. Although DOE is a recognised tool for optimising experimental systems, there has been widespread unwillingness to use it for assay optimisation, mostly because of the difficulties involved in performing experiments according to designs in 96-, 384- and 1536- well formats. The RED-MUP framework combines classical experimental designs orthogonally onto rectangular experimental platforms, which facilitates the execution of DOE on these platforms and hence provides an efficient tool for assay optimisation.</p><p>In combination, these two strategies can help uncovering the crossroads between biology and chemistry in drug discovery as well as lead to higher information content in the data received from biological evaluations, providing essential information for well-grounded decisions as to the future of the project. These two strategies can also help researchers identify the best routes to take at the crossroads linking biological and chemical elements of drug discovery programs.</p>

Ground penetrating radar (GPR) systems are increasingly being used for the detection and location of buried objects within the upper regions of the earth’s surface. The antenna is the most critical component of such a system. This thesis presents a comprehensive study of resistor-loaded planar dipole antennas for GPR applications using both theory and experiments. The theoretical analysis is performed using the finite difference time domain (FDTD) technique. The analysis starts with the most popular planar dipole, the bow-tie. A parametric study is done to find out how the flare angle, length, and lumped resistors of the antenna should be selected to achieve broadband properties and good target detection with less clutter. The screening of the antenna and the position of transmitting and receiving antennas with respect to each other and ground surface are also studied. A number of other planar geometrical shapes are considered and compared with the bow-tie in order to find what geometrical shape gives the best performance. The FDTD simulations are carried out for both lossless and lossy, dispersive grounds. Also simulations are carried out including surface roughness and natural clutter like rocks and twigs to make the modeling more realistic. Finally, a pair of resistor-loaded bow-tie antennas is constructed and both indoor and outdoor measurements are carried out to validate the simulation results. / <p>QC 20100923</p>

In the present thesis, computational methods for theanalysis and design of photonic bandgap structure areconsidered. Many numerical methods have been used to study suchstructures. Among them, the plane wave expansion method is veryoften used. Using this method, we show that inclusions ofelliptic air holes can be used effectively to obtain a largercomplete band gap for two-dimensional (2D) photonic crystals.An optimal design of a 2D photonic crystal is also consideredin the thesis using a combination of the plane wave expansionmethod and the conjugate gradient method. We find that amaximum complete 2D band gap can be obtained by connectingdielectric rods with veins for a photonic crystal with a squarelattice of air holes in GaAs. For some problems, such as defect modes, the plane waveexpansion method is extremely time-consuming. It seems that thefinite-difference time-domain (FDTD) method is promising, sincethe computational time is proportional to the number of thediscretization points in the computation domain (i.e., it is oforderN). A FDTD scheme in a nonorthogonal coordinate systemis presented in the thesis to calculate the band structure of a2D photonic crystal consisting of askew lattice. The algorithmcan easily be used for any complicated inclusion configuration,which can have both the dielectric and metallic constituents.The FDTD method is also applied to calculate the off-plane bandstructures of 2D photonic crystals in the present thesis. Wealso propose a numerical method for computing defect modes in2D crystals (with dielectric or metallic inclusions). Comparedto the FDTD transmission spectra method, our method reduces thecomputation time and memory significantly, and finds as manydefect modes as possible, including those that are not excitedby an incident plane wave in the FDTD transmission spectramethod. The FDTD method has also been applied to calculateguided modes and surface modes in 2D photonic crystals using acombination of the periodic boundary condition and theperfectly matched layer for the boundary treatment. Anefficient FDTD method, in which only real variables are used,is also proposed for the full-wave analysis of guided modes inphotonic crystal fibers. / QC 20100629

In this paper we investigate (d + 1)-point D-optimal designs for d-th degree polynomial regression with weight function w(x) > 0 on the interval [a, b]. Suppose that w'(x)/w(x) is a rational function and the information of whether the optimal support contains the boundary points a and b is available. Then the problem of constructing (d + 1)-point D-optimal designs can be transformed into a differential equation problem leading us to a certain matrix with k auxiliary unknown constants. We characterize the weight functions corresponding to the cases when k= 0 and k= 1. Then, we can solve (d + 1)-point D-optimal designs directly from differential equation (k = 0) or via eigenvalue problems (k = 1). The numerical results show us an interesting relationship between optimal designs and ordered eigenvalues.

Consider the minimally-supported D-optimal designs for dth degree polynomial regression with bounded and positive weight function on a compact interval. We show that the optimal design converges weakly to the arcsin distribution as d goes to infinity. Comparisons of the optimal design with the arcsin distribution and D-optimal arcsin support design by D-efficiencies are also given. We also show that if the design interval is [−1, 1], then the minimally-supported D-optimal design converges to the D-optimal arcsin support design with the specific weight function 1/¡Ô(£\-x^2), £\>1, as £\¡÷1+.

Consider the D-optimal designs for the dth-degree polynomial regression model with a bounded and positive weight function on a compact interval. As the degree of the model goes to infinity, we show that the D-optimal design converges weakly to the arcsin distribution. If the weight function is equal to 1, we derive the formulae of the values of the D-criterion for five classes of designs including (i) uniform density design; (ii) arcsin density design; (iii) J_{1/2,1/2} density design; (iv) arcsin support design and (v) uniform support design. The comparison of D-efficiencies among these designs are investigated; besides, the asymptotic expansions and limits of their D-efficiencies are also given. It shows that the D-efficiency of the arcsin support design is the highest among the first four designs.

New techniques and approaches for organic synthesis, purification and biological testing are enabling pharmaceutical industries to produce and test increasing numbers of compounds every year. Surprisingly, this has not led to more new drugs reaching the market, prompting two questions – why is there not a better correlation between their efforts and output, and can it be improved? One possible way to make the drug discovery process more efficient is to ensure, at an early stage, that the tested compounds are diverse, representative and of high quality. In addition the biological evaluation systems have to be relevant and reliable. The diversity of the tested compounds could be ensured and the reliability of the biological assays improved by using Design Of Experiments (DOE) more frequently and effectively. However, DOE currently offers insufficient options for these purposes, so there is a need for new, tailor-made DOE strategies. The aim of the work underlying this thesis was to develop and evaluate DOE approaches for diverse compound selection and efficient assay optimisation. This resulted in the publication of two new DOE strategies; D-optimal Onion Design (DOOD) and Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP), both of which are extensions to established experimental designs. D-Optimal Onion Design (DOOD) is an extension to D-optimal design. The set of possible objects that could be selected is divided into layers and D-optimal selection is applied to each layer. DOOD enables model-based, but not model-dependent, selections in discrete spaces to be made, since the selections are not only based on the D-optimality criterion, but are also biased by the experimenter’s prior knowledge and specific needs. Hence, DOOD selections provide controlled diversity. Assay development and optimisation can be a major bottleneck restricting the progress of a project. Although DOE is a recognised tool for optimising experimental systems, there has been widespread unwillingness to use it for assay optimisation, mostly because of the difficulties involved in performing experiments according to designs in 96-, 384- and 1536- well formats. The RED-MUP framework combines classical experimental designs orthogonally onto rectangular experimental platforms, which facilitates the execution of DOE on these platforms and hence provides an efficient tool for assay optimisation. In combination, these two strategies can help uncovering the crossroads between biology and chemistry in drug discovery as well as lead to higher information content in the data received from biological evaluations, providing essential information for well-grounded decisions as to the future of the project. These two strategies can also help researchers identify the best routes to take at the crossroads linking biological and chemical elements of drug discovery programs.

Dans le but de réduire la consommation d’énergie et d’augmenter la part des énergies renouvelables, la conception optimale des futurs bâtiments (bâtiments intelligents) apparaît comme un facteur important. Cette thèse vise donc à développer des modèles, des méthodes innovantes d’aide à la conception pour ces bâtiments. Notre nouvelle approche de conception est une optimisation globale et simultanée de l’enveloppe, des systèmes énergétiques et de leurs stratégies de gestion dès la phase d’esquisse, qui prend en compte plusieurs critères de coût (investissement et exploitation) et de confort (thermique, visuel et aéraulique). Le problème d’optimisation multi-objectif est donc un problème de couplage fort de grande taille avec de nombreuses variables et contraintes, qui induisent des difficultés lors de sa résolution. Après avoir fait des analyses sur des cas tests, une méthode d’optimisation d’ordre 1 est choisie, en association à des modèles analytiques dérivés formellement de manière automatique. Notre méthodologie est appliquée à la conception de maisons individuelles, et plus particulièrement des maisons à énergie positive. Les résultats obtenus par cette approche globale apportent des informations importantes aux concepteurs pour l’aider à faire des choix en phase amont du processus de conception. / In order to reduce the energy consumption and to increase the use of renewable energy, the optimal design of future buildings (smart-buildings) appears as an important factor.This thesis aims to develop models, innovative methods aiding decision-making during the design of buildings. Our approach of design is a global and simultaneous optimization of envelope, energy systems and their management strategies from the sketch phase, which takes into account multi-criterions of costs (investment et exploitation) and comforts (thermal, visual, aeraulic). The multi-objective optimization problem is so a strong coupling problem of large scale with a lot of variables and constraints, which leads to difficulties to solve.After the tests, an optimization method of order 1 is chosen in combination with analytical models formally derived automatically. Our methodology is applied to the design of individual houses, especially positive energy houses. The results of this global approach provide important information to designers to help make choices from the preliminary phase of the design process.

A ce jour, la fabrication additive est développée avec plusieurs procédés qui sont capables de fabriquer les pièces en plastique ou en métallique. De plus, la FA a des avantages qui sont des limites pour la technologie traditionnelle. Par exemple, grâce à la liberté de la forme de la pièce, il n’y pas besoin des outillages spécifiques. Mais la FA a aussi des inconvénients, par exemple, la qualité de surface de la pièce, normalement faible, le cout, de la fabrication, notamment à cause de l’investissement pour la machine de fabrication et aussi la matière première. Il y a beaucoup de différences entre la FA et les technologies traditionnelles. Nous considérons donc qu’il est nécessaire d’avoir un nouveau processus de conception pour la fabrication additive On étudie une méthode de conception pour la fabrication additive qui nous permet de fabriquer une pièce ou un mécanisme de manière optimale.Notre travail de thèse est appuyé sur les deux questions de recherche :• Quel modèle pour le processus de conception d’une pièce par fabrication additive ?o Comment tirer les profits de l’avantage de la FA, par exemple, la liberté de la forme ?o Comment intégrer des caractéristiques du procédé de FA dans le processus de conception ?• Quelle est l’influence de la représentation intermédiaire dans le processus de conception pour la fabrication additive ?o Les types de représentations intermédiaires avec la FAo Le choix du type de représentation intermédiaire pour évaluer le produit dans un moment spécifique du processus de conception pour la FA.En première temps, nous avons développé une modèle de processus qui permet de prise en compte la caractéristique des procédés, ainsi de tirer les profits de l’avantage de fabrication additive.En deuxième temps, nous avons déterminé une méthode de choix qui nous permet de choisir entre les types de représentations intermédiaire pour évaluer le produit dans un processus de conception pour la FA. Cette méthode est basé sur Case Based Reasoning. / To date, additive manufacturing is being developed with several processes that are capable of manufacturing plastic or metal parts. In addition, the FA has advantages that are limitations for traditional technology. For example, thanks to the freedom of the shape of the part, there is no need for specific tools. But the FA also has disadvantages, for example, the surface quality of the part, normally low, cost, manufacturing, especially because of investment for the manufacturing machine and also the raw material. There are many differences between AF and traditional technologies. We therefore consider it necessary to have a new design process for additive manufacture. A design method for additive manufacturing is being studied which allows us to manufacture a part or mechanism in an optimal way.Our thesis work is based on the two research questions:• Which model for the design process of a part by additive manufacturing?O How to draw the benefits of the FA advantage, for example, freedom of form?O How to integrate AF process characteristics into the design process?• What is the influence of intermediate representation in the design process for additive manufacturing?O Types of intermediate representations with FAO The choice of intermediate representation type to evaluate the product at a specific time in the design process for the FA.In the first phase, we have developed a process model that takes into account the characteristic of the processes, thus taking advantage of the additive manufacturing advantage.As a second step, we determined a method of choice that allows us to choose between the types of intermediate representations to evaluate the product in a design process for FA. This method is based on Case Based Reasoning.

Cette thèse aborde la conception optimale des chaînes de traction par l'exemple d'une chaîne de traction pour métro. Les données d'entrée de la conception d'une chaîne de traction sont la performance attendue et l'encombrement des équipements nécessaires. Pour aider le concepteur, l'outil informatique donne aujourd'hui la possibilité de construire une grande variété de modèles. D'autre part, les algorithmes d'optimisation permettent de trouver les configurations optimales. Ces possibilités ont été exploitées au cours de ce travail. Les modèles développés permettent d'estimer un large nombre de critères. A travers l'exemple de ces modèles, la méthode de conception suivie est détaillée dans le rapport. D'un point de vue technique, la chaîne de traction proposée est détaillée dans le dernier chapitre / This thesis deals with the optimal design of electric drivetrains. A drivetrain for metro is taken as an example. The input data for the design are the expected performance and the size of the required equipments.To assist the designer, the computing tool now gives the opportunity to build different sort of models. In addition, optimization algorithms allow finding optimal configurations. This work is an attempt to exploit these possibilities. The design models are used to value a large number of criteria. Through the example of these models, the design method followed is detailed in the document. From a technical point of view, the proposed drivetrain is detailed in the last chapter