In this thesis we construct a new model of the immune response to the growth of breast cancer cells and investigate the impact of certain drug therapies on the cancer. We use delay differential equations to model the interaction of breast cancer cells with the immune system. The new model is constructed by combining two previous models. The first model accounts for different cell cycles and includes terms to evaluate drug treatments, but ignores quiescent tumor cells. The second model includes quiescent cells, but ignores the immune response and drug treatments. The new model is obtained by combining and modifying these two models to account for quiescent cells, immune cells and includes drug intervention terms. This new model is used to evaluate the effects of pulsed applications of the drug Paclitaxel for models with and without quiescent cells. We use sensitivity equation methods to analyze the sensitivity of the model with respect to the initial number of immune cytotoxic T-cells. Numerical experiments are conducted to compare the model predictions to observed behavior. / Master of Science

L’objectif de ce travail de thèse est de démontrer pour la première fois la capture, la manipulationet la caractérisation de molécules biologiques grâce à une micropince réaliséeen technologie microsystème. La molécule d’ADN étant, dans un premier temps, la moléculecible, des fibres d’ADN sont capturées grâce à l’immersion de la micropince dansun petit volume inférieur à 1 μL de solution contenant les molécules. Elles sont ensuitecaractérisées mécaniquement et électriquement grâce aux fonctionnalités intégrées sur lamême puce en silicium.Le second volet de ce travail consiste à améliorer les performances du système pouratteindre la résolution d’une seule molécule. En effet dans le but d’étudier les phénomènesd’interactions au niveau moléculaire, il s’avère essentiel d’améliorer le système. Dans cebut précis, une commande par retour d’état de la micropince est étudiée. Elle permetalors de spécifiquement sensibiliser le système aux variations de raideur mécanique dusystème {micropince + molécules d’ADN}.[...] / The main objective of this Ph.D. work is to achieve biological experimentson DNA molecules with versatile silicon nanotweezers. Experiments on single moleculerely mostly on Optical Tweezers, Magnetic Tweezers or Atomic Force Spectroscopy, buthave a low throughput since preparations are done one at a time. To move towardssystematic biological or medical analysis, micro- and nano-systems (MNEMS) are theappropriate tools as they can integrate accurate molecular level engineering tools andcan be cheaply produced with highly parallel process.Design and fabrication of the silicon tweezers are made by ourselves in the lab of Pr.Hiroyuki Fujita (U. of Tokyo, Japan). DNA molecules are firstly trapped in solution bydielectrophoresis. Then biological reactions are characterized in real-time by monitoringthe mechanical resonance of the system {tweezers + DNA bundle}. The resolution of themeasurements allowed the sensing of about 30 of λ-DNA molecule stiffness (i.e. about20 mN/m). To achieve the single molecule resolution, we propose to implement a feedbackstrategy to alter the system.State feedback was developed to emulate a new system more sensitive to mechanicalstiffness parameter detection. As it remains problematic to design and fabricate newmicro mechanical device with extremely low stiffness (< 1 N/m), we propose to emulate acompliant system. By simulations it was demonstrated an enhancement of the sensitivityof about 10 when the resonant frequency of the closed-loop system is designed to be 10times lower than the tweezers resonant frequency (i.e. reducing the stiffness parameterof the system). Experimentally we demonstrated an improvement of the the sensitivityof superior to 2. However the issue is here to obtain stability, robustness with respectto disturbances and unmodeled dynamics. Before to attain the sensitivity of the singlemolecule, problematics about the model of the device or about the several dynamics ofthe device needs to be dealt in order to control and fit the improvement with the theory.

Objective condition assessment is essential to make better decisions for safety and serviceability of existing civil infrastructure systems. This study explores the condition of an existing transit guideway system that has been in service for thirty-five years. The structural system is composed of six-span continuous prestressed concrete bridge segments. The overall transit system incorporates a number of continuous bridges which share common design details, geometries, and loading conditions. The original analysis is based on certain simplifying assumptions such as rigid behavior over supports and simplified tendon/concrete/steel plate interaction. The current objective is to conduct a representative study for a more accurate understanding of the structural system and its behavior. The scope of the study is to generate finite element models (FEMs) to be used in static and dynamic parameter sensitivity studies, as well load rating and reliability analysis of the structure. The FEMs are used for eigenvalue analysis and simulations. Parameter sensitivity studies consider the effect of changing critical parameters, including material properties, prestress loss, and boundary and continuity conditions, on the static and dynamic structural response. Load ratings are developed using an American Association for State Highway Transportation Officials Load and Resistance Factor Rating (AASHTO LRFR) approach. The reliability of the structural system is evaluated based on the data obtained from various finite element models. Recommendations for experimental validation of the FEM are presented. This study is expected to provide information to make better decisions for operations, maintenance and safety requirements; to be a benchmark for future studies, to establish a procedure and methodology for structural condition assessment, and to contribute to the general research body of knowledge in condition assessment and structural health monitoring.

Climate analysts use Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations to make sense of models performance in predicting extreme events such as heavy precipitation. Similarly, weather analysts use numerical weather prediction models (NWP) to simulate weather conditions either by perturbing initial conditions or by changing multiple input parameterization schemes, e.g., cumulus and microphysics schemes. These simulations are used in operational weather forecasting and for studying the role of parameterization schemes in synoptic weather events like storms. This work addresses the need for visualizing the differences in both CMIP5 and NWP model output. This work proposes three glyph designs used for communicating CMIP5 model error. It also describes Ensemble Visual eXplorer tool that provides multiple ways of visualizing NWP model output and the related input parameter space. The proposed interactive dendrogram provides an effective way to relate multiple input parameterization schemes with spatial characteristics of model uncertainty features. The glyphs that were designed to communicate CMIP5 model error are extended to encode both parameterization schemes and graduated uncertainty, to provide related insights at specific locations such as storm center and the areas surrounding it. The work analyzes different ways of using glyphs to represent parametric uncertainty using visual variables such as color and size, in conjunction with Gestalt visual properties. It demonstrates the use of visual analytics in resolving some of the issues such as visual scalability. As part of this dissertation, we evaluated three glyph designs using average precipitation rate predicted by CMIP5 simulations, and Ensemble Visual eXplorer tool using WRF 1999 March 4th, North American storm track dataset.

This degree project undertakes a detailed examination of various algorithms used in Very Short-Term Load Forecasting (VSTLF) within network control systems, prioritizing forecasting accuracy and computational efficiency as critical evaluation criteria. The research comprehensively assesses a range of forecasting methods, including statistical models, machine learning algorithms, and advanced deep learning techniques, aiming to highlight their respective advantages, limitations, and suitability for different operational contexts. The study conducts a detailed analysis by comparing essential performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and execution time, before and after implementing adjustments to the formulations. This approach highlights how optimization strategies enhance the effectiveness of the models. Notably, the study identifies Support Vector Machine (SVM) and Multiple Linear Regression as frontrunners in terms of balancing accuracy with computational demand, making them particularly suitable for real-time forecasting needs. Meanwhile, Long Short-Term Memory (LSTM) networks demonstrate a commendable ability to capture complex, non-linear data patterns, albeit at a higher computational cost. The degree project further explores the sensitivity of these forecasting models to parameter adjustments, revealing a nuanced landscape where strategic modifications can significantly enhance model performance. This degree project not only contributes to the ongoing discourse on optimizing VSTLF algorithms but also provides actionable insights for stakeholders in the energy sector, aiming to facilitate the development of more reliable, efficient, and sustainable power system operations.

Les épilepsies acquises se déclarent après un processus graduel appelé épileptogenèse. Bien que cliniquement silencieux, ce processus implique des modifications fonctionnelles observables notamment par électroencéphalographie. Cette thèse vise i) à identifier des marqueurs électrophysiologiques apparaissant au cours de l’épileptogenèse, et ii) à comprendre les modifications physiopathologiques sous-jacentes responsables de ces marqueurs et de leur évolution temporelle. Dans un premier temps, nous avons, dans un modèle d’épilepsie partielle chez la souris, monitoré des signaux électrophysiologiques intracérébraux pendant la mise en place de la maladie. Nous avons observé dans ces signaux expérimentaux, l’émergence d’événements transitoires pathologiques appelés pointes épileptiques. Nous avons développé des méthodes de traitement du signal pour détecter et caractériser automatiquement ces événements. Ainsi, nous avons pu mettre en évidence certains changements dans la forme des pointes épileptiques au cours de l’épileptogenèse ; en particulier l’apparition et l’augmentation d’une onde qui suit la pointe épileptique. Une hypothèse défendue dans ces travaux est que ces changements morphologiques peuvent constituer des marqueurs de l’épileptogenèse dans ce modèle animal. Dans un second temps, afin d’interpréter ces modifications électrophysiologiques en termes de processus neurophysiologiques sous-jacents, nous avons implémenté un modèle biomathématique, physiologiquement argumenté, capable de simuler des pointes épileptiques. Formellement, ce modèle est un système dynamique non linéaire qui reproduit les interactions synaptiques (excitatrices et inhibitrices) dans une population de neurones. Une analyse de sensibilité de ce modèle a permis de mettre en évidence le rôle critique de certains paramètres de connectivité dans la morphologie des pointes. Nos résultats montrent en effet, qu’une diminution de l’inhibition GABAergique entraîne un accroissement de l’onde dans les pointes épileptiques. À partir du modèle théorique, nous avons pu ainsi émettre des hypothèses sur les modifications opérant au cours du processus d’épileptogenèse. Ces hypothèses ont pu être en partie vérifiées expérimentalement en bloquant artificiellement l’inhibition GABAergique, dans le modèle in vivo chez la souris, et dans un modèle in vitro chez le rat. En conclusion, ce travail de thèse fournit, dans un modèle animal, un biomarqueur électrophysiologique de l’épileptogenèse et tente d’expliquer, grâce à une modélisation biomathématique, les processus neurophysiologiques sous-jacents qu’il reflète. / Acquired epilepsies occur after a process called epileptogenesis. Although clinically silent, this process involves some functional modifications which can be observed by electroencephalography. The objectives of this thesis are i) to identify electrophysiological markers occurring during epileptogenesis, and ii) to understand which underlying pathophysiological modifications are responsible for these markers and their evolution. Firstly, using an in vivo experimental mouse model of partial epilepsy, we have monitored intracranial electrophysiological signals during epileptogenesis. We observed the emergence of pathological transient events called epileptic spikes. We have developed signal processing methods in order to automatically detect and characterize these events. Hence, we observed and quantified morphological changes of epileptic spikes during epileptogenesis. In particular, we noticed the emergence and the increase of a wave which directly follows the spike component. In this work, we defend the hypothesis that these morphological modifications can constitute markers of the epileptogenesis process in this animal model of epilepsy. Secondly, in order to interpret these electrophysiological modifications in terms of underlying pathophysiological processes, we have implemented a computational model able to simulate epileptic spikes. This neural mass model is a neurophysiologically-plausible mesoscopic representation of synaptic interactions (excitation and inhibition) in the hippocampus. Based on a sensitivity analysis of model parameters, we were able to determine some connectivity parameters that play a key role in the morphology of simulated epileptic spikes. In particular, our results show that a diminution of GABAergic inhibition leads to an increase of the aforementioned wave. Thus, using this theoretical model, we defined some hypotheses about pathophysiological modifications occurring during the epileptogenesis process. One of these hypotheses has been confirmed in blocking GABAa receptors in the in vivo mouse model, as well as in an in vitro model (rat, organotypic slices). In summary, based on the shape features of epileptic spikes, we devised an electrophysiological biomarker of epileptogenesis observed in a mouse model but useful in Human studies as well. Moreover, a computational modeling approach has permitted to suggest which pathophysiological processes might underlie this biomarker.