To be able to make informed descicions regarding the research of new drug molecules (ligands), it is crucial to have access to information regarding the chemical interaction between the drug and its biological target (protein). Computer-based methods have a given role in drug research today and, by using methods such as molecular docking, it is possible to investigate the way in which ligands and proteins interact. Despite the acceleration in computer power experienced in the last decades many problems persist in modelling these complicated interactions. The main objective of this thesis was to investigate and improve molecular modelling methods aimed to estimate protein-ligand binding. In order to do so, we have utilised chemometric tools, e.g. design of experiments (DoE) and principal component analysis (PCA), in the field of molecular modelling. More specifically, molecular docking was investigated as a tool for reproduction of ligand poses in protein 3D structures and for virtual screening. Adjustable parameters in two docking software were varied using DoE and parameter settings were identified which lead to improved results. In an additional study, we explored the nature of ligand-binding cavities in proteins since they are important factors in protein-ligand interactions, especially in the prediction of the function of newly found proteins. We developed a strategy, comprising a new set of descriptors and PCA, to map proteins based on their cavity physicochemical properties. Finally, we applied our developed strategies to design a set of glycopeptides which were used to study autoimmune arthritis. A combination of docking and statistical molecular design, synthesis and biological evaluation led to new binders for two different class II MHC proteins and recognition by a panel of T-cell hybridomas. New and interesting SAR conclusions could be drawn and the results will serve as a basis for selection of peptides to include in in vivo studies.

Mettre en place des stratégies de lutte contre les ravageurs, indépendantes des produits chimiques est un objectif fondamental pour une protection durable des cultures contre les ravageurs et la conservation d’un environnement sain pour les populations humaines en zone rurale. L’objectif principal de ce travail était de contribuer à la mise en place d’une lutte biologique par gestion des habitats à l’échelle du paysage de la noctuelle polyphage Helicoverpa armigera, principal ravageur de cotonnier dans le nord Bénin. Cette thèse visait à analyser l’influence des pratiques agricoles et de l’organisation du paysage sur l’abondance et l’origine trophique de H. armigera. L’analyse biochimique d’individus élevés en laboratoire, nous a permis de confirmer le fait que le gossypol est un bon marqueur pour identifier les adultes qui ont passé leur vie larvaire sur le cotonnier. Au contraire de la tomatine qui ne peut être considérée comme un marqueur de la tomate car la tomatine a été détectée seulement chez les larves d’H. armigera et non chez les adultes. Notre étude sur le terrain au nord Bénin dans 40 parcelles, a montré que les pratiques agricoles avaient un fort effet sur l’infestation larvaire. La date de semis et la fréquence de sarclage étaient négativement corrélées à l’infestation larvaire. La proportion de cotonniers dans le paysage et celle de tomate ont influencé positivement l’infestation en larves d’H. armigera. Nous avons également montré qu’un précédent cultural tomate présentait une abondance larvaire en moyenne trois fois supérieur à un précédent cultural maïs. Ensuite, dans des rayons de 100 m, 250 m et 500 m, nous avons étudié les effets de la composition et de l’hétérogénéité du paysage d’une part sur l’abondance des adultes d’H. armigera et d’autre part sur leur origine trophique. L’hétérogénéité du paysage en plantes hôtes est le facteur paysager principal qui a influencé positivement l’abondance des adultes. Les isotopes stables de Carbone nous ont permis d’identifier les individus dont la larve s’était nourrie sur des plantes de type photosynthétique C3 (cotonniers, tomates, ...) ou C4 (maïs, sorgho, …). L’origine trophique, plantes hôtes C3 ou C4, est reliée positivement à la proportion de plantes hôtes respectivement C3 ou C4 dans un rayon de 500m. Seulement 10% des individus ayant consommés des plantes en C3 ont été détecté positif au gossypol. La proportion de cotonniers dans le paysage ne semble pas expliquer la proportion d’individus détectés positif au gossypol. Nous formulons des propositions de gestion de l’assolement et des rotations culturales pour contribuer à la régulation d’H. armigera. Ainsi, il faudrait éviter que le cotonnier soit semé sur un précédent cultural tomate. Il serait important de décaler les dates de semis entre les parcelles de cotonniers voisines et de respecter la fréquence de sarclage minimale qui est de trois. Par ailleurs, il serait judicieux de préférer un environnement paysager homogène autour d’une parcelle de cotonnier, en privilégiant par exemple, le maïs. / The development of strategies independent of pesticides is a fundamental objective for sustainable crop protection against pests as well as for maintaining of a healthy environment for human populations. The rationale of the research presented here was to improve our ability to control the cotton bollworm, Helicoverpa armigera by non-pesticide methods via habitat conservation. We analyzed the influence of agricultural practices and landscape composition and diversity on the abundance and trophic origin of H. armigera and assessed gossypol and tomatine in individual H. armigera as cotton and tomato biomarkers respectively. Gossypol was shown to be a stable cotton biomarker, even in adult H. armigera 12 days after emergence. In contrast, tomatine was only detected in larvae of H. armigera and not adults; thereby tomatine can not be considered as a marker of tomato plants. Subsequently, in north Benin, the abundance of H. armigera larvae and adults was monitored in cotton fields. We found a strong effect of agricultural practices on H. armigera larvae abundance. Delay sowing date and increase frequency of weeding reduced the abundance of H. armigera in cotton fields; whereas the proportion of cotton and tomato in the landscape increased. This study also highlights the role of the previous landcover in the infestation of a cotton field: A previous tomato landcover increased infestation three times more than a previous maize landcover. At nested scales ranging from 100 m, 250 m to 500 m, we studied the effects of landscape composition and diversity firstly on the abundance of adult H. armigera and secondly on their trophic origin. We found that, landscape diversity was the main factor that influenced both the abundance adult and their trophic origin at 500 m scale. Analyses of stables isotopes of Carbone showed that proportion of hosts plants with C3 photosynthetic pathway in the landscape was positively related to H. armigera moths with C3 trophic origin signal at 500 m scale. Only 10% of moths were positive to gossypol signal. The proportion of cotton in the landscape seems not important to explain the trophic origin of individual which were positive to gossypol signal. Therefore, for integrated management of H. armigera our results suggest it is necessary to consider the following agricultural practices and crop diversity regimes (in regard to the resource use strategies of this polyphagous pest). A tomato previous landcover should be avoid; shift sowing date between cotton fields, and have at less three manual weedings. In additional, we suggest employing maize around cotton fields rather than other crops.

[EN] Statistical Process Control (SPC) and the Automatic Process Control (APC) are two control philosophies have evolved independently until recently. The overall objective of both APC and SPC is to optimize the performance of processes, reducing the variability of the resulting characteristics around the desired values. The fundamentals of the two disciplines arise from the idea that the whole process has operation variations. These variations may affect, to a greater or lesser extent, the final product quality and the process productivity. The two methodologies conceptualize processes and control in different ways, they originated in different industrial sectors and have evolved independently, until the interest to integrate them in the control of industrial processes was deduced. It was warned that they could be complementary rather than conflicting methodologies as they were understood until then. The possibility of combining the advantages of both, integrating them into a new control paradigm was explored. First, the problem of identifying and estimating a process model is considered. Controlled variables in this model are the main feature of product quality and a productivity variable. The latter is innovative since the productivity variables are measured, but they are not considered as controlled variables. For this, two methods of multivariate time series are used, the Box-Jenkins multiple transfer function in the parsimonious way and the impulse response function obtained by Partial Least Squares regression (Time Series-Partial Least Squares, TS-PLS). These two methods were compared taking into account different aspects such as the simplicity of the modeling process in the stages of identification, estimation and model validation, as well as the utility of graphical tools that provide both methodologies, the goodness of fit obtained, and the simplicity of the mathematical structure of the model. The DMC (Dynamic Matrix Control) controller, an automatic control algorithm belonging to the family of MPC (Model Predictive Control) controllers, is derived from the estimated Box-Jenkins multiple transfer function that has been selected as the most suitable for this kind of processes. An optimal tuning method to maximize the controller performance, applying experimental design 2k-p, is presented. Finally, an integrated control system MESPC (Multivariate Engineering Statistical Process Control) whose monitoring component has been implemented applying latent structures based multivariate statistical process control methods (Lsb-MSPC), has been developed. The monitoring module is designed to act as both process and DMC controller supervisor. To do this, we estimate a NOC-PCA model (Normal Operation Conditions Principal Component Analysis), which has as variables both process-related and quality-related variables, all derived from the automatic control system. From this model, and DModX graphics have been derived. We assessed the performance of MESPC system, subjecting it to simulated potential failures or special causes of variability. / [ES] El Control Estadístico de Procesos (Statistical Process Control, SPC) y el Control Automático de Procesos (Automatic Process Control, APC) son dos filosofías de control se han desarrollado hasta recientemente de forma independiente. El objetivo general tanto del SPC como del APC, es optimizar el funcionamiento de los procesos, reduciendo la variabilidad de las características resultantes en torno a los valores deseados. El fundamento de ambas disciplinas, parte de la idea de que todo proceso presenta variaciones en su funcionamiento. Estas variaciones pueden afectar en mayor o en menor medida a la calidad final del producto y a la productividad del proceso. Las dos metodologías conceptualizan los procesos y su control de diferentes formas, se originaron en diferentes sectores industriales y han evolucionado de forma independiente, hasta que se dedujo el interés de integrarlas en el control de los procesos industriales, ya que se advirtió que podían ser complementarias, antes que contrapuestas, como se entendían hasta entonces y se exploró la posibilidad de aunar las ventajas de ambas, integrándolas en un nuevo paradigma de control. Esta tesis se centra en el estudio de la integración de procedimientos multivariantes para la regulación óptima y la monitorización estadística de procesos, con el propósito de contribuir a la mejora de la calidad y de la productividad de los procesos. La metodología propuesta se ha aplicado con fines ilustrativos a un proceso MIMO de producción en continuo de Polietileno de Alta Densidad (PEAD).En primer lugar, se considera el problema de la identificación y posterior estimación de un modelo del proceso. Las variables controladas en este modelo han sido la principal característica de calidad del producto y una variable de productividad, esto último es innovador puesto que las variables de productividad se miden, pero no se consideran variables controladas. Para ello, se emplean dos metodologías de series temporales multivariantes, la obtención de la función de transferencia múltiple en forma parsimoniosa de Box-Jenkins y la obtención de la función de respuesta a impulsos mediante los modelos de regresión por mínimos cuadrados parciales (Time Series-Partial Least Squares, TS-PLS). Estas dos metodologías se han comparado teniendo en cuenta distintos aspectos como son la simplicidad del proceso de modelado en las etapas de identificación, estimación y validación del modelo, así como la utilidad de las herramientas gráficas que proporcionan ambas metodologías, la bondad de ajuste obtenida, y la simplicidad de la estructura matemática del modelo. A partir del modelo de función de transferencia múltiple estimado, elegido como el más adecuado para este tipo de procesos, se desarrolla el controlador DMC (Dynamic Matrix Control), un algoritmo de control automático que pertenece a la familia del Control Predictivo basado en Modelos (Model Predictive Control, MPC). Se presenta un método de sintonizado óptimo del controlador que permita maximizar su rendimiento, aplicando diseño de experimentos 2k-p.Finalmente, se ha desarrollado un sistema de control integrado MESPC (Multivariate Engineering Statistical Process Control), cuya componente de monitorización se ha implementado aplicando métodos de control estadístico multivariante de procesos basados en técnicas de proyección en estructuras latentes. Este módulo de monitorización se ha diseñado para que actúe como supervisor tanto del proceso como del controlador DMC. Para ello, se ha estimado un modelo NOC-PCA (Normal Operation Conditions Principal Component Analysis), en el que han intervenido tanto variables relacionadas con el proceso como con la calidad, todas derivadas de la componente del control automático. A partir de este modelo se han derivado los gráficos y DModX. Se ha evaluado el funcionamiento del sistema MESPC, sometiéndolo a fallos potenciales o causas especiales de variabiliabilidad. / [CAT] El Control Estadístic de Processos (Statistical Process Control, SPC) i del Control Automàtic de Processos (Automatic Process Control, APC) son dues filosofies de control s'han desenvolupat fins a recentment de forma independent. L'objectiu general tant del SPC com del APC, és optimitzar el funcionament dels processos, reduint la variabilitat de les característiques resultants entorn dels valors desitjats. El fonament d'ambdues disciplines, part de la idea que tot procés presenta variacions en el seu funcionament. Aquestes variacions poden afectar en major o en menor mesura a la qualitat final del producte i a la productivitat del procés. Les dues metodologies conceptualitzen els processos i el seu control de diferents formes, es van originar en diferents sectors industrials i han evolucionat de forma independent, fins que es va deduir l'interès d'integrar-les en el control dels processos industrials, ja que es va advertir que podien ser complementàries, abans que contraposades, com s'entenien fins llavors i es va explorar la possibilitat de conjuminar els avantatges d'ambdues, integrant-les en un nou paradigma de control. Aquesta tesi se centra en l'estudi de la integració de procediments multivariants per a la regulació òptima i el monitoratge estadístic de processos amb el propòsit de contribuir a la millora de la qualitat i de la productivitat dels processos. La metodologia proposada s'ha aplicat amb finalitats il·lustratives a un procés MIMO de producció en continu de Polietilè d'Alta Densitat (PEAD). En primer lloc, es considera el problema de la identificació i posterior estimació d'un model del procés. Les variables controlades en aquest model han sigut la principal característica de qualitat del producte i una variable de productivitat, açò últim és innovador ja que les variables de productivitat es mesuren, però no es consideren variables controlades. Per a açò, s'utilitzen dues metodologies de sèries temporals multivariants, l'obtenció de la funció de transferència múltiple en forma parsimòniosa de Box-Jenkins i l'obtenció de la funció de resposta a impulsos mitjançant els models de regressió per mínims quadrats parcials (Times Series-Partial Least Squares, TS-PLS). Aquestes dues metodologies s'han comparat tenint en compte diferents aspectes com són la simplicitat del procés de modelatge en les etapes d'identificació, estimació i validació del model, així com la utilitat de les eines gràfiques que proporcionen ambdues metodologies, la bondat d'ajust obtinguda, i la simplicitat de l'estructura matemàtica del model. A partir del model de funció de transferència múltiple estimat, triat com el més adequat per a aquest tipus de processos, es desenvolupa el controlador DMC (Dynamic Matrix Control), un algorisme de control automàtic que pertany a la família del Control Predictiu basat en Models (Model Predictive Control, MPC). Es presenta un mètode de sintonitzat òptim del controlador que permeta maximitzar el seu rendiment, aplicant disseny d'experiments 2k-p. Finalment, s'ha desenvolupat un sistema de control integrat MESPC (Multivariate Engineering Statistical Process Control). Per a implementar la component de monitoratge d'aquest sistema integrat s'han usat mètodes de control estadístic multivariants de processos basats en tècniques de projecció en estructures latents (Latent structures based-Multivariate Statistical Process Control). Aquest mòdul de monitoratge s'ha dissenyat perquè actue com a supervisor tant del procés com del controlador DMC. Per a açò, s'ha estimat un model NOC-PCA (Normal Operation Conditions Principal Component Analysis), en el qual han intervingut variables relacionades tant amb el procés, com amb la qualitat, totes derivades de la component del control automàtic. A partir d'aquest model s'han derivat els gràfics i DModX. S'ha avaluat el funcionament del sistema MESPC, sotmetent-lo a fallades potencials o causes especials de / Barceló Cerdá, S. (2016). Estudio de la integración de procedimientos multivariantes para la regulación óptima y monitorización estadística de procesos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/63442 / TESIS

L’objectif de cette thèse était d’appliquer des méthodes d’analyse multivariées au traitement des données provenant de la spectroscopie de plasma induit par laser (LIBS) et de la spectroscopie térahertz (THz) dans le but d’accroître les performances analytiques de ces techniques.Les spectres LIBS provenaient de campagnes de mesures directes sur différents sites géologiques. Une approche univariée n’a pas été envisageable à cause d’importants effets de matrices et c’est pour cela qu’on a analysé les données provenant des spectres LIBS par réseaux de neurones artificiels (ANN). Cela a permis de quantifier plusieurs éléments mineurs et majeurs dans les échantillons de sol avec un écart relatif de prédiction inférieur à 20% par rapport aux valeurs de référence, jugé acceptable pour des analyses sur site. Dans certains cas, il a cependant été nécessaire de prendre en compte plusieurs modèles ANN, d’une part pour classer les échantillons de sol en fonction d’un seuil de concentration et de la nature de leur matrice, et d’autre part pour prédire la concentration d’un analyte. Cette approche globale a été démontrée avec succès dans le cas particulier de l’analyse du plomb pour un échantillon de sol inconnu. Enfin, le développement d’un outil de traitement par ANN a fait l’objet d’un transfert industriel.Dans un second temps, nous avons traité des spectres d’absorbance terahertz. Ce spectres provenaient de mesures d’absorbance sur des mélanges ternaires de Fructose-Lactose-acide citrique liés par du polyéthylène et préparés sous forme de pastilles. Une analyse semi-quantitative a été réalisée avec succès par analyse en composantes principales (ACP). Puis les méthodes quantitatives de régression par moindres carrés partiels (PLS) et de réseaux de neurons artificiels (ANN) ont permis de prédire les concentrations de chaque constituant de l’échantillon avec une valeur d’erreur quadratique moyenne inférieure à 0.95 %. Pour chaque méthode de traitement, le choix des données d’entrée et la validation de la méthode ont été discutés en détail. / The aim of this work was the application of multivariate methods to analyze spectral data from laser-induced breakdown spectroscopy (LIBS) and terahertz (THz) spectroscopy to improve the analytical ability of these techniques.In this work, the LIBS data were derived from on-site measurements of soil samples. The common univariate approach was not efficient enough for accurate quantitative analysis and consequently artificial neural networks (ANN) were applied. This allowed quantifying several major and minor elements into soil samples with relative error of prediction lower than 20% compared to reference values. In specific cases, a single ANN model didn’t allow to successfully achieving the quantitative analysis and it was necessary to exploit a series of ANN models, either for classification purpose against a concentration threshold or a matrix type, or for quantification. This complete approach based on a series of ANN models was efficiently applied to the quantitative analysis of unknown soil samples. Based on this work, a module of data treatment by ANN was included into the software Analibs of the IVEA company. The second part of this work was focused on the data treatment of absorbance spectra in the terahertz range. The samples were pressed pellets of mixtures of three products, namely fructose, lactose and citric acid with polyethylene as binder. A very efficient semi-quantitative analysis was conducted by using principal component analysis (PCA). Then, quantitative analyses based on partial least squares regression (PLS) and ANN allowed quantifying the concentrations of each product with a root mean square error (RMSE) lower than 0.95 %. All along this work on data processing, both the selection of input data and the evaluation of each model have been studied in details.

Text in English / Developments in Internet technology and pervasive computing over the past two and half decades have resulted in a variety of Web-based applications (WBAs) that provide products and services to online users or customers. The Internet is used not only to transfer information via the web but is increasingly used to provide electronic services including business transactions, information-delivery and social networking, as well as e-government, e-health and e-learning. For such organisations, e-service quality, usability and user experience are considered to be critical determinants of their products’ or services’ success. Many studies to model these three concepts separately have been undertaken as part of broader studies of software quality or service quality modelling. However, to the current researcher’s knowledge, none of the studies have focussed on proposing an evaluation model that integrates and combines the three of them. This research is an effort to fill that gap. The primary purpose of this mixed-methods research was to develop a conceptual integrated model for evaluating e-service quality, usability and user experience (e-SQUUX) of WBAs and then contextualise it to evaluation of a University web portal (UWP). This was undertaken using an exploratory sequential research design. During a qualitative phase, an extensive extensive systematic literature review of 264 relevant sources relating to dimensions of e-service quality, usability and user experience, was undertaken to derive an integrated conceptual e-service quality, usability and user experience (e-SQUUX) Model for evaluating WBAs. The model was then empirically refined through a sequential series of validations, thus developing various versions of the e-SQUUX Model. First, it was content validated by a set of four expert reviewers. Second, during the quantitative phase, in the context of a University web portal, a questionnaire survey was conducted that included a comprehensive pilot study with 29 partipants, prior to the main survey. The main survey data from 174 particiapants was used to determine a validated model, using Exploratory factor analysis (EFA), followed by producing a structural model, using partial least square – structural equation modelling (PLS-SEM). This version consisted of the components of the final e-SQUUX Model. Consequently, the research enriches the body of knowledge on IS and HCI by providing the e-SQUUX Model as an evaluation tool. For designers, developers and managers of UWPs, the model serves as a customisable set of evaluation criteria and also provides specific recommendations for design. In line with the Exploratory sequential design of mixed methods research, the findings of the qualitative work in this research influenced the subsequent quantitative study, since the potential Likert-scale questionnaire items were derived from the definitions and meanings of the components that emanated from the qualitative phase of the study. Consequently, this research is an exemplar for developing an integrated evaluation model for specific facets or domains, and of its application in a particular context, in this case, a University web portal. Keywords: e-service quality, usability, user experience, evaluation model, integrated model, exploratory factor analysis, partial least square – structural equation modelling (PLS-SEM), mixed methods research, Exploratory sequential design, quantitative study, qualitative study, validation, Web-based applications, University web portal / Information System / Ph D. (Information Systems)

Human activities have increased the concentration of CO2 into the atmosphere, thus it has caused global warming. Construction equipment are semi-stationary machines and spend at least 30% of its life time during idling. The majority of the construction equipment is diesel powered and emits toxic emission into the environment. In this work, the idling will be investigated through adopting several statistical regressions models to quantify the fuel consumption of construction equipment during idling. The regression models which are studied in this work: Multivariate Linear Regression (ML-R), Support Vector Machine Regression (SVM-R), Gaussian Process regression (GP-R), Artificial Neural Network (ANN), Partial Least Square Regression (PLS-R) and Principal Components Regression (PC-R). Findings show that pre-processing has a significant impact on the goodness of the prediction of the explanatory data analysis in this field. Moreover, through mean centering and application of the max-min scaling feature, the accuracy of models increased remarkably. ANN and GP-R had the highest accuracy (99%), PLS-R was the third accurate model (98% accuracy), ML-R was the fourth-best model (97% accuracy), SVM-R was the fifth-best (73% accuracy) and the lowest accuracy was recorded for PC-R (83% accuracy). The second part of this project estimated the CO2 emission based on the fuel used and by adopting the NONROAD2008 model.  Keywords:

Instrumental disparities and soil moisture are two of the key limitations in implementing spectroscopic techniques in the field. This study sought to address these challenges through two objectives. The first objective was to assess Visible-near infrared (VisNIR) and mid-infrared (MIR) spectroscopic approaches and explore the feasibility of transferring calibration models between laboratory and portable spectrometers. The second objective addressed the challenge of soil moisture and its impact on spectra. The portable spectrometers demonstrated comparable performance to their laboratory-based counterparts in both regions. Spiking with extra-weight, was the most effective calibration transfer method eliminating disparities between instruments. The samples were rewetted under nine controlled conditions for the moisture study. Results showed that spiking with extra weights significantly outperformed other techniques and model enhancement was insensitive to the moisture contents. Findings of this study will be helpful for development and deployment of in situ sensors to enable field implementation of spectroscopy.