[ES] La presente tesis doctoral está dedicada a estudiar, desarrollar y aplicar metodologías basadas en datos, fundamentadas en modelos estadísticos multivariantes de variables latentes, para abordar el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se pone especial énfasis en los modelos causales basados en variables latentes que utilizan tanto datos provenientes de un diseño de experimentos como, principalmente, datos provenientes del proceso de producción diario, es decir, datos históricos. La tesis está estructurada en cinco partes. La primera parte discute el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se destacan los objetivos de la tesis. Además, se presenta una descripción exhaustiva de los modelos basados en variables latentes, sobre los cuales se fundamentan las metodologías novedosas propuestas en esta tesis. En la segunda parte, se presentan las novedosas aportaciones metodológicas. En primer lugar, se muestra el potencial de PLS para analizar datos del DOE, con o sin datos faltantes. Posteriormente, el potencial de los modelos causales basados en variables latentes se centra en definir el espacio de diseño de la materia prima que proporciona garantía de calidad con un cierto nivel de confianza para los atributos críticos de calidad, junto con el desarrollo de un nuevo índice de capacidad multivariante basado en el espacio latente para clasificar y seleccionar proveedores para una materia prima particular utilizada en un proceso de fabricación. La tercera parte pretende abordar aplicaciones novedosas mediante modelos causales basados en variables latentes utilizando datos históricos. En primer lugar, se trata de su aplicación en el ámbito sanitario: la Pandemia COVID-19. En este contexto, se utiliza el uso de modelos basados en variables latentes para desarrollar una alternativa a los ensayos clínicos controlados con placebo. Luego, se utilizan modelos basados en variables latentes para optimizar procesos en el marco de aplicaciones industriales. La cuarta parte presenta una interfaz gráfica de usuario desarrollada en código Python que integra los métodos desarrollados con el objetivo de ser autoexplicativa y fácil de usar. Finalmente, la última parte discute la relevancia de esta disertación, incluyendo propuestas que merecen mayor investigación. / [CA] Aquesta tesi doctoral està dedicada a estudiar, desenvolupar i aplicar metodologies basades en dades, fonamentades en models estadístics multivariants de variables latents, per abordar el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es posa un èmfasi especial en els models causals basats en variables latents que utilitzen tant; dades provinents d'un disseny d'experiments com, principalment, dades provinents del procés de producció diari, és a dir, dades històriques. La tesi està estructurada en cinc parts. A la primera part es discuteix el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es destaquen els objectius de la tesi. A més, es presenta una descripció exhaustiva dels models basats en variables latents, sobre els quals es fonamenten les noves metodologies proposades en aquesta tesi. A la segona part, es presenten les noves aportacions metodològiques. En primer lloc, es mostra el potencial de PLS per analitzar dades del DOE, amb dades faltants o sense aquestes. Posteriorment, el potencial dels models causals basats en variables latents se centra a definir l'espai de disseny de la matèria prima que proporciona garantia de qualitat amb un cert nivell de confiança per als atributs crítics de qualitat, juntament amb el desenvolupament d'un nou índex de capacitat multivariant basat en l'espai latent per a classificar i seleccionar proveïdors per a una primera matèria particular utilitzada en un procés de fabricació. La tercera part pretén abordar aplicacions noves mitjançant models causals basats en variables latents utilitzant dades històrques. En primer lloc, es tracta de la seva aplicació a l'àmbit sanitari: la Pandèmia COVID-19. En aquest context, es fa servir l'ús de models basats en variables latents per desenvolupar una alternativa als assaigs clínics controlats amb placebo. Després s'utilitzen models basats en variables latents per optimitzar processos en el marc d'aplicacions industrials. La quarta part presenta una interfície gràfica d'usuari desenvolupada en codi Python que integra els mètodes desenvolupats amb l'objectiu de ser autoexplicativa i fàcil d'usar. Finalment, l'última part discuteix la rellevància d'aquesta dissertació, incloent-hi propostes que mereixen més investigació. / [EN] The present Ph.D. thesis is devoted to studying, developing, and applying data-driven methodologies, based on multivariate statistical models of latent variables, to address the scientific learning paradigm in the Industry 4.0 environment. Particular emphasis is placed on causal latent variable-based models using both data coming from a planned design of experiments and, mainly, data coming from the daily production process, namely happenstance data. The dissertation is structured in five parts. The first part discusses the scientific learning paradigm in the Industry 4.0 environment. The objectives of the thesis are highlighted. In addition to that, a comprehensive description of latent variable-based models is presented, on which the novel methodologies proposed in this thesis are founded. In the second part, the novel methodological contributions are presented. Firstly, the potential of PLS to analyze data from DOE, with or without missing runs is illustrated. Then, the potential of causal latent variable-based models is concentrated on defining the raw material design space providing assurance of quality with a certain confidence level for the critical to quality attributes, jointly with the development of a novel latent space-based multivariate capability index to rank and select suppliers for a particular raw material used in a manufacturing process. The third part aims to address novel applications by means of causal latent variable-based models using happenstance data. First, it concerns a health application: the Pandemic COVID-19. In this context, the use of latent variable-based models is applied to develop an alternative to placebo-controlled clinical trials. Then, latent variable-based models are used to optimize processes within the framework of industrial applications. The fourth part introduces a graphical user interface developed in Python code that integrates the developed methods with the aim of being self-explanatory and user-friendly. Finally, the last part discusses the relevance of this dissertation, including proposals that deserve further research. / Borràs Ferrís, J. (2023). Causal latent space-based models for scientific learning in Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/198993

Thesis submitted in fulfilment of the requirements for the degree Master of Technology Chemistry in the Faculty of (Science) Supervisor: Professor T.N. van der Walt Bellville campus Date submitted: October 2011 / It is important to quantify polymeric components in a coating because they greatly influence the performance of a coating. The difficulty associated with analysis of polymers by Fourier transform infrared (FTIR) analysis’s is that colinearities arise from similar or overlapping spectral features. A quantitative FTIR method with attenuated total reflectance coupled to multivariate/ chemometric analysis is presented. It allows for simultaneous quantification of 3 polymeric components; a rheology modifier, organic opacifier and styrene acrylic binder, with no prior extraction or separation from the paint. The factor based methods partial least squares (PLS) and principle component regression (PCR) permit colinearities by decomposing the spectral data into smaller matrices with principle scores and loading vectors. For model building spectral information from calibrators and validation samples at different analysis regions were incorporated. PCR and PLS were used to inspect the variation within the sample set. The PLS algorithms were found to predict the polymeric components the best. The concentrations of the polymeric components in a coating were predicted with the calibration model. Three PLS models each with different analysis regions yielded a coefficient of correlation R2 close to 1 for each of the components. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) was less than 5%. The best out-put was obtained where spectral features of water was included (Trial 3). The prediction residual values for the three models ranged from 2 to -2 and 10 to -10. The method allows paint samples to be analysed in pure form and opens many opportunities for other coating components to be analysed in the same way.

En Analyse Sensorielle, les préférences des consommateurs sont généralement modélisées en fonction de données sensorielles par les méthodes de cartographie des préférences. L'objectif de cette thèse est de modéliser les préférences des consommateurs en intégrant, en plus des données sensorielles, de nouvelles variables relatives à leur perception des produits. Nous appellerons ces variables les dimensions subjectives. Elles recouvrent des dimensions pragmatiques liées à l'utilisation du produit et des dimensions plus symboliques telles que l'esthétisme, la modernité, l'originalité…Les problématiques relatives aux dimensions subjectives ont d'abord été étudiées lors d'une étude exploratoire sur des tasses à café. L'ensemble du travail a ensuite été réalisé sur 11 sièges de voitures. Dans un premier temps, des entretiens qualitatifs ont été réalisés auprès de 16 consommateurs d'une part et de 2 designers d'autre part. Ces entretiens ont permis d'identifier les dimensions subjectives caractéristiques des sièges. Une évaluation quantitative des dimensions subjectives et des préférences a ensuite été réalisée par 110 consommateurs. Enfin, les sièges ont été caractérisés sensoriellement par des experts. Les préférences des consommateurs ont été modélisées en fonction des données sensorielles et des dimensions subjectives par des modèles d'équations structurelles à variables latentes, plus précisément par Partial Least Square Path Modeling. Quatre modèles, fondés sur les groupes de préférences, ont été mis en place. Selon le groupe étudié, la contribution des deux jeux de données diffère et quatre profils de clients sont identifiés. D'un point de vue méthodologique, ce travail fournit des éléments de réponse sur l'intérêt des dimensions subjectives pour la modélisation des préférences. L'ensemble de la démarche est en cours d'application sur un produit alimentaire : le chocolat. / In Sensory Science, preference mapping is used to explain consumers' preferences with sensory data. This PhD aims to integrate not only sensory data but also new variables that are related to consumers' perception of the product in the modelling of consumers' preferences. These variables are labelled as subjective dimensions. They address the pragmatic dimensions that cover the context of use of the products and more symbolic dimensions, such as aesthetics, modernity, originality…An exploratory study based on coffee cups was a first mean to approach the issues related to subjective dimensions. Then, all the work was done on a study of 11 car seats. The first step consisted in qualitative interviews of 16 consumers and of 2 designers. These interviews allowed identifying the subjective dimensions that characterize car seats. 110 consumers then performed a quantitative evaluation of their preferences and subjective dimensions. Finally, the seats were characterized by experts with sensory descriptors. The consumers' preferences were modelled according to both sensory data and subjective dimensions, using structural equations: the Partial Least Square Path Modeling. Four models based on preferences clustering were established. The contribution of two kinds of data differed according to the considered cluster, which led to the identification of four customer profiles. From a methodological point of view, this work provides first elements about the benefit of subjective dimensions in preference modelling. The methodology is being implemented on a food product: chocolate.

This thesis challenges two key assumptions made in the current Islamic banking literature. Firstly, this thesis challenges and empirically invalidates the assumption that all Islamic banks are indistinguishable from their conventional counterparts and are thus equally unIslamic. To do so, this thesis uses the profit and loss sharing (PLS) criteria, which is central to the philosophy of Islamic banking and is the key principle differentiating Islamic from conventional banking, in theory and practice. By investigating variation in PLS levels between Islamic banks and comparing with conventional banks with and without Islamic windows, this thesis illustrates that the Islamic banking industry does not comprise a homogeneous group of banks that are all indistinguishable from their conventional counterparts. Rather, a typology of Islamic banks exists, comprising of three distinct groups of banks, each one following a different business model. While one group can genuinely be considered indistinguishable from conventional banks, another group shows clear evidence of pursuing PLS-oriented strategies in formulating its asset portfolio, differentiating itself from the purely debt-based intermediation model adopted by conventional banks. As such, empirical evidence shows that some Islamic banks are, in practice, operating closer to the PLS principle and can thus be considered more Islamic than others. Further investigation illustrates that the institutional environment matters for the provision of ideal PLS Islamic financing instruments. Secondly, this thesis overcomes two methodological issues to compare the corporate social performance (CSP) of Islamic and conventional banks. In doing so, this thesis challenges the second identified assumption from the literature, that religion-specific category of corporate social responsibility (CSR) is particular to Islamic banking, and invalidates it on conceptual, theoretical and empirical basis. A novel CSP Index based on the evidence-based disclosure criteria, comprising of 6 dimensions and 25 social performance indicators is constructed and complemented with three Social Performance Quantitative Indicators (SPQIs) to compare the CSP of Islamic and conventional banks. From this comparison, this thesis concludes that, contrary to the industry s claims and expectations held of it, Islamic banking does not offer an ethical alternative to conventional banking. Differences in the level and composition of CSP between the two industries are more subtle and require a nuanced approach to be studied.

Les plantes médicinales constituent une source inexhaustible de composés (des produits naturels - PN) utilisé en médecine pour la prévention et le traitement de diverses maladies. L'introduction de nouvelles technologies et méthodes dans le domaine de la chimie des produits naturels a permis le développement de méthodes ‘high throughput’ pour la détermination de la composition chimique des extraits de plantes, l'évaluation de leurs propriétés et l'exploration de leur potentiel en tant que candidats médicaments. Dernièrement, la métabolomique, une approche intégrée incorporant les avantages des technologies d'analyse moderne et la puissance de la bioinformatique s’est révélé un outil efficace dans la biologie des systèmes. En particulier, l'application de la métabolomique pour la découverte de nouveaux composés bioactifs constitue un domaine émergent dans la chimie des produits naturels. Dans ce contexte, le genre Acronychia de la famille des Rutaceae a été choisi sur la base de son usage en médecine traditionnelle pour ses propriétés antimicrobienne, antipyrétique, antispasmodique et anti-inflammatoire. Nombre de méthodes chromatographiques modernes, spectrométriques et spectroscopiques sont utilisées pour l'exploration de leur contenu en métabolites suivant trois axes principaux constituant les trois chapitres de cette thèse. En bref, le premier chapitre décrit l’étude phytochimique d’Acronychia pedunculata, l’identification des métabolites secondaires contenus dans cette espèce et l'évaluation de leurs propriétés biologiques. Le deuxième chapitre vise au développement de méthodes analytiques pour l'identification des dimères d’acétophénones (marqueurs chimiotaxonomiques du genre) et aux stratégies utilisées pour la déréplication de ces différents extraits et la caractérisation chimique des composés par UHPLC-HRMSn. Le troisième chapitre se concentre sur l'application de méthodologies métabolomique (RMN et LC-MS) pour l'analyse comparative (entre les différentes espèces, origines, organes), pour des études chimiotaxonomiques (entre les espèces) et pour la corrélation des composés contenus avec une activité pharmacologique. / Medicinal plants constitute an unfailing source of compounds (natural products – NPs) utilised in medicine for the prevention and treatment of various deceases. The introduction of new technologies and methods in the field of natural products chemistry enabled the development of high throughput methodologies for the chemical composition determination of plant extracts, evaluation of their properties and the exploration of their potentials as drug candidates. Lately, metabolomics, an integrated approach incorporating the advantages of modern analytical technologies and the power of bioinformatics has been proven an efficient tool in systems biology. In particular, the application of metabolomics for the discovery of new bioactive compounds constitutes an emerging field in natural products chemistry. In this context, Acronychia genus of Rutaceae family was selected based on its well-known traditional use as antimicrobial, antipyretic, antispasmodic and anti-inflammatory therapeutic agent. Modern chromatographic, spectrometric and spectroscopic methods were utilised for the exploration of their metabolite content following three basic axes constituting the three chapters of this thesis. Briefly, the first chapter describes the phytochemical investigation of Acronychia pedunculata, the identification of secondary metabolites contained in this species and evaluation of their biological properties. The second chapter refers to the development of analytical methods for the identification of acetophenones (chemotaxonomic markers of the genus) and to the dereplication strategies for the chemical characterisation of extracts by UHPLC-HRMSn. The third chapter focuses on the application of metabolomic methodologies (LC-MS & NMR) for comparative analysis (between different species, origins, organs), chemotaxonomic studies (between species) and compound-activity correlations.

<p>Transport into and across the cells of the human body is a prerequisite for the pharmacological action of drugs. Passive membrane permeability and active transport mechanisms are major determinants of the intestinal absorption of drugs, as well as of the distribution to target tissues and the subsequent metabolism and excretion from the body. In this thesis, the role of ATP-binding cassette (ABC) transporters and passive permeability on drug absorption and disposition was investigated. Particular emphasis was placed on defining the molecular properties important for these transport mechanisms. </p><p>The influence of different transport pathways on predictions of intestinal drug absorption was investigated using experimental models of different complexity. Experimental models that include the paracellular pathway gave improved predictions of intestinal drug absorption, especially for incompletely absorbed drugs. Further, the inhibition of the ABC transporters breast cancer resistance protein (BCRP/ABCG2) and multidrug-resistance associated protein 2 (MRP2/ABCC2) was experimentally investigated using structurally diverse datasets that were representative of orally administered drugs. A large number of previously unknown inhibitors were identified among registered drugs, but their clinical relevance for drug-drug interactions and drug-induced toxicity remains to be determined. The majority of the inhibitors affected all three major ABC transporters BCRP, MRP2 and P-glycoprotein (P gp/ABCB1), and these multi-specific inhibitors were found to be enriched in highly lipophilic weak bases. </p><p>To summarize, the present work has led to an increased knowledge of the molecular features of importance for ABC transporter inhibition and passive membrane permeability. Previously unknown ABC transporter inhibitors were identified and predictive computational models were developed for the different drug transport mechanisms. These could be valuable tools to assist in the prioritization of experimental efforts in early drug discovery.</p>

Transport into and across the cells of the human body is a prerequisite for the pharmacological action of drugs. Passive membrane permeability and active transport mechanisms are major determinants of the intestinal absorption of drugs, as well as of the distribution to target tissues and the subsequent metabolism and excretion from the body. In this thesis, the role of ATP-binding cassette (ABC) transporters and passive permeability on drug absorption and disposition was investigated. Particular emphasis was placed on defining the molecular properties important for these transport mechanisms. The influence of different transport pathways on predictions of intestinal drug absorption was investigated using experimental models of different complexity. Experimental models that include the paracellular pathway gave improved predictions of intestinal drug absorption, especially for incompletely absorbed drugs. Further, the inhibition of the ABC transporters breast cancer resistance protein (BCRP/ABCG2) and multidrug-resistance associated protein 2 (MRP2/ABCC2) was experimentally investigated using structurally diverse datasets that were representative of orally administered drugs. A large number of previously unknown inhibitors were identified among registered drugs, but their clinical relevance for drug-drug interactions and drug-induced toxicity remains to be determined. The majority of the inhibitors affected all three major ABC transporters BCRP, MRP2 and P-glycoprotein (P gp/ABCB1), and these multi-specific inhibitors were found to be enriched in highly lipophilic weak bases. To summarize, the present work has led to an increased knowledge of the molecular features of importance for ABC transporter inhibition and passive membrane permeability. Previously unknown ABC transporter inhibitors were identified and predictive computational models were developed for the different drug transport mechanisms. These could be valuable tools to assist in the prioritization of experimental efforts in early drug discovery.

In this research, it was mainly aimed to develop an acceptance model for processes, namely the process acceptance model (PAM). For this purpose, a questionnaire, comprising 3-part and 81-question, was developed to collect quantitative and qualitative data from people having relationships with certain process-focused models and/or standards (CMMI, ISO 15504, ISO 9001, ISO 27001, AQAP-160, AQAP-2110, and/or AS 9100). To revise and refine the questionnaire, expert reviews were ensured, and a pilot study was conducted with 60 usable responses. After reviews, refinements and piloting, the questionnaire was deployed to collect data and in-total 368 usable responses were collected from the people. Here, collected data were screened concerning incorrectly entered data, missing data, outliers and normality, and reliability and validity of the questionnaire were ensured. Partial least squares structural equation modeling (PLS SEM) was applied to develop the PAM. In this context, exploratory and confirmatory factor analyses were applied, and the initial model was estimated and evaluated. The initial model was modified as required by PLS SEM, and confirmatory factor analysis was repeated, and the modified final model was estimated and evaluated. Consequently, the PAM, with 18 factors and their statistically significant relationships, was developed. Furthermore, descriptive statistics and t-tests were applied to discover some interesting, meaningful, and important points to be taken into account regarding the acceptance of processes. Moreover, collected quantitative data were analyzed, and three additional factors were discovered regarding the acceptance of processes. Besides, a checklist to test and/or promote the acceptance of processes was established.

Dans les domaines de l'analyse, du contrôle et de la mesure physique, le laser constitue un outil métrologique particulièrement puissant et polyvalent, capable d'apporter des réponses concrètes à des problématiques variées, y compris d'ordre sociétal. Parmi ces dernières, la contamination des sites et des sols par les métaux lourds est un enjeu de santé publique important qui requiert de disposer de moyens de mesure adaptés aux réglementations existantes et suffisamment souples d'utilisation. Technique rapide et ne nécessitant pas de préparation de l'échantillon, la spectroscopie sur plasma induit par laser (LIBS) présente des avantages très intéressants pour réaliser des mesures sur site de la teneur en métaux lourds à l'échelle de la dizaine de ppm; la conception d'un appareil portable à moyen terme est envisageable.<br />Dans cette thèse nous montrons d'abord que le régime femtoseconde ne présente pas d'avantages par rapport au régime nanoseconde standard pour notre problématique. Ensuite nous mettons en œuvre un traitement avancé des spectres LIBS par des méthodes chimiométriques dont les performances améliorent sensiblement les résultats des analyses qualitatives et quantitatives d'échantillons de sols.

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.