This thesis presents the work conducted during a three year research project in the field of Control Systems and Biomaterials Engineering. The findings are presented over seven chapters, starting with a thorough literature review of the existing methods and key technologies, and following through by highlighting the existing problems with the current methods and how they have been overcome. The data is presented in tables, figures and photographs to enhance understanding and clarification. The research focuses on two relatively new manufacturing methods in the field of Tissue Engineering. Both of the methods are used for creating materials for regeneration of human and animal tissue, with the aim of replacing the current surgical methods. The methods are viewed from a control systems perspective and improvements have been made with the implementation of new technologies and methods. Additionally, further advancements are presented on the theoretical modelling field of control systems, where the shortfalls of existent modelling methods are highlighted and solutions proposed.

Fermentation processes play an integral role in the manufacture of pharmaceutical products. The Quality by Design initiative, combined with Process Analytical Technologies, aims to facilitate the consistent production of high quality products in the most efficient and economical way. The ability to estimate and control product quality from these processes is essential in achieving this aim. Large historical datasets are commonplace in the pharmaceutical industry and multivariate methods based on PCA and PLS have been successfully used in a wide range of applications to extract useful information from such datasets. This thesis has focused on the development and application of novel multivariate methods to the estimation and control of product quality from a number of processes. The document is divided into four main categories. Firstly, the related literature and inherent mathematical techniques are summarised. Following this, the three main technical areas of work are presented. The first of these relates to the development of a novel method for estimating the quality of products from a proprietary process using PCA. The ability to estimate product quality is useful for identifying production steps that are potentially problematic and also increases process efficiency by ensuring that any defective products are detected before they undergo any further processing. The proposed method is simple and robust and has been applied to two separate case studies, the results of which demonstrate the efficacy of the technique. The second area of work concentrates on the development of a novel method of identifying the operational phases of batch fermentation processes and is based on PCA and associated statistics. Knowledge of the operational phases of a process can be beneficial from a monitoring and control perspective and allows a process to be divided into phases that can be approximated by a linear model. The devised methodology is applied to two separate fermentation processes and results show the capability of the proposed method. The third area of work focuses on undertaking a performance evaluation of two multivariate algorithms, PLS and EPLS, in controlling the end-point product yield of fermentation processes. Control of end-point product quality is of crucial importance in many manufacturing industries, such as the pharmaceutical industry. Developing a controller based on historical and identification process data is attractive due to the simplicity of modelling and the increasing availability of process data. The methodology is applied to two case studies and performance evaluated. From both a prediction and control perspective, it is seen that EPLS outperforms PLS, which is important if modelling data is limited.

abstract: Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms. / Dissertation/Thesis / Ph.D. Computer Science 2011

Os serviços de emergências hospitalares (EH) desempenham um papel fundamental no sistema de saúde, servindo de porta de entrada para hospitais e fornecendo cuidados para pacientes com lesões e doenças graves. No entanto, as EH em todo o mundo sofrem com o aumento da demanda e superlotação. Múltiplos fatores convergem simultaneamente para resultar nessa superlotação, porém a otimização do gerenciamento do fluxo dos pacientes pode auxiliar na redução do problema. Nesse contexto, o tempo de permanência dos pacientes na EH (TPEH) é consolidado na literatura como indicador de qualidade do fluxo de pacientes. O tema desta dissertação é a previsão e gestão da demanda em EH com alto grau de congestionamento, que é abordado através de três artigos científicos. O objeto de estudo é o Hospital de Clínicas de Porto Alegre (HCPA). No primeiro artigo, são aplicados quatro modelos de previsão da procura por atendimento na EH, avaliando-se a influência de fatores climáticos e de calendário. O segundo artigo utiliza a técnica de regressão por mínimos quadrados parciais (PLS – partial least squares) para previsão de quatro indicadores relacionados ao TPEH para hospitais com alto grau de congestionamento. O tempo médio de permanência (TM) na EH resultou em um modelo preditivo com melhor ajuste, com erro médio absoluto percentual (MAPE - mean absolute percent error) de 5,68%. O terceiro artigo apresenta um estudo de simulação para identificação dos fatores internos do hospital que influenciam o TPEH. O número de exames de tomografias e a taxa de ocupação nas enfermarias clínicas e cirúrgicas (ECC) foram as que mais influenciaram. / Emergency departments (ED) play a key role in the health system, serving as gateway to hospitals and providing care for patients with injuries and serious illnesses. However, EDs worldwide suffer from increased demand and overcrowding. Multiple factors simultaneously converge to result in such overcrowding, and the optimization of patient flow management can help reduce the problem. In this context, the length of stay of patients in ED (LSED) is consolidated in the literature as a patient flow quality indicator. This thesis deals with forecast and demand management in EDs with a high degree of congestion. The subject is covered in three scientific papers, all analyzing data from the Hospital de Clínicas de Porto Alegre’s ED. In the first paper we apply four demand forecasting models to predict demand for service in the ED, evaluating the influence of climatic and calendar factors. The second article uses partial least squares (PLS) regression to predict four indicators related to LSED. The mean length of stay in the ED resulted in a model with the best fit, with mean percent absolute error (MAPE) of 5.68%. The third article presents a simulation study to identify the internal hospital factors influencing LSED. The number of CT exams and the occupancy rate in the clinical and surgical wards were the most influential factors.

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The University of ManchesterCarlos Alberto Duran VillalobosDoctor of Philosophy in the Faculty of Engineering and Physical SciencesDecember 2015This thesis presents an innovative batch-to-batch optimisation technique that was able to improve the productivity of two benchmark fed-batch fermentation simulators: Saccharomyces cerevisiae and Penicillin production. In developing the proposed technique, several important challenges needed to be addressed:For example, the technique relied on the use of a linear Multiway Partial Least Squares (MPLS) model to adapt from one operating region to another as productivity increased to estimate the end-point quality of each batch accurately. The proposed optimisation technique utilises a Quadratic Programming (QP) formulation to calculate the Manipulated Variable Trajectory (MVT) from one batch to the next. The main advantage of the proposed optimisation technique compared with other approaches that have been published was the increase of yield and the reduction of convergence speed to obtain an optimal MVT. Validity Constraints were also included into the batch-to-batch optimisation to restrict the QP calculations to the space only described by useful predictions of the MPLS model. The results from experiments over the two simulators showed that the validity constraints slowed the rate of convergence of the optimisation technique and in some cases resulted in a slight reduction in final yield. However, the introduction of the validity constraints did improve the consistency of the batch optimisation. Another important contribution of this thesis were a series of experiments that were implemented utilising a variety of smoothing techniques used in MPLS modelling combined with the proposed batch-to-batch optimisation technique. From the results of these experiments, it was clear that the MPLS model prediction accuracy did not significantly improve using these smoothing techniques. However, the batch-to-batch optimisation technique did show improvements when filtering was implemented.

Os serviços de emergências hospitalares (EH) desempenham um papel fundamental no sistema de saúde, servindo de porta de entrada para hospitais e fornecendo cuidados para pacientes com lesões e doenças graves. No entanto, as EH em todo o mundo sofrem com o aumento da demanda e superlotação. Múltiplos fatores convergem simultaneamente para resultar nessa superlotação, porém a otimização do gerenciamento do fluxo dos pacientes pode auxiliar na redução do problema. Nesse contexto, o tempo de permanência dos pacientes na EH (TPEH) é consolidado na literatura como indicador de qualidade do fluxo de pacientes. O tema desta dissertação é a previsão e gestão da demanda em EH com alto grau de congestionamento, que é abordado através de três artigos científicos. O objeto de estudo é o Hospital de Clínicas de Porto Alegre (HCPA). No primeiro artigo, são aplicados quatro modelos de previsão da procura por atendimento na EH, avaliando-se a influência de fatores climáticos e de calendário. O segundo artigo utiliza a técnica de regressão por mínimos quadrados parciais (PLS – partial least squares) para previsão de quatro indicadores relacionados ao TPEH para hospitais com alto grau de congestionamento. O tempo médio de permanência (TM) na EH resultou em um modelo preditivo com melhor ajuste, com erro médio absoluto percentual (MAPE - mean absolute percent error) de 5,68%. O terceiro artigo apresenta um estudo de simulação para identificação dos fatores internos do hospital que influenciam o TPEH. O número de exames de tomografias e a taxa de ocupação nas enfermarias clínicas e cirúrgicas (ECC) foram as que mais influenciaram. / Emergency departments (ED) play a key role in the health system, serving as gateway to hospitals and providing care for patients with injuries and serious illnesses. However, EDs worldwide suffer from increased demand and overcrowding. Multiple factors simultaneously converge to result in such overcrowding, and the optimization of patient flow management can help reduce the problem. In this context, the length of stay of patients in ED (LSED) is consolidated in the literature as a patient flow quality indicator. This thesis deals with forecast and demand management in EDs with a high degree of congestion. The subject is covered in three scientific papers, all analyzing data from the Hospital de Clínicas de Porto Alegre’s ED. In the first paper we apply four demand forecasting models to predict demand for service in the ED, evaluating the influence of climatic and calendar factors. The second article uses partial least squares (PLS) regression to predict four indicators related to LSED. The mean length of stay in the ED resulted in a model with the best fit, with mean percent absolute error (MAPE) of 5.68%. The third article presents a simulation study to identify the internal hospital factors influencing LSED. The number of CT exams and the occupancy rate in the clinical and surgical wards were the most influential factors.

Orientador: Ronei Jesus Poppi / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-08T04:12:09Z (GMT). No. of bitstreams: 1 Pataca_LuizCarlosMoutinho_D.pdf: 3563791 bytes, checksum: f2877b8f6fa77bd24a2a89eed93b78c4 (MD5) Previous issue date: 2006 / Doutorado / Quimica Analitica / Doutor em Ciências

Au cours de ces dernières années, les technologies mobiles ont créé des conditions de marché très concurrentielles. Face à cette nouvelle conjoncture, les banques ont lancé la banque mobile, une innovation technologique en milieu bancaire comme une nouvelle opportunité à saisir. Cette étude pose une question liée au cœur des principaux problèmes rencontrés dans le domaine bancaire : qui investit le plus dans les TIC, et qui vise à développer des relations à long terme avec ses clients. Afin de produire une valeur ajoutée sur les investissements technologiques, il devient important pour les banques d’assurer l’adoption de ces services par leurs clients dans un premier temps et d’assurer la survie de ces services (la continuité de l’utilisation) par le développement des relations durables et rentables avec les clients dans un deuxième temps. Ceci signifie que la compréhension des comportements des clients nécessite deux phases : la phase « adoption » et la phase « post-adoption ». La thèse vise, d’une part, à explorer les facteurs influençant l’adoption de la banque mobile par les clients et, d’autre part, à formuler un cadre explicatif de l’effet de ces facteurs pour établir et améliorer des relations entre les banques et leurs clients. L’analyse des données recueillies par questionnaire administré en face à face auprès de 282 répondants, identifie trois segments de clients : non utilisateurs, utilisateurs et adopteurs. L'analyse explicative réalisée par la méthode PLS relève le rôle important joué par quatre facteurs : l’utilité perçue, le risque perçu, la sécurité perçue et l’effort attendu dans les deux phases. / In recent years, mobile technologies have created very competitive market conditions. Facing this new environment, banks have launched mobile banking, a technological innovation in banking sector, as a new opportunity to seize. This study raises a question related to the heart of the main problems in banking: who invests the most in ICT, and who aims to develop long-term relationships with its clients. To produce added value on technological investments, it becomes important for banks to ensure the adoption of these services by their clients at first time and ensure the survival of these services (continuity of use) through the development of sustainable and profitable customer relationships in a second time. This means that the understanding of customer behavior requires two phases: the "adoption" phase and the "post-adoption" phase. The thesis aims, first, to explore the factors influencing the adoption of mobile banking by customers and, second, to formulate an explanatory framework of the effect of these factors to establish and improve relations between banks and their customers.The analysis of data collected by questionnaires administered face to face with 282 respondents identifies three customer segments: non-users, users and adopters. The explanatory analysis by the PLS method highlights the important role played by four factors: perceived usefulness, perceived risk, perceived safety and the expected effort in the two phases.

Companies are increasing their investment in technologies to enable better access to information and to gain a competitive advantage. Global competition is driving companies to reduce costs and enhance productivity, increasing their dependence on information technology. Information is a key asset within an organization and needs to be protected. Expanded connectivity and greater interdependence between companies and consumers has increased the damage potential of a security breach to a company's information systems. Improper unauthorized use of computer systems can create a devastating financial loss even to the point of causing the organization to go out of business. It is critically important to understand what causes users to understand, accept and to follow the organization's information systems security measures so that companies can realize the benefits of their technological investments. In the past several years, computer security breaches have stemmed from insider misuse and abuse of the information systems and non-compliance to the information systems security measures. The purpose of this study was to address the factors that affect employee acceptance of information systems security measures. The Technology Acceptance Model was extended and served as the theoretical framework for this study to examine the factors that affect employee adoption of information systems security measures. The research model included three independent dimensions, perceived ease of use, perceived usefulness and subjective norm. These constructs were hypothesized to predict intention to use information systems security measures, moderated by management support affecting subjective norm. Five hypotheses were posited. A questionnaire was developed to collect data from employees across multiple industry segments to test these hypotheses. Partial least squares statistical methodology was used to analyze the data and to test the hypotheses. The results of the statistical analysis supported three of the five hypotheses with subjective norm and management support showing the strongest effect on intention to use information systems security measures. Few studies have used TAM to study acceptance of systems in a mandatory environment and to specifically examine the employee acceptance of computer information systems security measures. This study, therefore, adds to the body of knowledge. Further, it provides important information for senior management and security professionals across multiple industries regarding the need to develop security policies and processes and to effectively communicate them throughout the organization and to design these measures to promote their use by employees in the organization.