Brownian motion with drift threshold model

In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.

Framework for a business interoperability quotient measurement model

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova da Lisboa para obtenção do grau de Mestre em Engenharia e Gestão Industrial (MEGI)

Context-aware multi-factor authentication

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Iberian peninsula ecosystem carbon fluxes: a model-data integration study

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia do Ambiente pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Vision based context categorization for all-terrain robot

Dissertation presented at the Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering.

An information model for lean, agile, resilient and green supply chain management

Dissertação para a obtenção de Grau de Mestre em Engenharia e Gestão Industrial

Context-aware personalization environment for mobile computing

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Mobilie Banking adoption: a novel in the portuguese context

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Creating a business model for a pharmaceutical bank in Lisbon

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

An ANP model to support decision-making in a Portuguese pharmaceutical supply chain

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

A simulation model for lean, agile, resilient and green supply chain management: practices and interoperability assessment

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

R&D spillovers in an endogenous growth model with physical capital, human capital and varieties

There is a family of models with Physical, Human capital and R&D for which convergence properties have been discussed (Arnold, 2000a; Gómez, 2005). However, spillovers in R&D have been ignored in this context. We introduce spillovers in this model and derive its steady-state and stability properties. This new feature implies that the model is characterized by a system of four differential equations. A unique Balanced Growth Path along with a two dimensional stable manifold are obtained under simple and reasonable conditions. Transition is oscillatory toward the steady-state for plausible values of parameters.

Quality of process modeling using BPMN: a model-driven approach

Dissertação para obtenção do Grau de Doutor em Engenharia Informática

E-Health individual adoption - empirical model based on UTAUT 2.

The future of health care delivery is becoming more citizen-centred, as today’s user is more active, better informed and more demanding. The European Commission is promoting online health services and, therefore, member states will need to boost deployment and use of online services. This makes e-health adoption an important field to be studied and understood. This study applied the extended unified theory of acceptance and usage technology (UTAUT2) to explain patients’ individual adoption of e-health. An online questionnaire was administrated Portugal using mostly the same instrument used in UTAUT2 adapted to e-health context. We collected 386 valid answers. Performance expectancy, effort expectancy, social influence, and habit had the most significant explanatory power over behavioural intention and habit and behavioural intention over technology use. The model explained 52% of the variance in behavioural intention and 32% of the variance in technology use. Our research helps to understand the desired technology characteristics of ehealth. By testing an information technology acceptance model, we are able to determine what is more valued by patients when it comes to deciding whether to adopt e-health systems or not.

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.