Alcohol sponsored events: the impact on attitudes and intentions, and the mitigating impact of persuasion knowledge

Doctor of Philosophy in Marketing and Strategy

An algorithmic approach to estimating the minimal number of periodic points for smooth self-maps of simply-connected manifolds

For a given self-map f of M, a closed smooth connected and simply-connected manifold of dimension m ≥ 4, we provide an algorithm for estimating the values of the topological invariant Dm r [f], which equals the minimal number of r-periodic points in the smooth homotopy class of f. Our results are based on the combinatorial scheme for computing Dm r [f] introduced by G. Graff and J. Jezierski [J. Fixed Point Theory Appl. 13 (2013), 63–84]. An open-source implementation of the algorithm programmed in C++ is publicly available at http://www.pawelpilarczyk.com/combtop/.

A data-driven analytics approach in the study of pneumonia’s fatalities

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

As potencialidades pedagógicas da WebQuest no ensino e aprendizagem da Geologia: um estudo com alunos do 11.º ano no tema "Principais etapas de formação das rochas sedimentares"

Relatório de estágio de mestrado em Ensino de Biologia e Geologia no 3.º Ciclo do Ensino Básico e no Ensino Secundário

Perspetivas de pais acerca da inclusão de alunos com transtornos do espectro do autismo na escola regular, da rede estadual de ensino da zona leste da cidade de Manaus - Amazonas, Brasil

Dissertação de mestrado em Educação Especial (área de especialização em Dificuldades de Aprendizagem Específicas)

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Industrial engineering and management curriculum profile: Developing a framework of competences

This paper presents a framework of competences developed for Industrial Engineering and Management that can be used as a tool for curriculum analysis and design, including the teaching and learning processes as well as the alignment of the curriculum with the professional profile. The framework was applied to the Industrial Engineering and Management program at University of Minho (UMinho), Portugal, and it provides an overview of the connection between IEM knowledge areas and the competences defined in its curriculum. The framework of competences was developed through a process of analysis using a combination of methods and sources for data collection. The framework was developed according to four main steps: 1) characterization of IEM knowledge areas; 2) definition of IEM competences; 3) survey; 4) application of the framework at the IEM curriculum. The findings showed that the framework is useful to build an integrated vision of the curriculum. The most visible aspect in the learning outcomes of IEM program is the lack of balance between technical and transversal competences. There was not almost any reference to the transversal competences and it is fundamentally concentrated on Project-Based Learning courses. The framework presented in this paper provides a contribution to the definition of IEM professional profile through a set of competences which need to be explored further. In addition, it may be a relevant tool for IEM curriculum analysis and a contribution for bridging the gap between universities and companies.

Ensino, aprendizagem e avaliação no Ensino Superior: Perspetivas e práticas

Tese de Doutoramento em Ciências da Educação - Especialidade de Desenvolvimento Curricular

Public service culture collections of microorganisms: are they important for biotechnology?

Microbiology as a scientific discipline recognised the need to preserve microorganisms for scientific studies establishing from its very beginning research culture collections (CC). Later on, to better serve different scientific fields and bioindustries with the increasing number of strains of scientific, medical, ecological and biotechnological importance public service CC were established with the specific aims to support their user communities. Currently, the more developed public service CC are recognised as microBiological Resources Centres (mBRC). mBRC are considered to be one of the key elements for sustainable international scientific infrastructure, which is necessary to underpin successful delivery of the benefits of biotechnology, whether within the health sector, the industrial sector or other sectors, and in turn ensure that these advances help drive economic growth. In more detail, mBRCs are defined by Organisation for Economic Co-operation and Development (OECD) as service providers and repositories of the living cells, genomes of organisms, and information relating to heredity and functions of biological systems. mBRCs contain collections of culturable organisms (e.g., microorganisms, plant, animal cells), replicable parts of these (e.g. genomes, plasmids, virus, cDNAs), viable but not yet culturable organisms, cells and tissues, as well as database containing molecular, physiological and structural information relevant to these collections and related bioinformatics. Thus mBRCs are fundamental to harnessing and preserving the world’s microbial biodiversity and genetic resources and serve as an essential element of the infrastructure for research and development. mBRCs serve a multitude of functions and assume a range of shapes and forms. Some are large national centres performing a comprehensive role providing access to diverse organisms. Other centres play much narrower, yet important, roles supplying limited but crucial specialised resources. In the era of the knowledge-based bio-economy mBRCs are recognised as vital element to underpinning the biotechnology.

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An evaluation of parchments' degradation a hybrid approach

Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and tools for problem solving. Hence, in this work we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

A new and efficient synthesis of benzoimidazopurine derivatives

[Excerpt] Purine nucleobases are essential biomolecules in living organisms. Playing several key roles in the cell, they have been a significant inspiration for drug design.1 Benzimidazole nucleus is an important pharmacophore in the development of molecules with pharmaceutical or biological interest. Benzimidazoles have been reported to display significant pharmacological activities such as antiulcer, antifungal, antiparkinson, anticancer and antibiotic.2 Fused structures incorporating these two scaffolds might be important for medicinal chemistry and, to the best of our knowledge, there are no reports of these systems in the literature. In particular, benzo[4,5]imidazo[2,1]purines seem to be novel and must be important target molecules in the heterocyclic synthesis. (...)