The national physics and chemistry exams and the learning of sciences

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

Machine learning Gaussian short rate

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Learning Activities with Semantic. Hypermedia in Higher Education

The increasing use of information and communication technologies (ICT) in diverse professional and personal contexts calls for new knowledge, and a set of abilities, competences and attitudes, for an active and participative citizenship. In this context it is acknowledged that universities have an important role innovating in the educational use of digital media to promote an inclusive digital literacy. The educational potential of digital technologies and resources has been recognized by both researchers and practitioners. Multiple pedagogical models and research approaches have already contributed to put in evidence the importance of adapting instructional and learning practices and processes to concrete contexts and educational goals. Still, academic and scientific communities believe further investments in ICT research is needed in higher education. This study focuses on educational models that may contribute to support digital technology uses, where these can have cognitive and educational relevance when compared to analogical technologies. A teaching and learning model, centered in the active role of the students in the exploration, production, presentation and discussion of interactive multimedia materials, was developed and applied using the internet and exploring emergent semantic hypermedia formats. The research approach focused on the definition of design principles for developing class activities that were applied in three different iterations in undergraduate courses from two institutions, namely the University of Texas at Austin, USA and the University of Lisbon, Portugal. The analysis of this study made possible to evaluate the potential and efficacy of the model proposed and the authoring tool chosen in the support of metacognitive skills and attitudes related to information structuring and management, storytelling and communication, using computers and the internet.

Management consulting labs-Galp loyalty learning experience

The purpose of this project was to analyze Galp’s loyalty approach in the Portuguese fuel market given the industry context, namely the entry of hypermarket and the resulting increase in competitiveness. The team performed analyses based on analytical models, qualitative research and internal interviews in order to assess Galp’s potential in the field of loyalty and consumers’ behavior. The final recommendations were based on incremental improvements to the Galp’s existing loyalty tool and an innovative paradigm change of the approach to loyalty.

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

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

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Data mining with graphical models

Data Mining, Learning from data, graphical models, possibility theory

Learning from Shocks and the Decision to Open

Two claims pervade the literature on the political economy of market reforms: that economic crises cause reforms; and that crises matter because they bring into question the validity of the economic model held to be responsible for them. Economic crises are said to spur a process of learning that is conducive to the abandonment of failing models and to the adoption of successful models. But although these claims have become the conventional wisdom, they have been hardly tested empirically due to the lack of agreement on what constitutes a crisis and to difficulties in measuring learning from them. I propose a model of rational learning from experience and apply it to the decision to open the economy. Using data from 1964 through 1990, I show that learning from the 1982 debt crisis was relevant to the first wave of adoption of an export promotion strategy, but learning was conditional on the high variability of economic outcomes in countries that opened up to trade. Learning was also symbolic in that the sheer number of other countries that liberalized was a more important driver of others’ decisions to follow suit.

Caracteritació de models funcionals d'àmfores romanes mitjançant Finite Element Analysis

Projecte de recerca elaborat a partir d’una estada al Laboratory of Archaeometry del National Centre of Scientific Research “Demokritos” d’Atenes, Grècia, entre juny i setembre 2006. Aquest estudi s’emmarca dins d’un context més ampli d’estudi del canvi tecnològic que es documenta en la producció d’àmfores de tipologia romana durant els segles I aC i I dC en els territoris costaners de Catalunya. Una part d’aquest estudi contempla el càlcul de les propietats mecàniques d’aquestes àmfores i la seva avaluació en funció de la tipologia amforal, a partir de l’Anàlisi d’Elements Finits (AEF). L’AEF és una aproximació numèrica que té el seu origen en les ciències d’enginyeria i que ha estat emprada per estimar el comportament mecànic d’un model en termes, per exemple, de deformació i estrès. Així, un objecte, o millor dit el seu model, es dividit en sub-dominis anomenats elements finits, als quals se’ls atribueixen les propietats mecàniques del material en estudi. Aquests elements finits estan connectats formant una xarxa amb constriccions que pot ser definida. En el cas d’aplicar una força determinada a un model, el comportament de l’objecte pot ser estimat mitjançant el conjunt d’equacions lineals que defineixen el rendiment dels elements finits, proporcionant una bona aproximació per a la descripció de la deformació estructural. Així, aquesta simulació per ordinador suposa una important eina per entendre la funcionalitat de ceràmiques arqueològiques. Aquest procediment representa un model quantitatiu per predir el trencament de l’objecte ceràmic quan aquest és sotmès a diferents condicions de pressió. Aquest model ha estat aplicat a diferents tipologies amforals. Els resultats preliminars mostren diferències significatives entre la tipologia pre-romana i les tipologies romanes, així com entre els mateixos dissenys amforals romans, d’importants implicacions arqueològiques.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.

A behavioral study of alpha-1b adrenergic receptor knockout mice: increased reaction to novelty and selectively reduced learning capacities.

Knockout mice lacking the alpha-1b adrenergic receptor were tested in behavioral experiments. Reaction to novelty was first assessed in a simple test in which the time taken by the knockout mice and their littermate controls to enter a second compartment was compared. Then the mice were tested in an open field to which unknown objects were subsequently added. Special novelty was introduced by moving one of the familiar objects to another location in the open field. Spatial behavior and memory were further studied in a homing board test, and in the water maze. The alpha-1b knockout mice showed an enhanced reactivity to new situations. They were faster to enter the new environment, covered longer paths in the open field, and spent more time exploring the new objects. They reacted like controls to modification inducing spatial novelty. In the homing board test, both the knockout mice and the control mice seemed to use a combination of distant visual and proximal olfactory cues, showing place preference only if the two types of cues were redundant. In the water maze the alpha-1b knockout mice were unable to learn the task, which was confirmed in a probe trial without platform. They were perfectly able, however, to escape in a visible platform procedure. These results confirm previous findings showing that the noradrenergic pathway is important for the modulation of behaviors such as reaction to novelty and exploration, and suggest that this is mediated, at least partly, through the alpha-1b adrenergic receptors. The lack of alpha-1b adrenergic receptors in spatial orientation does not seem important in cue-rich tasks but may interfere with orientation in situations providing distant cues only.

Notes on Agents’ Behavioral Rules Under Adaptive Learning and Studies of Monetary Policy

These notes try to clarify some discussions on the formulation of individual intertemporal behavior under adaptive learning in representative agent models. First, we discuss two suggested approaches and related issues in the context of a simple consumption-saving model. Second, we show that the analysis of learning in the NewKeynesian monetary policy model based on “Euler equations” provides a consistent and valid approach.