Digital serious games and simulation games - comparison of two approaches to lean training

In an increasingly competitive and globalized world, companies need effective training methodologies and tools for their employees. However, selecting the most suitable ones is not an easy task. It depends on the requirements of the target group (namely time restrictions), on the specificities of the contents, etc. This is typically the case for training in Lean, the waste elimination manufacturing philosophy. This paper presents and compares two different approaches to lean training methodologies and tools: a simulation game based on a single realistic manufacturing platform, involving production and assembly operations that allows learning by playing; and a digital game that helps understand lean tools. This paper shows that both tools have advantages in terms of trainee motivation and knowledge acquisition. Furthermore, they can be used in a complementary way, reinforcing the acquired knowledge.

FEW phone file system

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

Brincar faz-me feliz: a importância do brincar no desenvolvimento da criança

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Brincar para aprender: as intenções do educador

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Brincadeiras entre as crianças no jardim de infância

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

A brincadeira e o género em jardim-de-infância

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Comédia física: the playfull actor: estágio profissional

Relatório de Estágio submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Artes Performativas, Especialidade Interpretação.

Participação de crianças com multidificiência e deficiência visual em actividades na escola : orientações para a intervenção

O crescente reconhecimento das limitações das crianças com multideficiência e deficiência visual, quer nas interacções com os parceiros quer de uma forma geral nos ambientes em que se inserem, motivou este estudo, que pretendeu analisar o nível de participação destas crianças em actividades na escola. Considerando a importância de contribuir com informação para orientações na intervenção educativa de crianças com MDVI, realizou-se um estudo que analisa o seu comportamento e envolvimento em actividades da escola. Para a realização deste estudo, observaram-se os comportamentos de três crianças com MDVI, com idades compreendidas entre os 9 e os 10 anos, em três ambientes da escola, nomeadamente a sala de aula, o refeitório e o recreio, e em três actividades (pintura, jogos, almoço, saltar à corda, andar de baloiço e subir escadas) de forma a analisar o seu envolvimento e limitações nas actividades. Na análise dos dados das observações foram identificadas quatro categorias de participação: Inicia, Perde Oportunidade, Inicia com Apoio e Comportamento Potencialmente Comunicativo, registando-se valores que permitiram encontrar características dos comportamentos das crianças observadas, assim como o seu nível de participação em actividades na escola. Os resultados do estudo permitiram verificar que a participação das crianças em actividades está condicionada pelos ambientes em que estão envolvidas, e não pelas problemáticas que cada criança apresenta.----------------------------------------ABSTRACT: The motivation of this study is the increasing knowledge and awareness of children who have multiple disabilities and a visual impairment (MDVI) and the limitation with their peer interactions and in general. The purpose of this study was to analyze the participation level of children with MDVI in school activities. Considering the importance of contributing with guidelines for educational intervention with children with MDVI, we did a study that analyzes the behavior and the level of participation of MDVI children in school activities. In this research study we observed the behavior of three children with MDVI, of 9/10 years old, in three different environments at school; the classroom, the canteen and the playground, and in different activities (painting, playing games, having lunch, skipping rope, etc), in order to analyze their participation and their activity limitations in the activities referred. Data analysis identified four categories of participation: Initiation; Missed Opportunities; Initiation with support and Potentially communicative behavior. Results of data analysis allowed us to find out characteristics of children´s behavior, as well as their level of participation in activities. The main findings of this research allowed us to verify that the child’s engagement in activities depends on the environments where they are located and not on their disability.

Multi-strand narrative structures: a filmic game of multiple players

Session 7: Playing with Roles, images and improvising New States of Awareness, 3rd Global Conference, 1st November – 3rd November, 2014, Prague, Czech Republic.

Schistosomiasis mansoni in an area of low transmission: II. Risk factors for infection

Risk factors for Schistosoma mansoni infection were identified using a 1:1 matched case-control design. The work was conducted in the municipality of Pedro de Toledo, São Paulo State, Brazil, an area where the snail host is Biomphalaria tenagophila. Information on water contact patterns, knowledge, attitudes and pratices (kap), socioeconomic and sanitary conditions were obtained by mean of questionnaires. The crude odds ratio estimates and the adjusted odds ratio estimates using the logistic regression model are presented. Most of the examined individuals admitted recent water contacts (90.6% of the cases). The most frequent reason for contact was swimming, playing and fishing and the preferential site of contact was the river. According to the logistic regression technique, the main risk factors for infection were: a) water contact through swimming, playing and fishing; b) fording; c) bad hygiene. We concluded that recreational activities are the main reasons for schistosomiasis transmission in Pedro de Toledo and leisure alternatives should be offered to the local population.

Pozzolanic activity of oil-refining catalyst: evaluation by electron and atomic microscopy

The reuse of waste fluid catalytic cracking (FCC) catalyst as partial surrogate for cement can reduce the environmental impact of both the oil-refinery and cement production industries [1,2]. FCC catalysts can be considered as pozzolanic materials since in the presence of water they tend to chemically react with calcium hydroxide to produce compounds possessing cementitious properties [3,4]. In addition, partial replacement of cement with FCC catalysts can enhance the performance of pastes and mortars, namely by improving their compressive strength [5,6]. In the present work the reaction of waste FCC catalyst with Ca(OH)2 has been investigated after a curing time of 28 days by scanning electron microscopy (SEM) with electron backscattered signal (BSE) combined with X-ray energy dispersive spectroscopy (EDS) carried out with a JEOL JSM 7001F instrument operated at 15 kV coupled to an INCA pentaFetx3 Oxford spectrometer. The polished cross-sections of FCC particles embedded in resin have also been evaluated by atomic force microscopy (AFM) in contact mode (CM) using a NanoSurf EasyScan 2 instrument. The SEM/EDS results revealed that an inward migration of Ca occurred during the reaction. A weaker outward migration of Si and Al was also apparent (Fig. 1). The migration of Ca was not homogeneous and tended to follow high-diffusivity paths within the porous waste FCC catalyst particles. The present study suggests that the porosity of waste FCC catalysts is key for the migration/reaction of Ca from the surrounding matrix, playing an important role in the pozzolanic activity of the system. The topography images and surface roughness parameters obtained by atomic force microscopy can be used to infer the local porosity in waste FCC catalyst particles (Fig. 2).

Contributo para o estudo da infecção congénita pelo vírus citomegálico em Portugal

Resumo O vírus citomegálico humano (CMV) é o principal agente de infecção congénita, atingindo cerca de 0.2 a 2.2% de todos os recém-nascidos. As crianças que nascem infectadas por este vírus têm cerca de 11% a 12.7% de probabilidades de apresentarem sintomas e sinais de doença citomegálica ao nascimento, podendo cerca de 40 a 58% destas virem a apresentar sequelas neurológicas permanentes. Das crianças infectadas que terão infecção assintomática no período neo-natal, 5 a 15% poderão vir igualmente a sofrer de sequelas tardias, sobretudo a surdez ou o atraso mental. Em Portugal, desconhece-se a dimensão deste problema. O primeiro objectivo desta dissertação foi, desta forma, a determinação da prevalência através do recurso aos cartões do diagnóstico precoce (“Guthrie cards���), utilizando uma técnica de nested-PCR dirigida para o vírus. Foram estudados 3600 cartões, seleccionados de todo o território nacional (continente e ilhas), de uma forma proporcional ao número de nascimentos em cada distrito, dos quais 38 foram positivos, o que dá uma prevalência de 1.05% (intervalo de confiança para 95%: 0.748-1.446). A revisão sobre a experiência acumulada nos últimos 15 anos, na área do diagnóstico pré-natal, juntamente com um estudo adicional sobre a técnica da avidez, permitiu retirar algumas ilações, nomeadamente que este diagnóstico constitui uma arma diagnostica fiável para a avaliação pré-natal desta infecção congénita e que a selecção dos casos para amniocentese deverá obedecer a indicações serológicas precisas, como a “seroconversão para IgG” ou a “IgM confirmada” (devendo o método de confirmação ser a avidez das IgG com um índice <0,6) e as alterações ecográficas de etiologia não esclarecida. A possibilidade de utilizar pools de urinas para detectar a infecção congénita por CMV foi abordada na terceira parte do trabalho experimental. A metodologia aí descrita teve correlação total com o método de referência, permitindo uma redução bastante significativa nos tempos de execução e nos custos em consumíveis, pelo que abre a possibilidade da sua utilização para o rastreio da infecção congénita por CMV nos recém-nascidos.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

The ubiquitin editing enzyme A20 maintains immune homeostasis and prevents autoimmunity

Dissertation presented to obtain the Doctorate degree (Ph.D.) in Biology at Instituto de Tecnologia Química e Biológica da Universidade Nova de Lisboa