As emoções e o desenvolvimento da criatividade dos alunos de 3º ciclo, no âmbito da Composição Coreográfica, na Escola Vocacional de Dança das Caldas da Rainha

Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

Indisciplina na sala de aula: a perspetiva de professores do 3º Ciclo do Ensino Básico e do Ensino Secundário

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação

Conhecimento dos alunos sobre geometria no início do 3.º ciclo

Dissertação apresentada para obtenção do grau de Mestre em Educação Matemática na Educação Pré-Escolar e nos 1.º e 2.º Ciclos do Ensino Básico

Práticas de ensino supervisionadas do 1º e 2º ciclo do ensino básico: a prática de resolução de problemas na aquisição do conceito de comprimento e de perímetro no 3.º ano de escolaridade

Relatório final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º Ciclos do Ensino Básico

A articulação entre a aula de técnica de dança clássica e o repertório : contributo de natureza técnico-artística em função do aperfeiçoamento do repertório clássico - 3º ano da Escola de Dança do Orfeão de Leiria

Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

ROM-less RNS-to-binary converter moduli {22N − 1, 22N + 1, 2N − 3, 2N + 3}

In this paper, a novel ROM-less RNS-to-binary converter is proposed, using a new balanced moduli set {22n-1, 22n + 1, 2n-3, 2n + 3} for n even. The proposed converter is implemented with a two stage ROM-less approach, which computes the value of X based only in arithmetic operations, without using lookup tables. Experimental results for 24 to 120 bits of Dynamic Range, show that the proposed converter structure allows a balanced system with 20% faster arithmetic channels regarding the related state of the art, while requiring similar area resources. This improvement in the channel's performance is enough to offset the higher conversion costs of the proposed converter. Furthermore, up to 20% better Power-Delay-Product efficiency metric can be achieved for the full RNS architecture using the proposed moduli set. © 2014 IEEE.

Homo-and heteropolymetallic 3-(2-pyridyl)pyrazolate manganese and rhenium complexes

fac-[MBr(CO)(3)(pypzH)] (M = Mn, Re; pypzH = (3-(2-pyridyl) pyrazole) complexes are prepared from fac[ MBr(CO)(3)(NCMe)(2)] and pypzH. The result of their deprotonation depends on the metallic substrate: the rhenium complex affords cleanly the bimetallic compound [fac-{Re(CO)(3)(mu(2)-pypz)}] 2 (mu(2)-pypz = mu(2)-3-(2pyridyl-. 1N) pyrazolate-2. 1N), which was crystallographically characterized, whereas a similar manganese complex was not detected. When two equivalents of pyridylpyrazolate are used, polymetallic species [fac-M(CO) 3(mu(2)-pypz)(mu(3)-pypz) M'] (mu(3)-pypz = mu(3)-3-(2-pyridyl-kappa N-1) pyrazolate-1 kappa 2N, N: 2. 1N:; M = Mn, M' = Li, Na, K; M = Re, M' = Na) are obtained. The crystal structures of the manganese carbonylate complexes were determined. The lithium complex is a monomer containing one manganese and one lithium atom, whereas the sodium and potassium complexes are dimers and reveal an unprecedented coordination mode for the bridging 3-(2-pyridyl) pyrazolate ligand, where the nitrogen of the pyridyl fragment and the nitrogen-1 of pyrazolate are chelated to manganese atoms, and each nitrogen-2 of pyrazolate is coordinated to two alkaline atoms. The polymetallic carbonylate complexes are unstable in solution and evolve spontaneously to [fac-{Re(CO) 3(mu(2)-pypz)}](2) or to the trimetallic paramagnetic species [MnII(mu(2)-pypz) 2{fac-{MnI(CO) 3(mu(2)-pypz)}(2)}]. The related complex cis-[MnCl2(pypzH)(2)] was also synthesized and structurally characterized. The electrochemical behavior of the new homo-and heteropolymetallic 3-(2-pyridyl) pyrazolate complexes has been studied and details of their redox properties are reported.

Conceções das crianças no 3.º e 5.º ano sobre o conceito de ser vivo

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º Ciclo do Ensino Básico

Significados de fração – um estudo numa turma de 3.º ano

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

Generalização matemática baseada na análise de padrões numa turma de 3.º ano

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º ciclo do Ensino Básico

A high throughput colorimetric assay of beta-1,3-D-glucans by Congo red dye

Mushroom strains contain complex nutritional biomolecules with a wide spectrum of therapeutic and prophylactic properties. Among these compounds, β-d-glucans play an important role in immuno-modulating and anti-tumor activities. The present work involves a novel colorimetric assay method for β-1,3-d-glucans with a triple helix tertiary structure by using Congo red. The specific interaction that occurs between Congo red and β-1,3-d-glucan was detected by bathochromic shift from 488 to 516 nm (> 20 nm) in UV–Vis spectrophotometer. A micro- and high throughput method based on a 96-well microtiter plate was devised which presents several advantages over the published methods since it requires only 1.51 μg of polysaccharides in samples, greater sensitivity, speed, assay of many samples and very cheap. β-d-Glucans of several mushrooms (i.e., Coriolus versicolor, Ganoderma lucidum, Pleurotus ostreatus, Ganoderma carnosum, Hericium erinaceus, Lentinula edodes, Inonotus obliquus, Auricularia auricular, Polyporus umbellatus, Cordyseps sinensis, Agaricus blazei, Poria cocos) were isolated by using a sequence of several extractions with cold and boiling water, acidic and alkaline conditions and quantified by this microtiter plate method. FTIR spectroscopy was used to study the structural features of β-1,3-d-glucans in these mushroom samples as well as the specific interaction of these polysaccharides with Congo red. The effect of NaOH on triple helix conformation of β-1,3-d-glucans was investigated in several mushroom species.

Humidity passive sensors based on UHF RFID using cork dielectric slabs

In this paper we show the design of passive UHF RFID tag antenna on cork substrate. Due to the cork sensitivity to humidity changes, we can use the developed sensor to sense changes in the relative humidity of the environment, without the need for batteries. The antenna is built using inkjet printing technology, which allows a good accuracy of the design manufacturing. The sensor proved usable for humidity changes detection with a variation of threshold power from 11 to 15 dB between 60 and near 100% humidity levels. Presenting, therefore, reading ranges between 3 to 5 meters. © 2015 EurAAP.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.