Maximizing the utility of bio-based diisocyanate and chain extenders in crystalline segmented thermoplastic polyester urethanes: effect of polymerization protocol

High molecular weight semi crystalline thermoplastic poly(ester urethanes), TPEUs, were prepared from a vegetable oil-based diisocyanate, aliphatic diol chain extenders and poly(ethylene adipate) macro diol using one-shot, pre-polymer and multi-stage polyaddition methods. The optimized polymerization reaction achieved ultra-high molecular weight TPEUs (>2 million as determined by GPC) in a short time, indicating a very high HPMDI diol reactivity. TPEUs with very well controlled hard segment (HS) and soft segment (SS) blocks were prepared and characterized with DSC, TGA, tensile analysis, and WAXD in order to reveal structure property relationships. A confinement effect that imparts elastomeric properties to otherwise thermoplastic TPEUs was revealed. The confinement extent was found to vary predictably with structure indicating that one can custom engineer tougher polyurethane elastomers by "tuning" soft segment crystallinity with suitable HS block structure. Generally, the HPMDI-based TPEUs exhibited thermal stability and mechanical properties comparable to entirely petroleum-based TPEUs. (C) 2014 Elsevier Ltd. All rights reserved.

Contos de fadas no ensino fundamental I: analisando os recursos empregados e as estratégias que podem ser adotadas pelas/os docentes na desconstrução de estereótipos sexistas

Pós-graduação em Educação Sexual - FCLAR

Swarm-based descriptor combination and its application for image classification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Entre choques, cortes e fissuras - a (semi)formação estética: uma análise crítica da apropriação de filmes na educação escolar

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Algoritmos de aprendizado semi-supervisionado baseados em grafos aplicados na bioinformática

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

MHD numerical simulations of colliding winds in massive binary systems - I. Thermal versus non-thermal radio emission

In the past few decades detailed observations of radio and X-ray emission from massive binary systems revealed a whole new physics present in such systems. Both thermal and non-thermal components of this emission indicate that most of the radiation at these bands originates in shocks. O and B-type stars and WolfRayet (WR) stars present supersonic and massive winds that, when colliding, emit largely due to the freefree radiation. The non-thermal radio and X-ray emissions are due to synchrotron and inverse Compton processes, respectively. In this case, magnetic fields are expected to play an important role in the emission distribution. In the past few years the modelling of the freefree and synchrotron emissions from massive binary systems have been based on purely hydrodynamical simulations, and ad hoc assumptions regarding the distribution of magnetic energy and the field geometry. In this work we provide the first full magnetohydrodynamic numerical simulations of windwind collision in massive binary systems. We study the freefree emission characterizing its dependence on the stellar and orbital parameters. We also study self-consistently the evolution of the magnetic field at the shock region, obtaining also the synchrotron energy distribution integrated along different lines of sight. We show that the magnetic field in the shocks is larger than that obtained when the proportionality between B and the plasma density is assumed. Also, we show that the role of the synchrotron emission relative to the total radio emission has been underestimated.

Magnetic Tuning of All-Organic Binary Alloys between Two Stable Radicals

Mixtures of 2-(4,5,6,7-tetrafluorobenzimidazol-2-yl)-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazole-3-oxide-1-oxyl (F4BImNN) and 2-(benzi-midazol-2-yl)-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazole-3-oxide-1-oxyl (BImNN.) crystallize as solid solutions (alloys) across a wide range of binary compositions. (F4BImNN)(x)(BImNN)((1-x)) with x < 0.8 gives orthorhombic unit cells, while x >= 0.9 gives monoclinic unit cells. In all crystalline samples, the dominant intermolecular packing is controlled by one-dimensional (1D) hydrogen-bonded chains that lead to quasi-1D ferromagnetic behavior. Magnetic analysis over 0.4-300 K indicates ordering with strong 1D ferromagnetic exchange along the chains (J/k = 12-22 K). Interchain exchange is estimated to be 33- to 150-fold weaker, based on antiferromagnetic ordered phase formation below Neel temperatures in the 0.4-1.2 K range for the various compositions. The ordering temperatures of the orthorhombic samples increase linearly as (1 - x) increases from 0.25 to 1.00. The variation is attributed to increased interchain distance corresponding to decreased interchain exchange, when more F4BImNN is added into the orthorhombic lattice. The monoclinic samples are not part of the same trend, due to the different interchain arrangement associated with the phase change.

Three time-based scale formulations for the two-stage lot sizing and scheduling in process industries

In this paper, we propose three novel mathematical models for the two-stage lot-sizing and scheduling problems present in many process industries. The problem shares a continuous or quasi-continuous production feature upstream and a discrete manufacturing feature downstream, which must be synchronized. Different time-based scale representations are discussed. The first formulation encompasses a discrete-time representation. The second one is a hybrid continuous-discrete model. The last formulation is based on a continuous-time model representation. Computational tests with state-of-the-art MIP solver show that the discrete-time representation provides better feasible solutions in short running time. On the other hand, the hybrid model achieves better solutions for longer computational times and was able to prove optimality more often. The continuous-type model is the most flexible of the three for incorporating additional operational requirements, at a cost of having the worst computational performance. Journal of the Operational Research Society (2012) 63, 1613-1630. doi:10.1057/jors.2011.159 published online 7 March 2012

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

An unusual feature of yolk sac placentation in Necromys lasiurus (Rodentia, Cricetidae, Sigmodontinae)

We studied the development of the inverted yolk sac in a New World rodent, Necromys lasiurus during early placentation. Ten implantation sites were investigated by means of histology, immunohistochemistry and electron microscopy. The yolk sac was villous near its attachment to the placenta. Elsewhere it was non-villous and closely attached to the uterus. The uterine glands were shallow and wide mouthed. They were associated with vessels and filled with secretion, suggesting the release of histotroph. This feature was absent at later stages. The intimate association of the yolk sac with specialized glandular regions of the uterus may represent a derived character condition of Necromys and/or sigmodont rodents. (C) 2012 Elsevier Ltd. All rights reserved.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.