Solidification microstructure-dependent hydrogen generation behavior of Al–Sn and Al–Fe alloys in alkaline medium

This work deals with the development of quantitative correlations of hydrogen evolution performance with solidification microstructural and thermal parameters in Al–1Sn, Al–2Sn, Al–1Fe, and Al-1.5Fe [wt.%] alloys. The cellular growth as a function of growth and cooling rates is evaluated using power type experimental laws, which allow determining representative intervals of microstructure length scale for comparison purposes with the results of immersion tests in 5 wt%NaOH solution. For both Al alloys systems, hydrogen evolution becomes slower as the alloy solute content increased. However, for a given alloy composition, whereas a more homogeneous distribution of Sn-rich particles promotes faster hydrogen generation using Al–Sn alloys, coarsening of Al₆Fe IMCs (intermetallic compounds) fibers favors hydrogen production using Al–Fe alloys. When solidification conditions that result in a range of cellular spacings within 16 and 19 μm are considered, the specific hydrogen production of the Al-1wt.%Fe alloy is higher than that of the other studied alloys.     

Monolayer and bilayer graphene on polydimethylsiloxane as a composite membrane for gas‐barrier applications

We report the preparation of centimeter‐scale composite membranes formed by monolayer or bilayer graphene and polydimethylsiloxane (PDMS). Graphene was synthesized on copper foils by chemical vapor deposition (CVD), and two methods for transferring the graphene layers from the Cu to the PDMS were tested. The method based on the use of poly(methyl methacrylate) as a sacrificial support layer was more effective in producing membranes with significant gas‐barrier properties, in which the gas permeability values for CO₂ and N₂ were reduced by up to 30% as compared to blank PDMS membranes. Raman spectroscopy maps and atomic force microscopy revealed that, on the microscopic scale, graphene is preserved upon transfer, but the presence of extended defects such as folds and tears still limits further increases in the composite barrier properties. The deleterious effect of such defects is reduced by using more than one graphene layer. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45521.     

Modeling vague spatial data warehouses using the VSCube conceptual model

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.     

Word co-occurrence features for text classification

In this article we propose a data treatment strategy to generate new discriminative features, called compound-features (or c-features), for the sake of text classification. These c-features are composed by terms that co-occur in documents without any restrictions on order or distance between terms within a document. This strategy precedes the classification task, in order to enhance documents with discriminative c-features. The idea is that, when c-features are used in conjunction with single-features, the ambiguity and noise inherent to their bag-of-words representation are reduced. We use c-features composed of two terms in order to make their usage computationally feasible while improving the classifier effectiveness. We test this approach with several classification algorithms and single-label multi-class text collections. Experimental results demonstrated gains in almost all evaluated scenarios, from the simplest algorithms such as kNN (13% gain in micro-average F₁ in the 20 Newsgroups collection) to the most complex one, the state-of-the-art SVM (10% gain in macro-average F₁ in the collection OHSUMED).     

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter may be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features.     

Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry

The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and three-dimensional models are publicly available.     

Fusion of feature sets and classifiers for facial expression recognition

This paper presents a novel method for facial expression recognition that employs the combination of two different feature sets in an ensemble approach. A pool of base support vector machine classifiers is created using Gabor filters and Local Binary Patterns. Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the minimization of both the error rate and the size of the ensemble. Experimental results on JAFFE and Cohn-Kanade databases have shown the efficiency of the proposed strategy in finding powerful ensembles, which improves the recognition rates between 5% and 10% over conventional approaches that employ single feature sets and single classifiers.