An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

Short communication: High frequency of β-lactam-resistant Staphylococcus aureus in artisanal coalho cheese made from goat milk produced in northeastern Brazil

Reports of β-lactam-resistant Staphylococcus aureus in artisanal goat cheese are increasing, and this phenomenon is relevant to public health. The objective of the present study was to determine the prevalence of S. aureus strains carrying the blaZ and mecA resistance genes, as well as the genes encoding the staphylococcal enterotoxins SEA, SEB, SEC, SED, SEE, and TSST-1 in artisanal coalho cheese made from goat milk produced in northeastern Brazil. We used biochemical and molecular tests to characterize 54 S. aureus isolates found in artisanal coalho cheese collected from commercial establishments producing animal products in 11 municipalities of Pernambuco State, Brazil. A PCR analysis revealed that 42.6% (23/54) of the isolates were positive for the blaZ gene, and 7.4% (4/54) were resistant to methicillin by phenotypic testing. We did not detect mecA or any genes encoding enterotoxins. The presence of S. aureus carriers of the blaZ gene and the identification of methicillin-resistant S. aureus strains are of concern for the health of consumers of this type of cheese.     

Chromatographic Methods for the Determination of Carbohydrates and Organic Acids in Foods of Animal Origin

Carbohydrates are ubiquitous and range from simple monosaccharides to large complex polysaccharides. Organic acids are compounds with acidic properties. Both occur naturally in many foods and in fermented products. Organic acids are usually derived from the hydrolysis of carbohydrates by microorganisms such as lactic acid bacteria. These bacteria convert carbohydrates into energy required for growth, since they are not equipped with the enzymes necessary for respiration and are unable to perform oxidative phosphorylation. Determination of carbohydrates and organic acids in foods of animal origin is important, since they contribute to flavor and texture. Their presence and proportions can affect the chemical and sensory characteristics of a food matrix and they can provide information on nutritional properties of food and the means to optimize selected technological processes. Furthermore, the levels of carbohydrate and organic acid are important to monitor bacterial growth and activity. Actually, these compounds can be quantified by several methods including high‐performance liquid chromatography (HPLC) and gas chromatography (GC). High‐performance liquid chromatography has been widely used to analyze carbohydrates and nonvolatile organic acids, while gas chromatography has been used to determine the volatile organic acids in complex matrices. This contribution provides an overview of chromatographic methods (HPLC and GC) used to analyze carbohydrates and organic acids in foods of animal origin.     

Magnetic polystyrene–palygorskite nanocomposite obtained by heterogeneous phase polymerization to apply in the treatment of oily waters

Water contaminated by oil poses challenges to the management of water resources. Magnetic nanoparticles has been issue of different potential applications including remotion oil from water. Magnetic polystyrene–palygorskite nanocomposites were prepared by a heterogeneous phase polymerization for the removal of organic contaminants from water. The organo‐Fe₃O₄‐palygorskite nanoparticles were coated with polystyrene, forming water repellent and oil absorbing surfaces to promote the removal of oil from the surfaces of nanocomposites by applying an external magnetic field. X‐ray fluorescence, X‐ray diffraction, scanning electron microscopy, zeta potential and size distribution measurement, surface area determination by BET, density measurement by He pycnometry, carbon grade determination, thermogravimetric analysis, Fourier‐transform infrared spectroscopy, Raman spectroscopy, and evaluation of hydrophobicity by contact angle were used to characterize the nanoparticles. The magnetic nanocomposite obtained showed excellent hydrophobicity, around 78° contact angle. In addition, oil removal capability tests were also performed, according to which the preliminary results indicated removal of approximately 98% of oil in synthetic oily water samples. The oil–water separation using this magnetic nanocomposite provides a promising alternative strategy for water treatment. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46162.