Phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters

Analog Integrated Circuits and Signal Processing - This paper presents the complete design of a phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters. The...     

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.     

Reference classes: a tool for benchmarking universities’ research

Based on new comparison principles that take into account both the volume of scientific production and its impact, this paper proposes a method for defining reference classes of universities. Several tools are developed in order to enable university managers to define the value system according to which their university shall be compared to others. We apply this methodology to French universities and illustrate it using the reference classes of the best ranked universities according to several value systems.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...     

Plant oil bioconversion into increase biological activity through lipases derived from wastes

The aim of this study was to show that abundant and inexpensive plant oils can be biotransformed to increase biological activity (antioxidant, antimicrobial and cytotoxicity) through hydrolysis reaction catalysed by lipases. We tested homemade and commercial lipases through the biotransformation of nine different plant oils in forty different combinations. First, the chemical composition of the samples was investigated. Thereafter, biological tests were conducted to assess the antioxidant and antimicrobial activity of the sampled biotransformation products, as well as analyzed their influence on the viability of healthy and cancer cells. Summarising, sunflower, corn and olive oils modified by orange waste-obtained lipases presented the most promising results, reaching up to 90% of antioxidant activity increase and significant growth inhibition of bacteria colonies belonging to genera Escherichia, Listeria, Staphylococcus, Pseudomonas and Salmonella. In addition, those compounds affected human oral squamous carcinoma cells. The bioconversion of plant oils through lipases improves their biological properties and might be an option for biotechnological application.