Ozone as an Alternative for Disinfection of Explants during In Vitro Mass Plant Propagation

Ozone was used as an alternative for replacing sodium hypochlorite in the disinfection of yam nodal segments during the establishment stage for in vitro mass plant propagation. The purpose of this work was to evaluate the effect of ozone on disinfection of yam nodal segments in gaseous and aqueous phase in comparison to sodium hypochlorite. The immersion of yam nodal segments in water containing dissolved ozone with an ozone concentration of 1.0, 1.5 and 2.0 ppm during for 5 to 10 min gave the best results; 100% of yam nodal segments were free of microbial contamination. In terms of sprouting initiation (7.5 days), sprouting percentage (88%), and sprouts growth (4.81 cm), all were improved in comparison to the conventionally used sodium hypochlorite.     

Thiophene‐ and silarylene‐containing polyesters. Resonance effect on conductivity after polarization in an external electric field

Polyesters were synthesized by direct polycondensation of thiophene‐2,5‐dicarboxylic acid and five different silarylene‐containing diphenols using a tosyl chloride/pyridine/N,N‐dimethylformamide system as a condensing agent. Polymers were obtained in good yields and were characterized using Fourier transform infrared and NMR (¹H, ¹³C, 135‐DEPT and ²⁹Si) spectroscopy and elemental analysis. All polymers were completely soluble in aprotic organic polar solvents such as dimethylformamide, dimethylsulfoxide and N‐methyl‐2‐pyrrolidone. The range of effective mass of the polymers (m/z) was 1 × 10⁵–2 × 10⁵, determined using electrospray ionization mass spectrometry. Asymmetry and steric hindrance prevented dense packing of the polymeric chains, showing glass transition temperatures between ? 78 and ? 51 °C and loss of thermal stability at 177–199 °C (10% weight loss). Additionally, the melting points of the polyesters were found to be in the range 62–67 °C. Because of this, the samples were semi‐solid at room temperature. The optical band gaps of the polymers were observed between 4.54 and 4.48 eV, corresponding in all cases to insulator behavior. The molecular structure of the samples was studied using X‐ray diffraction, showing a degree of order that was associated with two monoclinic lattices. Additionally, the conductivity was studied using a two‐point method with contacts on top of polymer films. Prior to the electrical measurement, the samples were polarized in an external electric field of 0.8 to 6.4 V cm^?1, and the alignment of the dipoles increased the electrical conductivity. Copyright © 2012 Society of Chemical Industry     

Hierarchical feature selection based on relative dependency for gear fault diagnosis

Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the entire set. Consequently, algorithms to identify these features are valuable contributions. This work deals with the feature selection problem through attribute clustering. The proposed algorithm is inspired by existing approaches, where the relative dependency between attributes is used to calculate dissimilarity values. The centroids of the created clusters are selected as representative attributes. The selection algorithm uses a random process for proposing centroid candidates, in this way, the inherent exploration in random search is included. A hierarchical procedure is proposed for implementing this algorithm. In each level of the hierarchy, the entire set of available attributes is split in disjoint sets and the selection process is applied on each subset. Once the significant attributes are proposed for each subset, a new set of available attributes is created and the selection process runs again in the next level. The hierarchical implementation aims to refine the search space in each level on a reduced set of selected attributes, while the computational time-consumption is improved also. The approach is tested with real data collected from a test bed, results show that the diagnosis precision by using a Random Forest based classifier is over 98 % with only 12 % of the attributes from the available set.     

An overview of the nutritional value of beef and lamb meat from South America

The southern region of South America, a subtropical and temperate zone, is an important region for the production of beef and lamb meat, which is mainly produced in extensive pasture-based systems. Because of its content in highly valuable nutrients such as iron, zinc, selenium, fatty acids, and vitamins, meat is a unique and necessary food for the human diet in order to secure a long and healthy life, without nutritional deficiencies. Beef and lamb production systems based on temperate or tropical grasslands show interesting and, in some cases, a differential content in minerals, fatty acids and vitamins. This review deals with the distinctive aspects of the nutritional quality of beef and lamb meat produced in this region in terms of nutritional composition and the bioavailability of key nutrients related to its contribution for a healthy diet for all ages.     

Modeling energy consumption for master–slave applications

With energy costs now accounting for nearly 30 % of a datacenter’s operating expenses, energy consumption has become an important issue when designing and executing a parallel algorithm. This paper analyzes the energy consumption of MPI applications following the master–slave paradigm. The analytical model is derived for this paradigm and is validated over a master–slave matrix-multiplication. This analytical model is parameterized through architectural and algorithmic parameters, and it is capable of predicting the energy consumption for a given instance of the problem over a given architecture. We use an external, metered, power distribution unit that allows to easily measure the power consumption of computing nodes without the needing of dedicated hardware.     

Utilization of ramon seeds (Brosimum alicastrum swarts) as a new source material for thermoplastic starch production

The development and characterization of biodegradable polymers deriving from renewable natural sources has attracted much attention. The aim of this work was to partially characterize a thermoplastic starch obtained from the starch of seeds from the ramon tree (TPS‐RS) as an option to substitute thermoplastic starch from corn (TPS‐CS), in some of its applications. At 55% of relative humidity (RH), TPS‐RS had higher tensile strength and deformation than TPS‐CS. X‐ray diffraction analysis showed similar values in residual crystallinity (percentage of crystallinity that remains after plasticization process) in both TPS. The SEM micrographs showed a few remnant granular structures in the TPS‐RS. The FTIR showed a greater intensity in band at 1016 cm^?1 in the TPS‐CS and TPS‐RS in comparison with their corresponding native starch, indicating an increase in the amorphous region after plasticization. The TGA analysis showed greater thermal stability in TPS‐CS (340 °C) compared with TPS‐RS (327 °C). In addition, the glass transition temperature in both TPS was 24 °C. The results obtained represent a starting point to potentialize the use of TPS‐RS instead of TPS‐CS for the development of new biodegradable materials for practical applications in different areas. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44235.