Simón Bolívar University

About: Simón Bolívar University is a education organization based out in Caracas, Venezuela. It is known for research contribution in the topics: Population & Crystallization. The organization has 5912 authors who have published 8294 publications receiving 126152 citations.

Bionanocomposite Films Prepared from Corn Starch With and Without Nanopackaged Jamaica ( Hibiscus sabdariffa ) Flower Extract

Abstract: Active and pH-sensitive nano-fillers were prepared from natural and modified montmorillonite (Mnt) and nanopackaged with anthocyanins extracted from the Jamaica (Hibiscus sabdariffa) flower. These were then used to reinforce corn (Zea mays) starch-based films plasticized with glycerol, and processed by extrusion and thermo-molding. Seven film systems were investigated for their potential as “active and intelligent” (A&I) bionanocomposite films with improved properties. The thermal and mechanical properties of the bionanocomposite films obtained were enhanced largely due to the added modified clay nano-fillers, and the nanopackaging of the anthocyanins between the nano-clay layers. Unfortunately, however, the bionanocomposite films failed as A&I materials, despite the supposed effect of the nano-clays as protective nano-encapsulating materials for the active and pH-sensitive compound (anthocyanins). The results obtained suggest that the exfoliation of the nano-fillers as a consequence of the shear forces inside the extruder led to the exposure of the anthocyanins during extrusion. Because of this, we consider the large-scale development of A&I biodegradable films incorporating natural pigments very unlikely being processed by extrusion/thermo-molding, since there are several significant processes involved in the techniques currently available in the food and polymer industries that leave the active and pH-sensitive compounds unprotected.

Atrial activity enhancement by Wiener filtering using an artificial neural network

Abstract: Describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with 9 input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.

Remote sensing characterization of benthic habitats and submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela

Abstract: Remote sensing is a useful tool for characterizing submerged aquatic vegetation and other benthic habitats in shallow water areas with clear water transparency. In the present study, the visible bands of the Thematic Mapper (TM) sensor aboard Landsat 7 satellite were used in a supervised classification of benthic habitats and for the assessment of submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela. Initially, the TM visible bands were log‐transformed and linearly combined to reduce the depth‐dependent variance in the bottom reflectance signal. The supervised classification had an overall accuracy of 74%. Eight bottom types could be spectrally separated: sand, dispersed communities over sand (shallow and deep), dense seagrass, dispersed seagrass meadows over sand, reef communities, mixed vegetation over muddy bottom, and lagoons. Regression analyses were performed between the depth‐invariant band combinations and field samples of vegetation biomass. The regression using the TM ...