Determination of fat-soluble vitamins in vegetable oils through microwave-assisted high-performance liquid chromatography

In this manuscript, a study of the effect of microwave radiation on the high-performance liquid chromatography separation of tocopherols and vitamin K1 was conducted. The novelty of the application was the use of a relatively low polarity mobile phase in which the dielectric heating effect was minimized to evaluate the nonthermal effect of the microwave radiation over the separation process. Results obtained show that microwave-assisted high-performance liquid chromatography had a shorter analysis time from 31.5 to 13.3 min when the lowest microwave power was used. Moreover, narrower peaks were obtained; hence the separation was more efficient maintaining or even increasing the resolution between the peaks. This result confirms that the increase in mobile phase temperature is not the only variable for improving the separation process but also other nonthermal processes must intervene. Fluorescence detection demonstrated better signal-to-noise compared to photodiode arrayed detection mainly due to the independent effect of microwave pulses on the baseline noise, but photodiode array detection was finally chosen as it allowed a simultaneous detection of nonfluorescent compounds. Finally, a determination of the content of the vitamin E homologs was carried out in different vegetable oils. Results were coherent with those found in the literature.

High performance of Cu/CeO2-Nb2O5 catalysts for preferential CO oxidation and total combustion of toluene

Copper-based catalysts supported on niobium-doped ceria have been prepared and tested in the preferential oxidation of CO in excess of H2 (PROX) and in total oxidation of toluene. Supports and catalysts have been characterized by several techniques: N2 adsorption, ICP-OES, XRF, XRD, Raman Spectroscopy, SEM, TEM, H2-TPR and XPS, and their catalytic performance has been measured in PROX, with an ideal gas mixture (CO, O2 and H2) with or without CO2 and H2O, and in total oxidation of toluene. The effects of the copper loading and the amount of niobium in the supports have been evaluated. Remarkably, the addition of niobia to the catalysts may improve the catalytic performance in total oxidation of toluene. It allows us to prepare cheaper catalysts (niobia it is far cheaper than ceria) with improved catalytic performance.

Effect of Cyclic Topology on Charge-Transfer Properties of Organic Molecular Semiconductors: The Case of Cycloparaphenylene Molecules

We have studied the role played by cyclic topology on charge-transfer properties of recently synthesized π -conjugated molecules, namely the set of [n]cycloparaphenylene compounds, with n the number of phenylene rings forming the curved nanoring. We estimate the charge-transfer rates for holes and electrons migration within the array of molecules in their crystalline state. The theoretical calculations suggest that increasing the size of the system would help to obtain higher hole and electron charge-transfer rates and that these materials might show an ambipolar behavior in real samples, independently of the different mode of packing followed by the [6]cycloparaphenylene and [12]cycloparaphenylene cases studied.

Interfaces for Intelligent Environments

Presentaciones de la asignatura Interfaces para Entornos Inteligentes del Máster en Tecnologías de la Informática/Machine Learning and Data Mining.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.