Perfect discrete Morse functions on 2-complexes

This paper is focused on the study of perfect discrete Morse functions on a 2-simplicial complex. These are those discrete Morse functions such that the number of critical i-simplices coincides with the ith Betti number of the complex. In particular, we establish conditions under which a 2-complex admits a perfect discrete Morse function and conversely, we get topological properties necessary for a 2-complex admitting such kind of functions. This approach is more general than the known results in the literature (Lewiner et al., 2003), since our study is not restricted to surfaces. These results can be considered as a first step in the study of perfect discrete Morse functions on 3-manifolds.     

Temperature and pump power dependent photoluminescence characterization of MBE grown GaAsBi on GaAs

Bulk and quantum well GaAs_1?xBi_x/GaAs layers with Bi mole fractions from 0.02 to 0.10 are grown by molecular-beam epitaxy at temperatures ranging from 280 to 320?°C. The samples are characterized using temperature and pump-power dependent photoluminescence measurements covering 8–300?K and 1–250?mW (7–1,800?W/cm²), respectively. The results indicate that there is strong reduction in bandgap energy with the incorporation of small amounts of Bi and that GaAsBi most likely forms a weak type-I band alignment with GaAs.     

Extraction and characterization of highly purified collagen from bovine pericardium for potential bioengineering applications

Bovine pericardium is widely used as a raw material in bioengineering as a source of collagen, a fundamental structural molecule. The physical, chemical, and biocompatibility characteristics of these natural fibers enable their broad use in several areas of the health sciences. For these applications, it is important to obtain collagen of the highest possible purity. The lack of a method to produce these pure biocompatible materials using simple and economically feasible techniques presents a major challenge to their production on an industrial scale. This study aimed to extract, purify, and characterize the type I collagen protein originating from bovine pericardium, considered to be an abundant tissue resource. The pericardium tissue was collected from male animals at slaughter age. Pieces of bovine pericardium were enzymatically digested, followed by a novel protocol developed for protein purification using ion-exchange chromatography. The material was extensively characterized by electrophoresis, scanning electron microscopy, energy dispersive X-ray spectroscopy, and infrared spectroscopy. The results showed a purified material with morphological properties and chemical functionalities compatible with type I collagen and similar to a highly purified commercial collagen. Thus, an innovative and relatively simple processing method was developed to extract and purify type I collagen from bovine tissue with potential applications as a biomaterial for regenerative tissue engineering.     

Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.     

Temporal contexts: Effective text classification in evolving document collections

The management of a huge and growing amount of information available nowadays makes Automatic Document Classification (ADC), besides crucial, a very challenging task. Furthermore, the dynamics inherent to classification problems, mainly on the Web, make this task even more challenging. Despite this fact, the actual impact of such temporal evolution on ADC is still poorly understood in the literature. In this context, this work concerns to evaluate, characterize and exploit the temporal evolution to improve ADC techniques. As first contribution we highlight the proposal of a pragmatical methodology for evaluating the temporal evolution in ADC domains. Through this methodology, we can identify measurable factors associated to ADC models degradation over time. Going a step further, based on such analyzes, we propose effective and efficient strategies to make current techniques more robust to natural shifts over time. We present a strategy, named temporal context selection, for selecting portions of the training set that minimize those factors. Our second contribution consists of proposing a general algorithm, called Chronos, for determining such contexts. By instantiating Chronos, we are able to reduce uncertainty and improve the overall classification accuracy. Empirical evaluations of heuristic instantiations of the algorithm, named WindowsChronos and FilterChronos, on two real document collections demonstrate the usefulness of our proposal. Comparing them against state-of-the-art ADC algorithms shows that selecting temporal contexts allows improvements on the classification accuracy up to 10%. Finally, we highlight the applicability and the generality of our proposal in practice, pointing out this study as a promising research direction.     

Wind speed forecasting using the NARX model,case: La Mata,Oaxaca, México

This study presents the generation of a nonlinear autoregressive exogenous model (NARX) for wind speed forecasting in a 1 h, in advance horizon. A sample of meteorological data of hourly measurements taken during a year was used for the generation of the model. The variables measured were as follows: wind speed, wind direction, solar radiation, pressure, and temperature. All measurements were taken by the Comision Federal de Electricidad (CFE) at La Mata, in the state of Oaxaca, Mexico. Using the Mahalanobis distance, the sample of data was treated in order to detect deviated values in multivariable samples. Later on, the statistical Granger test was conducted to establish the entry variables that would be incorporated into the model. Since solar radiation was the only one determined as the cause for wind speed, it was the variable used in the configuration of the model. To compare the NARX model, a one-variable, nonlinear autoregressive model (NAR) was also generated. Both models, the NARX and the NAR were compared against the persistence model by means of applying the statistical error forecast measurements of mean absolute error, mean squared error, and mean absolute percentage error to the test data. The results showed the NARX model as the most precise of the three, reflecting the importance of the inclusion of additional meteorological variables in the wind speed forecasting models.