Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general. (C) 2011 Elsevier B.V. All rights reserved.

Magnetic non-destructive evaluation of ruptures of tensile armor in oil risers

Risers are flexible multilayered pipes formed by an inner flexible metal structure surrounded by polymer layers and spiral wound steel ligaments, also known as armor wires. Since these risers are used to link subsea pipelines to floating oil and gas production installations, and their failure could produce catastrophic consequences, some methods have been proposed to monitor the armor integrity. However, until now there is no practical method that allows the automatic non-destructive detection of individual armor wire rupture. In this work we show a method using magnetic Barkhausen noise that has shown high efficiency in the detection of armor wire rupture. The results are examined under the cyclic and static load conditions of the riser. This work also analyzes the theory behind the singular dependence of the magnetic Barkhausen noise on the applied tension in riser armor wires.

Comparative in vitro evaluation of forage legumes (prosopis, acacia, atriplex, and leucaena) on ruminal fermentation and methanogenesis

Two experiments in vitro were conducted to evaluate four Egyptian forage legume browses, i.e., leaves of prosopis (Prosopis juliflora), acacia (Acacia saligna), atriplex (A triplex halimus), and leucaena (Leucaena leucocephala), in comparison with Tifton (Cynodon sp.) grass hay for their gas production, methanogenic potential, and ruminal fermentation using a semi-automatic system for gas production (first experiment) and for ruminal and post ruminal protein degradability (second experiment). Acacia and leucaena showed pronounced methane inhibition compared with Tifton, while prosopis and leucaena decreased the acetate:propionate ratio (P<0.01). Acacia and leucaena presented a lower (P<0.01) ruminal NH3-N concentration associated with the decreasing (P<0.01) ruminal protein degradability. Leucaena, however, showed higher (P<0.01) intestinal protein digestibility than acacia. This study suggests that the potential methanogenic properties of leguminous browses may be related not only to tannin content, but also to other factors.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.