Evaluation of the kinetics of xylose formation from dilute sulfuric acid hydrolysis of forest residues of Eucalyptus grandis

Dilute acid hydrolysis studies were performed on forest residues of Eucalyptus grandis, in a cylindrical reactor of stainless steel. The kinetics of this hydrolysis reaction was investigated employing 0.65% sulfuric acid, a residue/acid solution ratio of 1/9 (w/w), temperatures of 130, 140, 150, and 160 degrees C, and reaction times in the range 20-100 min. The results showed that, under the optimized conditions of acid hydrolysis employed in this study, the variables temperature and reaction time had a strong influence on hemicellulose removal and a small influence on the degree of lignin and cellulose removal. The highest xylose extraction yield was 87.6% attained at 160 degrees C, after 70 min reaction time, simultaneously with the formation of decomposition products, namely 2.8% acetic acid, 0.6% furfural, and 0.06% 5-hydroxymethylfurfural. A similar xylose extraction yield (82.8%) was observed at 150 degrees C after 100 min, with the formation of 3.2% acetic acid, 1.0% furfural, and 0.07% 5-hydroxymethylfurfural. The kinetic parameters determined at 130, 140, 150, and 160 degrees C for degradation of xylan present in the hemicellulose of the eucalyptus forest residue during the formation of xylose were the first-order reaction rate constants (k) for each temperature, 1.22 x 10(-4), 2.12 x 10(-4), 5.43 x 10(-4), and 9.05 x 10(-4) s(-1), respectively, and an activation energy (E-a) of 101.3 kJ mol(-1).

Soil seed banks in tropical forest fragments with different disturbance histories in southeastern Brazil

Soil seed banks are considered an important mechanism for natural regeneration in tropical forest ecosystems. This paper investigated the soil seed bank in two semideciduous seasonal tropical forest fragments with different disturbance histories in Botucatu, southeastern Brazil. In each study site, 40 superficial soil samples (30 cm × 30 cm × 5 cm) were taken at the end of both the dry and rainy seasons. The seeds were estimated by the germination method. Average soil seed density was 588.6 and 800.3 seeds m-2, respectively, for site 1 (less disturbed) and site 2 (more disturbed). Seed density and diversity (H′) were significantly higher in site 2 in both seasons. Non-woody taxa predominated in both fragments, but pioneer tree species were better represented in the less disturbed forest. Both ecosystems have a potential for regeneration from soil seed banks, but this potential is higher in the less disturbed site. Low richness and density of pioneer tree species in the seed bank indicate that the ecosystem has lost its resilience. The seed bank is not as important in these ecosystems as in other forests. Results indicate that management strategies to restore these forests should take into account the possibility of recovering soil seed bank processes and dynamics. © 2007 Elsevier B.V. All rights reserved.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Automatic classification of fish germ cells through optimum-path forest

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.

Fast robot voice interface through optimum-path forest

Voice-based user interfaces have been actively pursued aiming to help individuals with motor impairments, providing natural interfaces to communicate with machines. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for voice-based robot interface, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster. Experiments were conducted against Support Vector Machines, Neural Networks and a Bayesian classifier to show the OPF robustness. The proposed architecture provides high accuracy rates allied with low computational times. © 2012 IEEE.

Fast fault diagnosis in power transformers using optimum-path forest

In this paper we propose a fast and an accurate method for fault diagnosis in power transformers by means of Optimum-Path Forest (OPF) classifier. Since we applied Dissolved Gas Analysis (DGA), the samples have been labeled by IEEE/IEC standard, which was further analyzed by OPF and several other well known supervised pattern recognition techniques. The experiments have showed that OPF can achieve high recognition rates with low computational cost. © 2012 IEEE.

Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Diversity and Physiological Characterization of D-Xylose-Fermenting Yeasts Isolated from the Brazilian Amazonian Forest

Background: This study is the first to investigate the Brazilian Amazonian Forest to identify new D-xylose-fermenting yeasts that might potentially be used in the production of ethanol from sugarcane bagasse hemicellulosic hydrolysates. Methodology/Principal Findings: A total of 224 yeast strains were isolated from rotting wood samples collected in two Amazonian forest reserve sites. These samples were cultured in yeast nitrogen base (YNB)-D-xylose or YNB-xylan media. Candida tropicalis, Asterotremella humicola, Candida boidinii and Debaryomyces hansenii were the most frequently isolated yeasts. Among D-xylose-fermenting yeasts, six strains of Spathaspora passalidarum, two of Scheffersomyces stipitis, and representatives of five new species were identified. The new species included Candida amazonensis of the Scheffersomyces clade and Spathaspora sp. 1, Spathaspora sp. 2, Spathaspora sp. 3, and Candida sp. 1 of the Spathaspora clade. In fermentation assays using D-xylose (50 g/L) culture medium, S. passalidarum strains showed the highest ethanol yields (0.31 g/g to 0.37 g/g) and productivities (0.62 g/L.h to 0.75 g/L.h). Candida amazonensis exhibited a virtually complete D-xylose consumption and the highest xylitol yields (0.55 g/g to 0.59 g/g), with concentrations up to 25.2 g/L. The new Spathaspora species produced ethanol and/or xylitol in different concentrations as the main fermentation products. In sugarcane bagasse hemicellulosic fermentation assays, S. stipitis UFMG-XMD-15.2 generated the highest ethanol yield (0.34 g/g) and productivity (0.2 g/L.h), while the new species Spathaspora sp. 1 UFMG-XMD-16.2 and Spathaspora sp. 2 UFMG-XMD-23.2 were very good xylitol producers. Conclusions/Significance: This study demonstrates the promise of using new D-xylose-fermenting yeast strains from the Brazilian Amazonian Forest for ethanol or xylitol production from sugarcane bagasse hemicellulosic hydrolysates.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Clasificación de una imagen de alta resolución "Quickbird" con la técnica de análisis de imágenes en base a objetos