Implications of long-term land-use change for the hydrology and solute budgets of small catchments in Amazonia

The replacement of undisturbed tropical forest with cattle pasture has the potential to greatly modify the hydrology of small watersheds and the fluxes of solutes. We examined the fluxes of water, Cl(-), NO(3)(-)-N: SO(4)(2--)-S, NH(4)(+)-N, Na(+), K(+), Mg(2+) and Ca(2+) in different flow paths in similar to 1 ha catchments of undisturbed open tropical rainforest and a 20 year-old pasture established from forest in the southwestern Brazilian Amazon state of Rondonia. Storm flow discharge was 18% of incident rainfall in pasture, but only 1% in forest. Quickflow predominated over baseflow in both catchments and in both wet and dry seasons. In the pasture, groundwater and quickflow were important flow paths for the export of all solutes. In the forest, quickflow was important for NO(3)(-)-N export, but all other solutes were exported primarily by groundwater outflow. Both catchments were sinks for SO(4)(2-)-S and Ca(2+), and sources of Na(+). The pasture catchment also lost K(+) and Mg(2+) because of higher overland flow frequency and volume and to cattle excrement. These results show that forest clearing dramatically influences small watershed hydrology by increasing quickflow and water export to streams. They also indicate that tropical forest watersheds are highly conservative for most solutes but that pastures continue to lose important cations even decades after deforestation and pasture establishment. (c) 2008 Elsevier B.V. All rights reserved.

Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km(2) were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km(2). We observe an increase of 2000 km(2) of agricultural intensification, where areas of single crops were converted to double crops during the study period. (C) 2007 Elsevier Inc. All rights reserved.

Discharge-calcium concentration relationships in streams of the Amazon and Cerrado of Brazil: soil or land use controlled

Stream discharge-concentration relationships are indicators of terrestrial ecosystem function. Throughout the Amazon and Cerrado regions of Brazil rapid changes in land use and land cover may be altering these hydrochemical relationships. The current analysis focuses on factors controlling the discharge-calcium (Ca) concentration relationship since previous research in these regions has demonstrated both positive and negative slopes in linear log(10)discharge-log(10)Ca concentration regressions. The objective of the current study was to evaluate factors controlling stream discharge-Ca concentration relationships including year, season, stream order, vegetation cover, land use, and soil classification. It was hypothesized that land use and soil class are the most critical attributes controlling discharge-Ca concentration relationships. A multilevel, linear regression approach was utilized with data from 28 streams throughout Brazil. These streams come from three distinct regions and varied broadly in watershed size (< 1 to > 10(6) ha) and discharge (10(-5.7)-10(3.2) m(3) s(-1)). Linear regressions of log(10)Ca versus log(10)discharge in 13 streams have a preponderance of negative slopes with only two streams having significant positive slopes. An ANOVA decomposition suggests the effect of discharge on Ca concentration is large but variable. Vegetation cover, which incorporates aspects of land use, explains the largest proportion of the variance in the effect of discharge on Ca followed by season and year. In contrast, stream order, land use, and soil class explain most of the variation in stream Ca concentration. In the current data set, soil class, which is related to lithology, has an important effect on Ca concentration but land use, likely through its effect on runoff concentration and hydrology, has a greater effect on discharge-concentration relationships.

Runoff sources and land cover change in the Amazon: an end-member mixing analysis from small watersheds

The flowpaths by which water moves from watersheds to streams has important consequences for the runoff dynamics and biogeochemistry of surface waters in the Amazon Basin. The clearing of Amazon forest to cattle pasture has the potential to change runoff sources to streams by shifting runoff to more surficial flow pathways. We applied end-member mixing analysis (EMMA) to 10 small watersheds throughout the Amazon in which solute composition of streamwater and groundwater, overland flow, soil solution, throughfall and rainwater were measured, largely as part of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia. We found a range in the extent to which streamwater samples fell within the mixing space determined by potential flowpath end-members, suggesting that some water sources to streams were not sampled. The contribution of overland flow as a source of stream flow was greater in pasture watersheds than in forest watersheds of comparable size. Increases in overland flow contribution to pasture streams ranged in some cases from 0% in forest to 27-28% in pasture and were broadly consistent with results from hydrometric sampling of Amazon forest and pasture watersheds that indicate 17- to 18-fold increase in the overland flow contribution to stream flow in pastures. In forest, overland flow was an important contribution to stream flow (45-57%) in ephemeral streams where flows were dominated by stormflow. Overland flow contribution to stream flow decreased in importance with increasing watershed area, from 21 to 57% in forest and 60-89% in pasture watersheds of less than 10 ha to 0% in forest and 27-28% in pastures in watersheds greater than 100 ha. Soil solution contributions to stream flow were similar across watershed area and groundwater inputs generally increased in proportion to decreases in overland flow. Application of EMMA across multiple watersheds indicated patterns across gradients of stream size and land cover that were consistent with patterns determined by detailed hydrometric sampling.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Multifractal regime transition in a modified minority game model

The search for more realistic modeling of financial time series reveals several stylized facts of real markets. In this work we focus on the multifractal properties found in price and index signals. Although the usual minority game (MG) models do not exhibit multifractality, we study here one of its variants that does. We show that the nonsynchronous MG models in the nonergodic phase is multifractal and in this sense, together with other stylized facts, constitute a better modeling tool. Using the structure function (SF) approach we detected the stationary and the scaling range of the time series generated by the MG model and, from the linear (non-linear) behavior of the SF we identified the fractal (multifractal) regimes. Finally, using the wavelet transform modulus maxima (WTMM) technique we obtained its multifractal spectrum width for different dynamical regimes. (C) 2009 Elsevier Ltd. All rights reserved.

Multifractality in the random parameter model for multivariate time series

The Random Parameter model was proposed to explain the structure of the covariance matrix in problems where most, but not all, of the eigenvalues of the covariance matrix can be explained by Random Matrix Theory. In this article, we explore the scaling properties of the model, as observed in the multifractal structure of the simulated time series. We use the Wavelet Transform Modulus Maxima technique to obtain the multifractal spectrum dependence with the parameters of the model. The model shows a scaling structure compatible with the stylized facts for a reasonable choice of the parameter values. (C) 2009 Elsevier B.V. All rights reserved.

Coconut coir as biosorbent for Cr(VI) removal from laboratory wastewater

A high cost-effective treatment of sulphochromic waste is proposed employing a raw coconut coir as biosorbent for Cr(VI) removal. The ideal pH and sorption kinetic, sorption capacities, and sorption sites were the studied biosorbent parameters. After testing five different isotherm models with standard solutions, Redlich-Peterson and Toth best fitted the experimental data, obtaining a theoretical Cr(VI) sorption capacity (SC) of 6.3 mg g(-1). Acid-base potentiometric titration indicated around of 73% of sorption sites were from phenolic compounds, probably lignin. Differences between sorption sites in the coconut coir before and after Cr adsorption identified from Fourier transform infrared spectra suggested a modification of sorption sites after sulphochromic waste treatment, indicating that the sorption mechanism involves organic matter oxidation and chromium uptake. For sulphocromic waste treatment, the SC was improved to 26.8 +/- 0.2 mg g(-1), and no adsorbed Cr(VI) was reduced, remaining only Cr(III) in the final solution. The adsorbed material was calcinated to obtain Cr2O3, with a reduction of more than 60% of the original mass. (c) 2008 Elsevier B.V. All rights reserved.

An alternative application to the Portuguese agro-industrial residue: Wheat straw

The effects of alkaline treatments of the wheat straw with sodium hydroxide were investigated. The optimal condition for extraction of hemicelluloses was found to be with 0.50 mol/l sodium hydroxide at 55C for 2 h. This resulted in the release of 17.3% of hemicellulose (% dry starting material), corresponding to the dissolution of 49.3% of the original hemicellulose. The yields were determined by gravimetric analysis and expressed as a proportion of the starting material. Chemical composition and physico-chemical properties of the samples of hemicelluloses were elucidated by a combination of sugar analyses, Fourier transform infrared (FTIR), and thermal analysis. The results showed that the treatments were very effective on the extraction of hemicelluloses from wheat straw and that the extraction intensity (expressed in terms of alkali concentration) had a great influence on the yield and chemical features of the hemicelluloses. The FTIR analysis revealed typical signal pattern for the hemicellulosic fraction in the 1,200-1,000 cm(-1) region. Bands between 1,166 and 1,000 cm(-1) are typical of xylans.

Synthesis of Nb(2)O(5)center dot nH(2)O nanoparticles by water-in-oil microemulsion

Hydrous niobium oxide (Nb(2)O(5)center dot nH(2)O) nanoparticles had been Successfully prepared by water-in-oil microemulsion. They were characterized by X-ray diffraction (XRD), thermal analysis (TG/DTG), Fourier transform infrared spectroscopy (FTIR), BET surface area measurement, transmission electron microscopy (TEM), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results showed that the nanoparticle was exactly Nb(2)O(5)center dot nH(2)O with spherical shape. Their BET surface area was 60 m(2) g(-1). XRD results showed that Nb(2)O(5)center dot nH(2)O nanoparticles with crystallite size in nanometer scale were formed. The crystallinity and crystallity size increased with increasing annealing temperature. TT-phase of Nb(2)O(5) was obtained when the sample is annealed at 550 degrees C. (C) 2009 Elsevier B.V. All rights reserved.

Cellulosic Films Obtained from the Treatment of Sugarcane Bagasse Fibers with N-methylmorpholine-N-oxide (NMMO)

Ethanol/water organosolv pulping was used to obtain sugarcane bagasse pulp that was bleached with sodium chlorite. This bleached pulp was used to obtain cellulosic films that were further evaluated by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). A good film formation was observed when temperature of 74 degrees C and baths of distilled water were used, which after FTIR, TGA, and SEM analysis indicated no significant difference between the reaction times. The results showed this to be an interesting and promising process, combining the prerequisites for a more efficient utilization of agro-industrial residues.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Digital frequency relaying based on the modified least mean square method

This research presents a method for frequency estimation in power systems using an adaptive filter based on the Least Mean Square Algorithm (LMS). In order to analyze a power system, three-phase voltages were converted into a complex signal applying the alpha beta-transform and the results were used in an adaptive filtering algorithm. Although the use of the complex LMS algorithm is described in the literature, this paper deals with some practical aspects of the algorithm implementation. In order to reduce computing time, a coefficient generator was implemented. For the algorithm validation, a computing simulation of a power system was carried Out using the ATP software. Many different situations were Simulated for the performance analysis of the proposed methodology. The results were compared to a commercial relay for validation, showing the advantages of the new method. (C) 2009 Elsevier Ltd. All rights reserved.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Classification of normal swallowing and oropharyngeal dysphagia using wavelet

Oropharyngeal dysphagia is characterized by any alteration in swallowing dynamics which may lead to malnutrition and aspiration pneumonia. Early diagnosis is crucial for the prognosis of patients with dysphagia, and the best method for swallowing dynamics assessment is swallowing videofluoroscopy, an exam performed with X-rays. Because it exposes patients to radiation, videofluoroscopy should not be performed frequently nor should it be prolonged. This study presents a non-invasive method for the pre-diagnosis of dysphagia based on the analysis of the swallowing acoustics, where the discrete wavelet transform plays an important role to increase sensitivity and specificity in the identification of dysphagic patients. (C) 2008 Elsevier Inc. All rights reserved.