Near infrared spectroscopy for soil bulk density assessment

P>Soil bulk density values are needed to convert organic carbon content to mass of organic carbon per unit area. However, field sampling and measurement of soil bulk density are labour-intensive, costly and tedious. Near-infrared reflectance spectroscopy (NIRS) is a physically non-destructive, rapid, reproducible and low-cost method that characterizes materials according to their reflectance in the near-infrared spectral region. The aim of this paper was to investigate the ability of NIRS to predict soil bulk density and to compare its performance with published pedotransfer functions. The study was carried out on a dataset of 1184 soil samples originating from a reforestation area in the Brazilian Amazon basin, and conventional soil bulk density values were obtained with metallic ""core cylinders"". The results indicate that the modified partial least squares regression used on spectral data is an alternative method for soil bulk density predictions to the published pedotransfer functions tested in this study. The NIRS method presented the closest-to-zero accuracy error (-0.002 g cm-3) and the lowest prediction error (0.13 g cm-3) and the coefficient of variation of the validation sets ranged from 8.1 to 8.9% of the mean reference values. Nevertheless, further research is required to assess the limits and specificities of the NIRS method, but it may have advantages for soil bulk density predictions, especially in environments such as the Amazon forest.

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance. but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation. (C) 2009 Elsevier B.V. All rights reserved.

Sequential injections as an alternative to gradient exploitation for implementing differential kinetic analysis in a flow injection system

A novel flow-based strategy for implementing simultaneous determinations of different chemical species reacting with the same reagent(s) at different rates is proposed and applied to the spectrophotometric catalytic determination of iron and vanadium in Fe-V alloys. The method relies on the influence of Fe(II) and V(IV) on the rate of the iodide oxidation by Cr(VI) under acidic conditions, the Jones reducing agent is then needed Three different plugs of the sample are sequentially inserted into an acidic KI reagent carrier stream, and a confluent Cr(VI) solution is added downstream Overlap between the inserted plugs leads to a complex sample zone with several regions of maximal and minimal absorbance values. Measurements performed on these regions reveal the different degrees of reaction development and tend to be more precise Data are treated by multivariate calibration involving the PLS algorithm The proposed system is very simple and rugged Two latent variables carried out ca 95% of the analytical information and the results are in agreement with ICP-OES. (C) 2010 Elsevier B V. All rights reserved.

Optimization and validation of a LIBS method for the determination of macro and micronutrients in sugar cane leaves

Laser induced breakdown spectrometry (LIBS) was applied for the determination of macro (P, K, Ca, Mg) and micronutrients (B, Cu, Fe, Mn and Zn) in sugar cane leaves, which is one of the most economically important crops in Brazil. Operational conditions were previously optimized by a neuro-genetic approach, by using a laser Nd:YAG at 1064 nm with 110 mJ per pulse focused on a pellet surface prepared with ground plant samples. Emission intensities were measured after 2.0 mu s delay time, with 4.5 mu s integration time gate and 25 accumulated laser pulses. Measurements of LIBS spectra were based on triplicate and each replicate consisted of an average of ten spectra collected in different sites (craters) of the pellet. Quantitative determinations were carried out by using univariate calibration and chemometric methods, such as PLSR and iPLS. The calibration models were obtained by using 26 laboratory samples and the validation was carried out by using 15 test samples. For comparative purpose, these samples were also microwave-assisted digested and further analyzed by ICP OES. In general, most results obtained by LIBS did not differ significantly from ICP OES data by applying a t-test at 95% confidence level. Both LIBS multivariate and univariate calibration methods produced similar results, except for Fe where better results were achieved by the multivariate approach. Repeatability precision varied from 0.7 to 15% and 1.3 to 20% from measurements obtained by multivariate and univariate calibration, respectively. It is demonstrated that LIBS is a powerful tool for analysis of pellets of plant materials for determination of macro and micronutrients by choosing calibration and validation samples with similar matrix composition.

Partial microwave-assisted wet digestion of animal tissue using a baby-bottle sterilizer for analyte determination by inductively coupled plasma optical emission spectrometry

A procedure for partial digestion of bovine tissue is proposed using polytetrafluoroethylene (PTFE) microvessels inside a baby-bottle sterilizer under microwave radiation for multi-element determination by inductively coupled plasma optical emission spectrometry (ICP OES). Samples were directly weighed in laboratory-made polytetrafluoroethylene vessels. Nitric acid and hydrogen peroxide were added to the uncovered vessels, which were positioned inside the baby-bottle sterilizer, containing 500 mL of water. The hydrogen peroxide volume was fixed at 100 mu L The system was placed in a domestic microwave oven and partial digestion was carried out for the determination of Ca, Cu, Fe. Mg, Mn and Zn by inductively coupled plasma optical emission spectrometry. The single-vessel approach was used in the entire procedure, to minimize contamination in trace analysis. Better recoveries and lower residual carbon content (RCC) levels were obtained under the conditions established through a 2(4-1) fractional factorial design: 650 W microwave power, 7 min digestion time, 50 mu L nitric acid and 50 mg sample mass. The digestion efficiency was ascertained according to the residual carbon content determined by inductively coupled plasma optical emission spectrometry. The accuracy of the proposed procedure was checked against two certified reference materials. (C) 2009 Elsevier B.V. All rights reserved.

Effects of Varenicline in Adult Smokers: A Multinational, 24-Week, Randomized, Double-Blind, Placebo-Controlled Study

Background: Prevalence rates of smoking are rising in developing countries. Previous trials evaluating the efficacy and tolerability of the smoking-cessation medication varenicline have used largely participants of Caucasian origin. Objective: This study was conducted to evaluate the efficacy and tolerability of varenicline in populations of participants from Latin America, Africa, and the Middle East to investigate potential differences in the therapeutic response to varenicline. Methods: This multinational, randomized, double-blind, placebo-controlled trial was conducted at 42 centers in 11 countries (Latin America: Brazil, Colombia, Costa Rica, Mexico, and Venezuela; Africa: Egypt and South Africa; Middle East: Jordan, Lebanon, Saudi Arabia, and the United Arab Emirates). Participants were male and female smokers aged 18 to 75 years who were motivated to stop smoking; smoked >= 10 cigarettes/d, with no cumulative period of abstinence >3 months in the previous year; and who had no serious or unstable disease within the previous 6 months. Subjects were randomized in a 2:1 ratio to receive varenicline 1 mg or placebo, BID for 12 weeks, with a 12-week nontreatment follow-up. Brief smoking-cessation counseling was provided. The main outcome measures were carbon monoxide confirmed continuous abstinence rate (CAR) at weeks 9 to 12 and weeks 9 to 24. Adverse events (AEs) were recorded for tolerability assessment. Results: Overall, 588 subjects (varenicline, 390; placebo, 198) were randomized and treated. The mean (SD) ages of subjects in the varenicline and placebo groups were 43.1 (10.8) and 43.9 (10.8) years, respectively; 57.7% and 65.7% were male; and the mean (SD) weights were 75.0 (16.0) and 76.7 (16.3) kg (range, 40.0-130.0 and 45.6-126.0 kg). CAR at weeks 9 to 12 was significantly higher with varenicline than with placebo (53.59% vs 18.69%; odds ratio [OR] = 5.76; 95% CI, 3.74-8.88; P < 0.0001), and this rate was maintained during weeks 9 to 24 (39.74% vs 13.13%; OR = 4.78; 95% CI, 2.97-7.68; P < 0.0001). Nausea, headache, and insomnia were the most commonly reported AEs with varenicline and were reported numerically more frequently in the varenicline group compared with the placebo group. Serious AEs (SAEs) were reported in 2.8% of varenicline recipients compared with 1.0% in the placebo group, with 6 subjects reporting psychiatric SAEs compared with none in the placebo group. Conclusion: Based on these data, varenicline was apparently efficacious and generally well tolerated as a smoking-cessation aid in smokers from selected sites in Latin America, Africa, and the Middle East. ClinicalTrials. gov identifier: NCT00594204. (Clin Ther. 2011;33:465-477) (C) 2011 Published by Elsevier HS Journals, Inc.

Equivalence between hot tensile and creep testing data for Kanthal A1 alloy

Hot tensile and creep tests were carried out on Kanthal A1 alloy in the temperature range from 600 to 800 degrees C. Each of these sets of data were analyzed separately according to their own methodologies, but an attempt was made to find a correlation between them. A new criterion proposed for converting hot tensile data to creep data, makes possible the analysis of the two kinds of results according to usual creep relations like: Norton, Monkman-Grant, Larson-Miller and others. The remarkable compatibility verified between both sets of data by this procedure strongly suggests that hot tensile data can be converted to creep data and vice-versa for Kanthal A1 alloy, as verified previously for other metallic materials.

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.

Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp RFLP/PCR data set

The taxonomy of the N(2)-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradryrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses Clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster. (C) 2008 Elsevier B.V. All rights reserved.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Determination of in situ uniaxial compressive strength of coal seams based on geophysical data

The central issue for pillar design in underground coal mining is the in situ uniaxial compressive strength (sigma (cm)). The paper proposes a new method for estimating in situ uniaxial compressive strength in coal seams based on laboratory strength and P wave propagation velocity. It describes the collection of samples in the Bonito coal seam, Fontanella Mine, southern Brazil, the techniques used for the structural mapping of the coal seam and determination of seismic wave propagation velocity as well as the laboratory procedures used to determine the strength and ultrasonic wave velocity. The results obtained using the new methodology are compared with those from seven other techniques for estimating in situ rock mass uniaxial compressive strength.

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Modeling and experimental study of nucleate boiling on a vertical array of horizontal plain tubes

An investigation of nucleate boiling on a vertical array of horizontal plain tubes is presented in this paper. Experiments were performed with refrigerant RI 23 at reduced pressures varying from 0.022 to 0.64, tube pitch to diameter ratios of 1.32, 1.53 and 2.00, and heat fluxes from 0.5 to 40 kW/m(2). Brass tubes with external diameters of 19.05 mm and average roughness of 0.12 mu m were used in the experiments. The effect of the tube spacing on the local heat transfer coefficient along the tube array was negligible within the present range of experimental conditions. For partial nucleate boiling, characterized by low heat fluxes, and low reduced pressures, the tube positioning shows a remarkable effect on the heat transfer coefficient. Based on these data, a general correlation for the prediction of the nucleate boiling heat transfer coefficient on a vertical array of horizontal tubes under flooded conditions was proposed. According to this correlation, the ratio between the heat transfer coefficients of a given tube and the lowest tube in the array depends only on the tube row number, the reduced pressure and the heat flux. By using the proposed correlation, most of the experimental heat transfer coefficients obtained in the present study were predicted within +/- 15%. The new correlation compares reasonably well with independent data from the literature. (C) 2008 Elsevier Inc. All rights reserved.

Short-term experimental data of drying shrinkage of ground granulated blast-furnace slag cement concrete

This paper presents results of laboratory testing of unrestrained drying shrinkage during a period of 154 days of different concrete mixtures from the Brazilian production line that utilize ground granulated blast-furnace slag in their compositions. Three concrete mixtures with water/cement ratio of 0.78(M1), 0.41(M2), and 0.37(M3) were studied. The obtained experimental data were compared with the analytical results from prediction models available in the literature: the ACI 209 model (ACI), the B3 model (B3), the Eurocode 2 model (EC2), the GL 2000 model (GL), and the Brazilian NBR 6118 model (NBR), and an analysis of the efficacy of these models was conducted utilizing these experimental data. In addition, the development of the mechanical properties (compressive strength and modulus of elasticity) of the studied concrete mixtures was also measured in the laboratory until 126 days. From this study, it could be concluded that the ACI and the GL were the models that most approximated the experimental drying shrinkage data measured during the analyzed period of time.

Approximate algorithms for credal networks with binary variables

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. (C) 2007 Elsevier Inc. All rights reserved.