Collimated scanning LS-INAA for testing trace elements homogeneity in Brazilian coffee beans

The degree of homogeneity is normally assessed by the variability of the results of independent analyses of several (e.g., 15) normal-scale replicates. Large sample instrumental neutron activation analysis (LS-INAA) with a collimated Ge detector allows inspecting the degree of homogeneity of the initial batch material, using a kilogram-size sample. The test is based on the spatial distributions of induced radioactivity. Such test was applied to samples of Brazilian whole (green) coffee beans (Coffea arabica and Coffea canephora) of approximately I kg in the frame of development of a coffee reference material. Results indicated that the material do not contain significant element composition inhomogeneities between batches of approximately 30-50 g, masses typically forming the starting base of a reference material.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

Evolutionary tuning of SVM parameter values in multiclass problems

Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved.

As vivências: questões de tradução, sentidos e fontes epistemológicas no legado de Vigotski

O objetivo deste artigo é abordar a questão das vivências na perspectiva de L.S. Vigotski, tratando de suas fontes epistemológicas, de suas raízes na língua russa e de seus sentidos específicos, priorizando "A Tragédia do Hamlet, Príncipe da Dinamarca" (1916) e os textos pedológicos dos anos 1930, com destaque para "A questão do meio na pedologia". Destacam-se as transformações das ideias do autor sobre vivências e sua contribuição metodológica à  análise da relação indivíduo-meio no desenvolvimento humano, articulada aos conceitos de consciência e sistema.

Leucine supplementation improves adiponectin and total cholesterol concentrations despite the lack of changes in adiposity or glucose homeostasis in rats previously exposed to a high-fat diet

Background: Studies suggest that leucine supplementation (LS) has a therapeutic potential to prevent obesity and to promote glucose homeostasis. Furthermore, regular physical exercise is a widely accepted strategy for body weight maintenance and also for the prevention of obesity. The aim of this study was to determine the effect of chronic LS alone or combined with endurance training (ET) as potential approaches for reversing the insulin resistance and obesity induced by a high-fat diet (HFD) in rats. Methods: Forty-seven rats were randomly divided into two groups. Animals were fed a control diet-low fat (n = 10) or HFD (n = 37). After 15 weeks on HFD, all rats received the control diet-low fat and were randomly divided according to treatment: reference (REF), LS, ET, and LS+ET (n = 7-8 rats per group). After 6 weeks of treatment, the animals were sacrificed and body composition, fat cell volume, and serum concentrations of total cholesterol, HDL-cholesterol, triacylglycerol, glucose, adiponectin, leptin and tumor necrosis factor-alpha (TNF-alpha) were analyzed. Results: At the end of the sixth week of treatment, there was no significant difference in body weight between the REF, LS, ET and LS+ET groups. However, ET increased lean body mass in rats (P = 0.019). In addition, ET was more effective than LS in reducing adiposity (P = 0.019), serum insulin (P = 0.022) and TNF-alpha (P = 0.044). Conversely, LS increased serum adiponectin (P = 0.021) levels and reduced serum total cholesterol concentration (P = 0.042). Conclusions: The results showed that LS had no beneficial effects on insulin sensitivity or adiposity in previously obese rats. On the other hand, LS was effective in increasing adiponectin levels and in reducing total cholesterol concentration.

Mn dimers on graphene nanoribbons: An ab initio study

We show, using ab initio density functional theory calculations, that Mn dimers adsorbed on graphene nanoribbons (Mn(2)/GNRs) present a magnetic bistability, as does the isolated Mn dimer. Our total energy results indicate that Mn dimers lying along the edge sites of zigzag GNRs represent the most likely configuration. We find that similar to the isolated Mn(2) molecule, the antiferromagnetic coupling represents the ground state for Mn(2)/GNR, and the spin density configuration of the GNR does not play an important role on the net magnetic moment of Mn(2), which makes GNRs an ideal substrate for adsorption of these molecules. The ground state and the excited state configuration of the Mn dimer, viz., low-spin (LS) and high-spin (HS), are maintained in the face of changes in the spin density configuration of the substrate. Here we find that the Mn(2)/GNR systems exhibit a LS <-> HS binary behavior, which can be considered as a useful property in the development of nanomemories based upon metallic clusters. (C) 2011 American Institute of Physics. [doi:10.1063/1.3553849]

Improving sample representativeness in environmental studies: a major component for the uncertainty budget

For environmental quality assessment, INAA has been applied for determining chemical elements in small (200 mg) and large (200 g) samples of leaves from 200 trees. By applying the Ingamells` constant, the expected percent standard deviation was estimated in 0.9-2.2% for 200 mg samples. Otherwise, for composite samples (200 g), expected standard deviation varied from 0.5 to 10% in spite of analytical uncertainties ranging from 2 to 30%. Results thereby suggested the expression of the degree of representativeness as a source of uncertainty, contributing for increasing of the reliability of environmental studies mainly in the case of composite samples.

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.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Impact on HDPE and PVC plates - Experimental tests and numerical simulations

This paper presents first material tests on HDPE and PVC, and subsequently impact tests on plates made of the same materials. Finally, numerical simulations of the plate impact tests are compared with the experimental results. A rather comprehensive series of mechanical material tests were performed to disclose the behaviour of PVC and HDPE in tension and compression. Quasi-static tests were carried out at three rates in compression and two in tension. Digital image correlation. DIC, was used to measure the in-plane strains, revealing true stress-strain curves and allowing to analyze strain-rate sensitivity and isotropy of Poisson`s ratio. In addition, dynamic compression tests were carried out in a split-Hopkinson pressure bar. Quasi-static and dynamic tests were also performed on clamped plates made of the same PVC and HDPE materials, using an optical technique to measure the full-field out-of-plane deformations. These tests, together with the material data, were used for comparative purposes of a finite element analysis. A reasonable agreement between experimental and numerical results was achieved. (C) 2010 Elsevier Ltd. All rights reserved.

Mechanical strength evaluation for Nd-YAG laser and electric resistance spot weld (ERSW) joint under multiaxial loading

This work presents a comparison between laser weld (LBW) and electric resistance spot weld (ERSW) processes used for assemblies of components in a body-in-white (BIW) at a world class automotive industry. It is carried out by evaluating the mechanical strength modeled both by experimental and numerical methods. An ""Arcan"" multiaxial test was designed and manufactured in order to enable 0 degrees, 45 degrees and 90 degrees directional loadings. The welded specimens were uncoated low carbon steel sheets (S-y = 170 MPa) used currently at the automotive industry, with two different thicknesses: 0.80 and 1.20 mm. A numerical analysis was carried out using the finite element method (FEM) through LS-DYNA code. (c) 2007 Elsevier B.V. All rights reserved.

Local search multiuser detection

In this work, a wide analysis of local search multiuser detection (LS-MUD) for direct sequence/code division multiple access (DS/CDMA) systems under multipath channels is carried out considering the performance-complexity trade-off. It is verified the robustness of the LS-MUD to variations in loading, E(b)/N(0), near-far effect, number of fingers of the Rake receiver and errors in the channel coefficients estimates. A compared analysis of the bit error rate (BER) and complexity trade-off is accomplished among LS, genetic algorithm (GA) and particle swarm optimization (PSO). Based on the deterministic behavior of the LS algorithm, it is also proposed simplifications over the cost function calculation, obtaining more efficient algorithms (simplified and combined LS-MUD versions) and creating new perspectives for the MUD implementation. The computational complexity is expressed in terms of the number of operations in order to converge. Our conclusion pointed out that the simplified LS (s-LS) method is always more efficient, independent of the system conditions, achieving a better performance with a lower complexity than the others heuristics detectors. Associated to this, the deterministic strategy and absence of input parameters made the s-LS algorithm the most appropriate for the MUD problem. (C) 2008 Elsevier GmbH. All rights reserved.

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Fatty acid profile and qualitative characteristics of meat from zebu steers fed with different oilseeds

The objective of this study was to evaluate the effects of dietary addition of ground oilseed sources on the quality, fatty acid profile, and CLA content of meat from zebu steers. Thirty-one zebu steers with an initial average age of 23 mo and an initial BW of 365 kg were used in this study. The experimental period was 84 d, which was preceded by an adaption period of 28 d. The diet was provided ad libitum with a forage: concentrate ratio of 40:60. Corn silage was used as the forage source. Four different concentrates were formulated for each treatment: without additional lipids (control) or with ground soybeans (SB), ground cottonseed (CS), or ground linseed (LS). The SB, CS, and LS diets were formulated to have 6.5% ether extract on a total dietary DM basis. The experiment was set up as a completely randomized design. After slaughter, samples were taken from the longissimus thoracis muscle for the measurement of fatty acid concentration and the evaluation of meat quality. The luminosity index was greater in the control and LS diets (P < 0.01). The greatest percentages of myristic acid (C14:0), palmitic acid (C16:0), trans octadecenoic acid (C18:1 trans-10, trans-11, or trans-12), and SFA in the subcutaneous fat were observed in the CS treatment (P < 0.01). Moreover, the least percentages of oleic acid (C18:1 cis-9) and total unsaturated fatty acids in the subcutaneous fat were observed in the CS diet (P < 0.01). The meat linoleic acid and a-linolenic acid percentages were greatest in the SB and LS treatments, respectively (P < 0.001). The unsaturated fatty acid: SFA ratio was smallest for the CS diet (P < 0.01). A gradual increase in oxidation was observed as a function of storage time; however, the diets did not affect the rancidity of the meat (P > 0.05). The fatty acid profile of subcutaneous fat was impaired by the addition of CS. Supplying ground oilseeds did not increase the content of CLA in the meat.