Residence time distribution in holding tubes using generalized convection model and numerical convolution for non-ideal tracer detection

A procedure is proposed for the determination of the residence time distribution (RTD) of curved tubes taking into account the non-ideal detection of the tracer. The procedure was applied to two holding tubes used for milk pasteurization in laboratory scale. Experimental data was obtained using an ionic tracer. The signal distortion caused by the detection system was considerable because of the short residence time. Four RTD models, namely axial dispersion, extended tanks in series, generalized convection and PER + CSTR association, were adjusted after convolution with the E-curve of the detection system. The generalized convection model provided the best fit because it could better represent the tail on the tracer concentration curve that is Caused by the laminar velocity profile and the recirculation regions. Adjusted model parameters were well cot-related with the now rate. (C) 2010 Elsevier Ltd. All rights reserved.

Improved effective charge density in MOS capacitors with PECVD SiOxNy dielectric layer obtained at low RF power

In this work SiOxNy films are produced and characterized. Series of samples were deposited by the plasma enhanced chemical vapor deposition (PECVD) technique at low temperatures from silane (SiH4), nitrous oxide (N2O) and helium (He) precursor gaseous mixtures, at different deposition power in order to analyze the effect of this parameter on the films structural properties, on the SiOxNy/Si interface quality and on the SiOxNy effective charge density. In order to compare the film structural properties with the interface (SiOxNy/Si) quality and effective charge density, MOS capacitors were fabricated using these films as dielectric layer. X-ray absorption near-edge spectroscopy (XANES), at the Si-K edge, was utilized to investigate the structure of the films and the material bonding characteristics were analyzed through Fourier transform infrared spectroscopy (FTIR). The MOS capacitors were characterized by low and high frequency capacitance (C-V) measurements, in order to obtain the interface state density (D-it) and the effective charge density (N-ss). An effective charge density linear reduction for decreasing deposition power was observed, result that is attributed to the smaller amount of ions present in the plasma for low RF power. (C) 2008 Elsevier B.V. All rights reserved.

Distributed power control algorithm for multiple access systems based on Verhulst model

In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

Spectral properties of chaotic signals generated by the skew tent map

Chaotic signals have been considered potentially attractive in many signal processing applications ranging from wideband communication systems to cryptography and watermarking. Besides, some devices as nonlinear adaptive filters and phase-locked loops can present chaotic behavior. In this paper, we derive analytical expressions for the autocorrelation sequence, power spectral density and essential bandwidth of chaotic signals generated by the skew tent map. From these results, we suggest possible applications in communication systems. (C) 2009 Elsevier B.V. All rights reserved.

Spectral properties of chaotic signals with applications in communications

This paper investigates the characteristics of the Power Spectral Density (PSD) of chaotic signals generated by skew tent maps. The influence of the Lyapunov exponent on the autocorrelation sequence and on the PSD is evaluated via computational simulations. We conclude that the essential bandwidth of these signals is strongly related to this exponent and they can be low-pass or high-pass depending on the family`s parameter. This way, the PSD of a chaotic signal is a function of the generating map although this is not a one-to-one relationship. (C) 2009 Elsevier Ltd. All rights reserved.

Modeling and measuring double-frequency jitter in one-way master-slave networks

The double-frequency jitter is one of the main problems in clock distribution networks. In previous works, sonic analytical and numerical aspects of this phenomenon were studied and results were obtained for one-way master-slave (OWMS) architectures. Here, an experimental apparatus is implemented, allowing to measure the power of the double-frequency signal and to confirm the theoretical conjectures. (C) 2008 Elsevier B.V. All rights reserved.

Thermodynamic information system for diagnosis and prognosis of power plant operation condition

A thermodynamic information system for diagnosis and prognosis of an existing power plant was developed. The system is based on an analytic approach that informs the current thermodynamic condition of all cycle components, as well as the improvement that can be obtained in the cycle performance by the elimination of the discovered anomalies. The effects induced by components anomalies and repairs in other components efficiency, which have proven to be one of the main drawbacks in the diagnosis and prognosis analyses, are taken into consideration owing to the use of performance curves and corrected performance curves together with the thermodynamic data collected from the distributed control system. The approach used to develop the system is explained, the system implementation in a real gas turbine cogeneration combined cycle is described and the results are discussed. (C) 2011 Elsevier Ltd. All rights reserved.

Bias-corrected Pearson estimating functions for Taylor`s power law applied to benthic macrofauna data

Estimation of Taylor`s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm, with standard errors calculated from the inverse Godambe information matrix. The method is applied to a set of biomass data for benthic macrofauna from two Danish estuaries. (C) 2011 Elsevier B.V. All rights reserved.

SacRALF1, a peptide signal from the grass sugarcane (Saccharum spp.), is potentially involved in the regulation of tissue expansion

Rapid alkalinization factor (RALF) is part of a growing family of small peptides with hormone characteristics in plants. Initially isolated from leaves of tobacco plants, RALF peptides can be found throughout the plant kingdom and they are expressed ubiquitously in plants. We took advantage of the small gene family size of RALF genes in sugarcane and the ordered cellular growth of the grass sugarcane leaves to gain information about the function of RALF peptides in plants. Here we report the isolation of two RALF peptides from leaves of sugarcane plants using the alkalinization assay. SacRALF1 was the most abundant and, when added to culture media, inhibited growth of microcalli derived from cell suspension cultures at concentrations as low as 0.1 mu M. Microcalli exposed to exogenous SacRALF1 for 5 days showed a reduced number of elongated cells. Only four copies of SacRALF genes were found in sugarcane plants. All four SacRALF genes are highly expressed in young and expanding leaves and show a low or undetectable level of expression in expanded leaves. In half-emerged leaf blades, SacRALF transcripts were found at high levels at the basal portion of the leaf and at low levels at the apical portion. Gene expression analyzes localize SacRALF genes in elongation zones of roots and leaves. Mature leaves, which are devoid of expanding cells, do not show considerable expression of SacRALF genes. Our findings are consistent with SacRALF genes playing a role in plant development potentially regulating tissue expansion.

Education in Human Rights: Reflections on Power, Violence and Authority in the School

This article explores human rights and education based on an intervention experience conducted in three schools located in Sao Paulo City, which had as its main goal a substantial reduction in violence (2004-2005). The guideline was that education should be considered a basic human right, taking into consideration the power and authority relations that exist within this institution. What are the problems that we face, nowadays, to consider education as a human right, in the difficult Brazilian history? Is it possible to think about some kind of democratic authority within the school, when our vision of authority is linked to despotic leaders, or even when there is no space for any authority? How does this discussion associate with the violence in our daily life in school? These are some of the questions included in the debate proposed by this article.

Signal Transduction, Plasma Membrane Calcium Movements, and Pigment Translocation in Freshwater Shrimp Chromatophores

Crustacean color change results from the differential translocation of chromatophore pigments, regulated by neurosecretory peptides like red pigment concentrating hormone (RPCH) that, in the red ovarian chromatophores of the freshwater shrimp Macrobrachium olfersi, triggers pigment aggregation via increased cytosolic cGMP and Ca(2+) of both smooth endoplasmatic reticulum (SER) and extracellular origin. However, Ca(2+) movements during RPCH signaling and the mechanisms that regulate intracellular [Ca(2+)] are enigmatic. We investigate Ca(2+) transporters in the chromatophore plasma membrane and Ca(2+) movements that occur during RPCH signal transduction. Inhibition of the plasma membrane Ca(2+)-ATPase by La(3+) and indirect inhibition of the Na(+)/Ca(2+) exchanger by ouabain induce pigment aggregation, revealing a role for both in Ca(2+) extrusion. Ca(2+) channel blockade by La(3+) or Cd(2+) strongly inhibits slow-phase RPCH-triggered aggregation during which pigments disperse spontaneously. L-type Ca(2+) channel blockade by gabapentin markedly reduces rapid-phase translocation velocity; N- or P/Q-type blockade by omega-conotoxin MVIIC strongly inhibits RPCH-triggered aggregation and reduces velocity, effects revealing RPCH-signaled influx of extracellular Ca(2+). Plasma membrane depolarization, induced by increasing external K(+) from 5 to 50 mM, produces Ca(2+)-dependent pigment aggregation, whereas removal of K(+) from the perfusate causes pigment hyperdispersion, disclosing a clear correlation between membrane depolarization and pigment aggregation; K(+) channel blockade by Ba(2+) also partially inhibits RPCH action. We suggest that, during RPCH signal transduction, Ca(2+) released from the SER, together with K(+) channel closure, causes chromatophore membrane depolarization, leading to the opening of predominantly N- and/or P/Q-type voltage-gated Ca(2+) channels, and a Ca(2+)/cGMP cascade, resulting in pigment aggregation. J. Exp. Zool. 313A:605-617, 2010. (C) 2010 Wiley-Liss, Inc.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Renal Cell Carcinoma: T1 and T2 Signal Intensity Characteristics of Papillary and Clear Cell Types Correlated with Pathology

OBJECTIVE. The objective of our study was to describe the T1 and T2 signal intensity characteristics of papillary renal cell carcinoma (RCC) and clear cell RCC with pathologic correlation. MATERIALS AND METHODS. Of 539 RCCs, 49 tumors (21 papillary RCCs and 28 clear cell RCCs) in 45 patients were examined with MRI. Two radiologists retrospectively and independently assessed each tumor`s T1 and T2 signal intensity qualitatively and quantitatively (i.e., the signal intensity [SI] ratio [tumor SI/renal cortex SI]). Of the 49 tumors, 37 (76%) were assessed for pathology features including tumor architecture and the presence of hemosiderin, ferritin, necrosis, and fibrosis. MRI findings and pathology features were correlated. Statistical methods included summary statistics and Wilcoxon`s rank sum test for signal intensity, contingency tables for assessing reader agreement, concordance rate between the two readers with 95% CIs, and Fisher`s exact test for independence, all stratified by RCC type. RESULTS. Papillary RCCs and clear cell RCCs had a similar appearance and signal intensity ratio on T1-weighted images. On T2-weighted images, most papillary RCCs were hypointense (reader 1, 13/21; reader 2, 14/21), with an average mean signal intensity ratio for both readers of 0.67 +/- 0.2, and none was hyperintense, whereas most clear cell RCCs were hyperintense (reader 1, 21/28; reader 2, 17/28), with an average mean signal intensity ratio for both readers of 1.41 +/- 0.4 (p < 0.05). A tumor T2 signal intensity ratio of <= 0.66 had a specificity of 100% and sensitivity of 54% for papillary RCC. Most T2 hypointense tumors exhibited predominant papillary architecture; most T2 hyperintense tumors had a predominant nested architecture (p < 0.05). CONCLUSION. On T2-weighted images, most papillary RCCs are hypointense and clear cell RCCs, hyperintense. The T2 hypointense appearance of papillary RCCs correlated with a predominant papillary architecture at pathology.