Sequence-specific electrochemical detection of Alicyclobacillus acidoterrestris DNA using electroconductive polymer-modified fluorine tin oxide electrodes

This study outlines the quantification of low levels of Alicyclobacillus acidoterrestris in pure cultures, since this bacterium is not inactivated by pasteurization and may remain in industrialized foods and beverages. Electroconductive polymer-modified fluorine tin oxide (FTO) electrodes and multiple nanoparticle labels were used for biosensing. The detection of A. acidoterrestris in pure cultures was performed by reverse transcription polymerase chain reaction (RT-PCR) and the sensitivity was further increased by asymmetric nested RT-PCR using electrochemical detection for quantification of the amplicon. The quantification of nested RT-PCR products by Ag/Au-based electrochemical detection was able to detect 2 colony forming units per mL (CFU mL(-1)) of spores in pure culture and low detection and quantification limits (7.07 and 23.6 nM, respectively) were obtained for the target A. acidoterrestris on the electrochemical detection bioassay.

Rapid and sensitive measurements of nitrate ester explosives using microchip electrophoresis with electrochemical detection

This article describes an effective microchip protocol based on electrophoretic-separation and electrochemical detection for highly sensitive and rapid measurements of nitrate ester explosives, including ethylene glycol dinitrate (EGDN), pentaerythritol tetranitrate (PETN), propylene glycol dinitrate (PGDN) and glyceryl trinitrate (nitroglycerin, NG). Factors influencing the separation and detection processes were examined and optimized. Under the optimal separation conditions obtained using a 15 mM borate buffer (pH 9.2) containing 20 mM SDS, and applying a separation voltage of 1500 V, the four nitrate ester explosives were separated within less than 3 min. The glassy-carbon amperometric detector (operated at -0.9 V vs. Ag/AgCl) offers convenient cathodic detection down to the picogram level, with detection limits of 0.5 ppm and 0.3 ppm for PGDN and for NG, respectively, along with good repeatability (RSD of 1.8-2.3%; n = 6) and linearity (over the 10-60 ppm range). Such effective microchip operation offers great promise for field screening of nitrate ester explosives and for supporting various counter-terrorism surveillance activities.

Rapid prototyping of polymeric electrophoresis microchips with integrated copper electrodes for contactless conductivity detection

A simple and easy approach to produce polymeric microchips with integrated copper electrodes for capacitively coupled contactless conductivity detection (CD) is described. Copper electrodes were fabricated using a printed circuit board (PCB) as an inexpensive thin-layer of metal. The electrode layout was first drawn and laser printed on a wax paper sheet. The toner layer deposited on the paper sheet was thermally transferred to the PCB surface working as a mask for wet chemical etching of the copper layer. After the etching step, the toner was removed with an acetonitrile-dampened cotton. A poly(ethylene terephthalate) (PET) film coated with a thin thermo-sensitive adhesive layer was used to laminate the PCB plate providing an insulator layer of the electrodes to perform CID measurements. Electrophoresis microchannels were fabricated in poly(dimethylsiloxane) (PDMS) by soft lithography and reversibly sealed against the PET film. These hybrid PDMS/PET chips exhibited a stable electroosmotic mobility of 4.25 +/- 0.04 x 10(-4) V cm(-2) s(-1), at pH 6.1, over fifty runs. Efficiencies ranging from 1127 to 1690 theoretical plates were obtained for inorganic cations.

Evaluation of the use of multiple lines for determination of metals in water by inductively coupled plasma optical emission spectrometry with axial viewing

Inductively coupled plasma optical emission spectrometers (ICP DES) allow fast simultaneous measurements of several spectral lines for multiple elements. The combination of signal intensities of two or more emission lines for each element may bring such advantages as improvement of the precision, the minimization of systematic errors caused by spectral interferences and matrix effects. In this work, signal intensities for several spectral lines were combined for the determination of Al, Cd, Co, Cr, Mn, Pb, and Zn in water. Afterwards, parameters for evaluation of the calibration model were calculated to select the combination of emission lines leading to the best accuracy (lowest values of PRESS-Predicted error sum of squares and RMSEP-Root means square error of prediction). Limits of detection (LOD) obtained using multiple lines were 7.1, 0.5, 4.4, 0.042, 3.3, 28 and 6.7 mu g L(-1) (n = 10) for Al, Cd. Co, Cr, Mn, Pb and Zn, respectively, in the presence of concomitants. On the other hand, the LOD established for the most intense emission line were 16. 0.7, 8.4, 0.074. 23, 26 and 9.6 mu g L(-1) (n = 10) for these same elements in the presence of concomitants. The accuracy of the developed procedure was demonstrated using water certified reference material. The use of multiple lines improved the sensitivity making feasible the determination of these analytes according to the target values required for the current environmental legislation for water samples and it was also demonstrated that measurements in multiple lines can also be employed as a tool to verify the accuracy of an analytical procedure in ICP DES. (C) 2009 Elsevier B.V. All rights reserved.

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.

Cardiovascular adaptive responses in rats submitted to moderate resistance training

The present study investigated the effects of 8 week of resistance training (RT) on hemodynamic and ventricular function on cardiac myosin ATPase activity, and on contractility of papillary muscles of rats. Groups: control (CO), electrically stimulated (ES), trained at 60% (TR 60%) and 75% of one repetition maximum (1RM) (TR 75%). Exercise protocol: 5 sets of 12 repetitions at 60 and 75% of 1RM, 5 times per week. The CO and ES groups had similar values for parameters analyzed (P > 0.05). Blood pressure (BP), heart rate (13%), left ventricle systolic pressure (LVSP 13%) decreased and cardiac myosin ATPase activity increased in the TR 75% group (90%, P < 0.05). The contractile performance of papillary muscles increased in trained rats (P < 0.05). Eight weeks of RT was associated with lowering of resting BP, heart rate and LVSP, improvements in contractility of the papillary muscle and an increase of cardiac myosin ATPase activity in rats.

Constant-random practice and the adaptive process in motor learning with varying amounts of constant practice

The adaptive process in motor learning was examined in terms of effects of varying amounts of constant practice performed before random practice. Participants pressed five response keys sequentially, the last one coincident with the lighting of a final visual stimulus provided by a complex coincident timing apparatus. Different visual stimulus speeds were used during the random practice. 33 children (M age=11.6 yr.) were randomly assigned to one of three experimental groups: constant-random, constant-random 33%, and constant-random 66%. The constant-random group practiced constantly until they reached a criterion of performance stabilization three consecutive trials within 50 msec. of error. The other two groups had additional constant practice of 33 and 66%, respectively, of the number of trials needed to achieve the stabilization criterion. All three groups performed 36 trials under random practice; in the adaptation phase, they practiced at a different visual stimulus speed adopted in the stabilization phase. Global performance measures were absolute, constant, and variable errors, and movement pattern was analyzed by relative timing and overall movement time. There was no group difference in relation to global performance measures and overall movement time. However, differences between the groups were observed on movement pattern, since constant-random 66% group changed its relative timing performance in the adaptation phase.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

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.

Fault Detection in Power Distribution Systems Using Automated Integration of Computational Intelligence Tools

The main purpose of this paper is to present architecture of automated system that allows monitoring and tracking in real time (online) the possible occurrence of faults and electromagnetic transients observed in primary power distribution networks. Through the interconnection of this automated system to the utility operation center, it will be possible to provide an efficient tool that will assist in decisionmaking by the Operation Center. In short, the desired purpose aims to have all tools necessary to identify, almost instantaneously, the occurrence of faults and transient disturbances in the primary power distribution system, as well as to determine its respective origin and probable location. The compilations of results from the application of this automated system show that the developed techniques provide accurate results, identifying and locating several occurrences of faults observed in the distribution system.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids

On-line leak detection is a main concern for the safe operation of pipelines. Acoustic and mass balance are the most important and extensively applied technologies in field problems. The objective of this work is to compare these leak detection methods with respect to a given reference situation, i.e., the same pipeline and monitoring signals acquired at the inlet and outlet ends. Experimental tests were conducted in a 749 m long laboratory pipeline transporting water as the working fluid. The instrumentation included pressure transducers and electromagnetic flowmeters. Leaks were simulated by opening solenoid valves placed at known positions and previously calibrated to produce known average leak flow rates. Results have clearly shown the limitations and advantages of each method. It is also quite clear that acoustics and mass balance technologies are, in fact, complementary. In general, an acoustic leak detection system sends out an alarm more rapidly and locates the leak more precisely, provided that the rupture of the pipeline occurs abruptly enough. On the other hand, a mass balance leak detection method is capable of quantifying the leak flow rate very accurately and of detecting progressive leaks.

Effective Electromechanical Coupling Coefficients of Piezoelectric Adaptive Structures: Critical Evaluation and Optimization

This work presents a critical analysis of methodologies to evaluate the effective (or generalized) electromechanical coupling coefficient (EMCC) for structures with piezoelectric elements. First, a review of several existing methodologies to evaluate material and effective EMCC is presented. To illustrate the methodologies, a comparison is made between numerical, analytical and experimental results for two simple structures: a cantilever beam with bonded extension piezoelectric patches and a simply-supported sandwich beam with an embedded shear piezoceramic. An analysis of the electric charge cancelation effect on the effective EMCC observed in long piezoelectric patches is performed. It confirms the importance of reinforcing the electrodes equipotentiality condition in the finite element model. Its results indicate also that smaller (segmented) and independent piezoelectric patches could be more interesting for energy conversion efficiency. Then, parametric analyses and optimization are performed for a cantilever sandwich beam with several embedded shear piezoceramic patches. Results indicate that to fully benefit from the higher material coupling of shear piezoceramic patches, attention must be paid to the configuration design so that the shear strains in the patches are maximized. In particular, effective square EMCC values higher than 1% were obtained embedding nine well-spaced short piezoceramic patches in an aluminum/foam/aluminum sandwich beam.

Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator

This paper aims to formulate and investigate the application of various nonlinear H(infinity) control methods to a fiee-floating space manipulator subject to parametric uncertainties and external disturbances. From a tutorial perspective, a model-based approach and adaptive procedures based on linear parametrization, neural networks and fuzzy systems are covered by this work. A comparative study is conducted based on experimental implementations performed with an actual underactuated fixed-base planar manipulator which is, following the DEM concept, dynamically equivalent to a free-floating space manipulator. (C) 2011 Elsevier Ltd. All rights reserved.