Geochemistry, mineral composition and Ar40/Ar39 ages of dolerites from ODP Leg 180 sites

New trace element analyses are presented for Leg 180 dolerites, basalts from the Papuan Ultramafic Belt (PUB), and basement rocks of Woodlark Island. The Leg 180 dolerites are similar to those from Woodlark Island in being derived from an enriched source but differ from the PUB, which came from a source similar to normal mid-ocean ridge basalts. A reliable 40Ar/39Ar age of 54.0 ± 1.0 Ma has been obtained by step heating of a whole-rock sample from Site 1109, and a similar but less reliable age was obtained for a sample from Site 1118. Plagioclase from Site 1109 did not give a meaningful age. This age is broadly similar to ages from the Dabi volcanics of the nearby Cape Vogel and for the PUB.

Dynamic chanel model LMS updating for RSS-based localization

Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes.

Cancelación activa del ruido utilizando el KIT TMS320C5515 EZDSP

El control, o cancelación activa de ruido, consiste en la atenuación del ruido presente en un entorno acústico mediante la emisión de una señal igual y en oposición de fase al ruido que se desea atenuar. La suma de ambas señales en el medio acústico produce una cancelación mutua, de forma que el nivel de ruido resultante es mucho menor al inicial. El funcionamiento de estos sistemas se basa en los principios de comportamiento de los fenómenos ondulatorios descubiertos por Augustin-Jean Fresnel, Christiaan Huygens y Thomas Young entre otros. Desde la década de 1930, se han desarrollado prototipos de sistemas de control activo de ruido, aunque estas primeras ideas eran irrealizables en la práctica o requerían de ajustes manuales cada poco tiempo que hacían inviable su uso. En la década de 1970, el investigador estadounidense Bernard Widrow desarrolla la teoría de procesado adaptativo de señales y el algoritmo de mínimos cuadrados LMS. De este modo, es posible implementar filtros digitales cuya respuesta se adapte de forma dinámica a las condiciones variables del entorno. Con la aparición de los procesadores digitales de señal en la década de 1980 y su evolución posterior, se abre la puerta para el desarrollo de sistemas de cancelación activa de ruido basados en procesado de señal digital adaptativo. Hoy en día, existen sistemas de control activo de ruido implementados en automóviles, aviones, auriculares o racks de equipamiento profesional. El control activo de ruido se basa en el algoritmo fxlms, una versión modificada del algoritmo LMS de filtrado adaptativo que permite compensar la respuesta acústica del entorno. De este modo, se puede filtrar una señal de referencia de ruido de forma dinámica para emitir la señal adecuada que produzca la cancelación. Como el espacio de cancelación acústica está limitado a unas dimensiones de la décima parte de la longitud de onda, sólo es viable la reducción de ruido en baja frecuencia. Generalmente se acepta que el límite está en torno a 500 Hz. En frecuencias medias y altas deben emplearse métodos pasivos de acondicionamiento y aislamiento, que ofrecen muy buenos resultados. Este proyecto tiene como objetivo el desarrollo de un sistema de cancelación activa de ruidos de carácter periódico, empleando para ello electrónica de consumo y un kit de desarrollo DSP basado en un procesador de muy bajo coste. Se han desarrollado una serie de módulos de código para el DSP escritos en lenguaje C, que realizan el procesado de señal adecuado a la referencia de ruido. Esta señal procesada, una vez emitida, produce la cancelación acústica. Empleando el código implementado, se han realizado pruebas que generan la señal de ruido que se desea eliminar dentro del propio DSP. Esta señal se emite mediante un altavoz que simula la fuente de ruido a cancelar, y mediante otro altavoz se emite una versión filtrada de la misma empleando el algoritmo fxlms. Se han realizado pruebas con distintas versiones del algoritmo, y se han obtenido atenuaciones de entre 20 y 35 dB medidas en márgenes de frecuencia estrechos alrededor de la frecuencia del generador, y de entre 8 y 15 dB medidas en banda ancha. ABSTRACT. Active noise control consists on attenuating the noise in an acoustic environment by emitting a signal equal but phase opposed to the undesired noise. The sum of both signals results in mutual cancellation, so that the residual noise is much lower than the original. The operation of these systems is based on the behavior principles of wave phenomena discovered by Augustin-Jean Fresnel, Christiaan Huygens and Thomas Young. Since the 1930’s, active noise control system prototypes have been developed, though these first ideas were practically unrealizable or required manual adjustments very often, therefore they were unusable. In the 1970’s, American researcher Bernard Widrow develops the adaptive signal processing theory and the Least Mean Squares algorithm (LMS). Thereby, implementing digital filters whose response adapts dynamically to the variable environment conditions, becomes possible. With the emergence of digital signal processors in the 1980’s and their later evolution, active noise cancellation systems based on adaptive signal processing are attained. Nowadays active noise control systems have been successfully implemented on automobiles, planes, headphones or racks for professional equipment. Active noise control is based on the fxlms algorithm, which is actually a modified version of the LMS adaptive filtering algorithm that allows compensation for the acoustic response of the environment. Therefore it is possible to dynamically filter a noise reference signal to obtain the appropriate cancelling signal. As the noise cancellation space is limited to approximately one tenth of the wavelength, noise attenuation is only viable for low frequencies. It is commonly accepted the limit of 500 Hz. For mid and high frequencies, conditioning and isolating passive techniques must be used, as they produce very good results. The objective of this project is to develop a noise cancellation system for periodic noise, by using consumer electronics and a DSP development kit based on a very-low-cost processor. Several C coded modules have been developed for the DSP, implementing the appropriate signal processing to the noise reference. This processed signal, once emitted, results in noise cancellation. The developed code has been tested by generating the undesired noise signal in the DSP. This signal is emitted through a speaker simulating the noise source to be removed, and another speaker emits an fxlms filtered version of the same signal. Several versions of the algorithm have been tested, obtaining attenuation levels around 20 – 35 dB measured in a tight bandwidth around the generator frequency, or around 8 – 15 dB measured in broadband.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

Analysis of the aerobic-anaerobic transition in elite cyclists during incremental exercise with the use of electromyography

To investigate the validity and reliability of surface electromyography (EMG) as a new non-invasive determinant of the metabolic response to incremental exercise in elite cyclists. The relation between EMG activity and other more conventional methods for analysing the aerobic-anaerobic transition such as blood lactate measurements (lactate threshold (LT) and onset of blood lactate accumulation (OBLA)) and ventilatory parameters (ventilatory thresholds 1 and 2 (VT1 and VT2)) was studied.Twenty eight elite road cyclists (age 24 (4) years; VO2MAX 69.9 (6.4) ml/kg/min; values mean (SD)) were selected as subjects. Each of them performed a ramp protocol (starting at 0 W, with increases of 5 W every 12 seconds) on a cycle ergometer (validity study). In addition, 15 of them performed the same test twice (reliability study). During the tests, data on gas exchange and blood lactate levels were collected to determine VT1, VT2, LT, and OBLA. The root mean squares of EMG signals (rms-EMG) were recorded from both the vastus lateralis and the rectus femoris at each intensity using surface electrodes. Results - A two threshold response was detected in the rms-EMG recordings from both muscles in 90% of subjects, with two breakpoints, EMG(T1) and EMG(T2), at around 60-70% and 80-90% of VO2MAX respectively. The results of the reliability study showed no significant differences (p > 0.05) between mean values of EMG(T1) and EMG(T2) obtained in both tests. Furthermore, no significant differences (p > 0.05) existed between mean values of EMG(T1), in the vastus lateralis and rectus femoris, and VT1 and LT (62.8 (14.5) and 69.0 (6.2) and 64.6 (6.4) and 68.7 (8.2)% of VO2MAX respectively), or between mean values of EMG(T2), in the vastus lateralis and rectus femoris, and VT2 and OBLA (86.9 (9.0) and 88.0 (6.2) and 84.6 (6.5) and 87.7 (6.4)% of VO2MAX respectively). Rms-EMG may be a useful complementary non-invasive method for analysing the aerobic-anaerobic transition (ventilatory and lactate thresholds) in elite cyclists.

Genetic response to combined family selection for improved mean harvest weight in giant freshwater prawn (Macrobrachium rosenbergii) in Vietnam

We estimated genetic changes in body and carcass weight traits in a giant freshwater prawn (GFP) (Macrobrachium rosenbergii) population selected for increased body weight at harvest in Vietnam. The data set consisted of 18,387 individual body and 1730 carcass weight records, as well as full pedigree information collected over four generations. Average selection response (per generation) in body weight at harvest (transformed to square root) estimated as the difference between the Selection line and the Control group was 7.4% calculated from least squares mean (LSMs), 7.0% from estimated breeding values (EBVs) and 4.4% calculated from EBVs between two consecutive generations. Favorable correlated selection responses (estimated from LSMs) were found for other body traits including: total length, cephalothorax length, abdominal length, cephalothorax width, and abdominal width (12.1%, 14.5%, 10.4%, 15.5% and 13.3% over three selection generations, respectively). Data in the second generation of selection showed positive correlated responses for carcass weight traits including: abdominal weight, exoskeleton-off weight, and telson-off weight of 8.8%, 8.6% and 8.8%, respectively. We conclude that body weight at harvest responded well to the application of combined (between and within) family selection and correlated responses in carcass weight traits were favorable.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Molecular dynamics calculation of mean square displacement in alkali metals and rare gas solids and comparison with lattice dynamics

Molec ul ar dynamics calculations of the mean sq ua re displacement have been carried out for the alkali metals Na, K and Cs and for an fcc nearest neighbour Lennard-Jones model applicable to rare gas solids. The computations for the alkalis were done for several temperatures for temperature vol ume a swell as for the the ze r 0 pressure ze ro zero pressure volume corresponding to each temperature. In the fcc case, results were obtained for a wide range of both the temperature and density. Lattice dynamics calculations of the harmonic and the lowe s t order anharmonic (cubic and quartic) contributions to the mean square displacement were performed for the same potential models as in the molecular dynamics calculations. The Brillouin zone sums arising in the harmonic and the quartic terms were computed for very large numbers of points in q-space, and were extrapolated to obtain results ful converged with respect to the number of points in the Brillouin zone.An excellent agreement between the lattice dynamics results was observed molecular dynamics and in the case of all the alkali metals, e~ept for the zero pressure case of CSt where the difference is about 15 % near the melting temperature. It was concluded that for the alkalis, the lowest order perturbation theory works well even at temperat ures close to the melting temperat ure. For the fcc nearest neighbour model it was found that the number of particles (256) used for the molecular dynamics calculations, produces a result which is somewhere between 10 and 20 % smaller than the value converged with respect to the number of particles. However, the general temperature dependence of the mean square displacement is the same in molecular dynamics and lattice dynamics for all temperatures at the highest densities examined, while at higher volumes and high temperatures the results diverge. This indicates the importance of the higher order (eg. ~* ) perturbation theory contributions in these cases.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Estimation of the Offspring and Immigration Mean Vectors for Bisexual Branching Processes with Immigration of Females and Males

2000 Mathematics Subject Classification: 60J80, 62M05.

Offspring Mean Estimators in Branching Processes with Immigration

2000 Mathematics Subject Classification: 60J80.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).