Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach.

Microscopic-macroscopic approach for binding energies with the Wigner-Kirkwood method

The semiclassical Wigner-Kirkwood ̄h expansion method is used to calculate shell corrections for spherical and deformed nuclei. The expansion is carried out up to fourth order in ̄h. A systematic study of Wigner-Kirkwood averaged energies is presented as a function of the deformation degrees of freedom. The shell corrections, along with the pairing energies obtained by using the Lipkin-Nogami scheme, are used in the microscopic-macroscopic approach to calculate binding energies. The macroscopic part is obtained from a liquid drop formula with six adjustable parameters. Considering a set of 367 spherical nuclei, the liquid drop parameters are adjusted to reproduce the experimental binding energies, which yields a root mean square (rms) deviation of 630 keV. It is shown that the proposed approach is indeed promising for the prediction of nuclear masses.

Neuro-mechanical determinants of repeated treadmill sprints - Usefulness of an "hypoxic to normoxic recovery" approach.

To improve our understanding of the limiting factors during repeated sprinting, we manipulated hypoxia severity during an initial set and examined the effects on performance and associated neuro-mechanical alterations during a subsequent set performed in normoxia. On separate days, 13 active males performed eight 5-s sprints (recovery = 25 s) on an instrumented treadmill in either normoxia near sea-level (SL; FiO2 = 20.9%), moderate (MH; FiO2 = 16.8%) or severe normobaric hypoxia (SH; FiO2 = 13.3%) followed, 6 min later, by four 5-s sprints (recovery = 25 s) in normoxia. Throughout the first set, along with distance covered [larger sprint decrement score in SH (-8.2%) compared to SL (-5.3%) and MH (-7.2%); P < 0.05], changes in contact time, step frequency and root mean square activity (surface electromyography) of the quadriceps (Rectus femoris muscle) in SH exceeded those in SL and MH (P < 0.05). During first sprint of the subsequent normoxic set, the distance covered (99.6, 96.4, and 98.3% of sprint 1 in SL, MH, and SH, respectively), the main kinetic (mean vertical, horizontal, and resultant forces) and kinematic (contact time and step frequency) variables as well as surface electromyogram of quadriceps and plantar flexor muscles were fully recovered, with no significant difference between conditions. Despite differing hypoxic severity levels during sprints 1-8, performance and neuro-mechanical patterns did not differ during the four sprints of the second set performed in normoxia. In summary, under the circumstances of this study (participant background, exercise-to-rest ratio, hypoxia exposure), sprint mechanical performance and neural alterations were largely influenced by the hypoxia severity in an initial set of repeated sprints. However, hypoxia had no residual effect during a subsequent set performed in normoxia. Hence, the recovery of performance and associated neuro-mechanical alterations was complete after resting for 6 min near sea level, with a similar fatigue pattern across conditions during subsequent repeated sprints in normoxia.

BioSuper: A web tool for the superimposition of biomolecules and assemblies with rotational symmetry

Background Most of the proteins in the Protein Data Bank (PDB) are oligomeric complexes consisting of two or more subunits that associate by rotational or helical symmetries. Despite the myriad of superimposition tools in the literature, we could not find any able to account for rotational symmetry and display the graphical results in the web browser. Results BioSuper is a free web server that superimposes and calculates the root mean square deviation (RMSD) of protein complexes displaying rotational symmetry. To the best of our knowledge, BioSuper is the first tool of its kind that provides immediate interactive visualization of the graphical results in the browser, biomolecule generator capabilities, different levels of atom selection, sequence-dependent and structure-based superimposition types, and is the only web tool that takes into account the equivalence of atoms in side chains displaying symmetry ambiguity. BioSuper uses ICM program functionality as a core for the superimpositions and displays the results as text, HTML tables and 3D interactive molecular objects that can be visualized in the browser or in Android and iOS platforms with a free plugin. Conclusions BioSuper is a fast and functional tool that allows for pairwise superimposition of proteins and assemblies displaying rotational symmetry. The web server was created after our own frustration when attempting to superimpose flexible oligomers. We strongly believe that its user-friendly and functional design will be of great interest for structural and computational biologists who need to superimpose oligomeric proteins (or any protein). BioSuper web server is freely available to all users at http://ablab.ucsd.edu/BioSuper webcite.

The Coordinated Radio and Infrared Survey for High-mass Star Formation. II. Source Catalog

The CORNISH project is the highest resolution radio continuum survey of the Galactic plane to date. It is the 5 GHz radio continuum part of a series of multi-wavelength surveys that focus on the northern GLIMPSE region (10° < l < 65°), observed by the Spitzer satellite in the mid-infrared. Observations with the Very Large Array in B and BnA configurations have yielded a 1.''5 resolution Stokes I map with a root mean square noise level better than 0.4 mJy beam 1. Here we describe the data-processing methods and data characteristics, and present a new, uniform catalog of compact radio emission. This includes an implementation of automatic deconvolution that provides much more reliable imaging than standard CLEANing. A rigorous investigation of the noise characteristics and reliability of source detection has been carried out. We show that the survey is optimized to detect emission on size scales up to 14'' and for unresolved sources the catalog is more than 90% complete at a flux density of 3.9 mJy. We have detected 3062 sources above a 7σ detection limit and present their ensemble properties. The catalog is highly reliable away from regions containing poorly sampled extended emission, which comprise less than 2% of the survey area. Imaging problems have been mitigated by down-weighting the shortest spacings and potential artifacts flagged via a rigorous manual inspection with reference to the Spitzer infrared data. We present images of the most common source types found: H II regions, planetary nebulae, and radio galaxies. The CORNISH data and catalog are available online at http://cornish.leeds.ac.uk.

High Altitude Increases Alteration in Maximal Torque but Not in Rapid Torque Development in Knee Extensors after Repeated Treadmill Sprinting.

We assessed knee extensor neuromuscular adjustments following repeated treadmill sprints in different normobaric hypoxia conditions, with special reference to rapid muscle torque production capacity. Thirteen team- and racquet-sport athletes undertook 8 × 5-s "all-out" sprints (passive recovery = 25 s) on a non-motorized treadmill in normoxia (NM; FiO2 = 20.9%), at low (LA; FiO2 = 16.8%) and high (HA; FiO2 = 13.3%) normobaric hypoxia (simulated altitudes of ~1800 m and ~3600 m, respectively). Explosive (~1 s; "fast" instruction) and maximal (~5 s; "hard" instruction) voluntary isometric contractions (MVC) of the knee extensors (KE), with concurrent electromyographic (EMG) activity recordings of the vastus lateralis (VL) and rectus femoris (RF) muscles, were performed before and 1-min post-exercise. Rate of torque development (RTD) and EMG (i.e., Root Mean Square or RMS) rise from 0 to 30, -50, -100, and -200 ms were recorded, and were also normalized to maximal torque and EMG values, respectively. Distance covered during the first 5-s sprint was similar (P > 0.05) in all conditions. A larger (P < 0.05) sprint decrement score and a shorter (P < 0.05) cumulated distance covered over the eight sprints occurred in HA (-8 ± 4% and 178 ± 11 m) but not in LA (-7 ± 3% and 181 ± 10 m) compared to NM (-5 ± 2% and 183 ± 9 m). Compared to NM (-9 ± 7%), a larger (P < 0.05) reduction in MVC torque occurred post-exercise in HA (-14 ± 9%) but not in LA (-12 ± 7%), with no difference between NM and LA (P > 0.05). Irrespectively of condition (P > 0.05), peak RTD (-6 ± 11%; P < 0.05), and normalized peak RMS activity for VL (-8 ± 11%; P = 0.07) and RF (-14 ± 11%; P < 0.01) muscles were reduced post-exercise, whereas reductions (P < 0.05) in absolute RTD occurred within the 0-100 (-8 ± 9%) and 0-200 ms (-10 ± 8%) epochs after contraction onset. After normalization to MVC torque, there was no difference in RTD values. Additionally, the EMG rise for VL muscle was similar (P > 0.05), whereas it increased (P < 0.05) for RF muscle during all epochs post-exercise, independently of the conditions. In summary, alteration in repeated-sprint ability and post-exercise MVC decrease were greater at high altitude than in normoxia or at low altitude. However, the post-exercise alterations in RTD were similar between normoxia and low-to-high hypoxia.

Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Moisture sorption isotherms and isosteric heat determination in Chilean papaya (Vasconcellea pubescens)

The moisture sorption isotherms of Chilean papaya were determined at 5, 20, and 45 ºC, over a relative humidity range of 10-95%. The GAB, BET, Oswin, Halsey, Henderson, Smith, Caurie and Iglesias-Chirife models were applied to the sorption experimental data. The goodness of fit of the mathematical models was statistically evaluated by means of the determination coefficient, mean relative percentage deviation, sum square error, root-mean-square error, and chi-square values. The GAB, Oswin and Halsey models were found to be the most suitable for the description of the sorption data. The sorption heats calculated using the Clausius-Clapeyron equation were 57.35 and 59.98 kJ·mol-1, for adsorption and desorption isotherms, respectively.

Estatística aplicada à química: dez dúvidas comuns

Ten common doubts of chemistry students and professionals about their statistical applications are discussed. The use of the N-1 denominator instead of N is described for the standard deviation. The statistical meaning of the denominators of the root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV) are given for researchers using multivariate calibration methods. The reason why scientists and engineers use the average instead of the median is explained. Several problematic aspects about regression and correlation are treated. The popular use of triplicate experiments in teaching and research laboratories is seen to have its origin in statistical confidence intervals. Nonparametric statistics and bootstrapping methods round out the discussion.

Aplicação de técnicas multivariadas e inteligência artificial na análise de espectros de infravermelho para determinação de matéria orgânica em amostras de solos

In this paper studies based on Multilayer Perception Artificial Neural Network and Least Square Support Vector Machine (LS-SVM) techniques are applied to determine of the concentration of Soil Organic Matter (SOM). Performances of the techniques are compared. SOM concentrations and spectral data from Mid-Infrared are used as input parameters for both techniques. Multivariate regressions were performed for a set of 1117 spectra of soil samples, with concentrations ranging from 2 to 400 g kg-1. The LS-SVM resulted in a Root Mean Square Error of Prediction of 3.26 g kg-1 that is comparable to the deviation of the Walkley-Black method (2.80 g kg-1).

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Comparison between hydrographically conditioned digital elevation models in the morphometric charaterization of watersheds

The aim of this study was to compare the hydrographically conditioned digital elevation models (HCDEMs) generated from data of VNIR (Visible Near Infrared) sensor of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), of SRTM (Shuttle Radar Topography Mission) and topographical maps from IBGE in a scale of 1:50,000, processed in the Geographical Information System (GIS), aiming the morphometric characterization of watersheds. It was taken as basis the Sub-basin of São Bartolomeu River, obtaining morphometric characteristics from HCDEMs. Root Mean Square Error (RMSE) and cross validation were the statistics indexes used to evaluate the quality of HCDEMs. The percentage differences in the morphometric parameters obtained from these three different data sets were less than 10%, except for the mean slope (21%). In general, it was observed a good agreement between HCDEMs generated from remote sensing data and IBGE maps. The result of HCDEM ASTER was slightly higher than that from HCDEM SRTM. The HCDEM ASTER was more accurate than the HCDEM SRTM in basins with high altitudes and rugged terrain, by presenting frequency altimetry nearest to HCDEM IBGE, considered standard in this study.

Evaluation of thermorregulatory capacity of dairy buffaloes using infrared thermography

ABSTRACT The objective of this study was to evaluate the thermoregulatory response of dairy buffaloes in pre-milking and post-milking. To identify animal thermoregulatory capacity, skin surface temperatures were taken by an infrared thermometer (SST), a thermographic camera (MTBP) as well as respiratory rate records (RR). Black Globe and Humidity Index (BGHI), radiating thermal load (RTL) and enthalpy (H) were used to characterize the thermal environment. Artificial Neural Networks analyzed those indices as well as animal physiological data, using a single layer trained with the least mean square (LMS) algorithm. The results indicated that pre-milking and post-milking environments reached BGHI, RR, SST and MTBP values above thermal neutrality zone for buffaloes. In addition, limits of surface skin temperatures were mostly influenced by changing ambient conditions to the detriment of respiratory rates. It follows that buffaloes are sensitive to environmental changes and their skin temperatures are the best indicators of thermal comfort in relation to respiratory rate.

Fibrinonecrotic enteritis of piglets in a commercial farm: a postmortem study of the prevalence and the role of lesion associated agents Isospora suis and Clostridium perfringens

The objectives were to determine the prevalence of fibrinonecrotic enteritis (FNE) on a farrow-to-finish farm of 1,000 sows, to categorize the pathological changes, and to to investigate the lesion associated agents Isospora suis and Clostridium perfringens. Causes of preweaning mortality (PWM) were classified into 8 categories including FNE. Obtained data were evaluated for statistical significance by adjusted Chi-square analysis. Samples of FNE were taken for complementary studies including a PCR technique for genotyping toxin genes of Clostridium perfringens from gut samples fixed in 10% neutral formalin. From 3,153 piglets examined, less than 1% was classified as FNE. FNE prevalence increased progressively from the first to the third week, the last differing statistically from the others. Eighty percent of gut samples with FNE lesions were positive to Isospora suis, when examined by PCR from 9 severe FNE lesions detected 7 positive samples only for a toxin gene, characteristic of C. perfringens type-A.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional least-square method (LS). Among these the most important are: there are no restrictions on linearity or in the form which the parameters appears in the mathematical model describing the system, and it is not required that these parameters be time invariant. The EKF uses the statistical properties of the process and the observation noise, to produce estimates based on the mean square error of the estimates themselves. Differently, the LS minimizes a cost function based on the plant output behavior. Results for the estimation of some longitudinal aerodynamic derivatives from simulated data are presented.