Estimation of field capacity from ring infiltrometer-drainage data

Field capacity (FC) is a parameter widely used in applied soil science. However, its in situ method of determination may be difficult to apply, generally because of the need of large supplies of water at the test sites. Ottoni Filho et al. (2014) proposed a standardized procedure for field determination of FC and showed that such in situ FC can be estimated by a linear pedotransfer function (PTF) based on volumetric soil water content at the matric potential of -6 kPa [θ(6)] for the same soils used in the present study. The objective of this study was to use soil moisture data below a double ring infiltrometer measured 48 h after the end of the infiltration test in order to develop PTFs for standard in situ FC. We found that such ring FC data were an average of 0.03 m³ m- 3 greater than standard FC values. The linear PTF that was developed for the ring FC data based only on θ(6) was nearly as accurate as the equivalent PTF reported by Ottoni Filho et al. (2014), which was developed for the standard FC data. The root mean squared residues of FC determined from both PTFs were about 0.02 m³ m- 3. The proposed method has the advantage of estimating the soil in situ FC using the water applied in the infiltration test.

Rôle de l'EGFR dans le cancer pulmonaire non à petites cellules [The role of EGFR in non-small cell lung carcinoma]

EGFR receptor is expressed on most of the non small cell lung carcinoma (NSCLC) cells. Its relative importance in oncogenesis and tumour progression seems to greatly vary among NSCLC. Two molecules targeting differently EGFR are currently used for the treatment of metastatic NSCLC. cetuximab, a monoclonal antibody directed against the extracellular domain of the receptor, leads to a moderate survival benefit when associated with standard first-line chemotherapy. Erlotinib, a small EGFR tyrosine-kinase inhibitor molecule is used in 2nd or 3rd treatment line. Predictive factors for efficiency of these new treatments are subjects of intense research, in order to allow a better selection of the patients who could benefit from such a strategy.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Performance of 'Oneco' mandarin on six rootstocks in South Brazil

The objective of this work was to assess the effect of six rootstocks on yield, fruit quality, and growth of 'Oneco' mandarin during the first seven harvesting seasons, in Butiá, Rio Grande do Sul State, Brazil. The rootstocks evaluated were: 'Swingle' citrumelo (Citrus paradisi × Poncirus trifoliata), 'Caipira' orange (C. sinensis), 'Troyer' citrange (C. sinensis × P. trifoliata), 'Rangpur' lime (C. limonia), 'Volkamer' lemon (C. volkameriana), and 'Flying Dragon' trifoliata orange (P. trifoliata var. monstrosa). Plants budded onto 'Flying Dragon' had the lowest vegetative development, which indicates the dwarfing characteristics of this rootstock, and had the highest mean production efficiency, despite low yield. Plants grafted on 'Volkamer' lemon and 'Rangpur' lime had the highest alternate bearing. Under the experimental conditions evaluated, the most adequate rootstocks for mandarin 'Oneco' are 'Swingle' citrumelo and 'Troyer' citrange, regarding fruit yield and quality.

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.

Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

Quantile Estimation in Non-Stationary Streaming Data

We provide an incremental quantile estimator for Non-stationary Streaming Data. We propose a method for simultaneous estimation of multiple quantiles corresponding to the given probability levels from streaming data. Due to the limitations of the memory, it is not feasible to compute the quantiles by storing the data. So estimating the quantiles as the data pass by is the only possibility. This can be effective in network measurement. To provide the minimum of the mean-squared error of the estimation, we use parabolic approximation and for comparison we simulate the results for different number of runs and using both linear and parabolic approximations.

A priori parameterisation of the CERES soil-crop models and tests against several European data sets

Mechanistic soil-crop models have become indispensable tools to investigate the effect of management practices on the productivity or environmental impacts of arable crops. Ideally these models may claim to be universally applicable because they simulate the major processes governing the fate of inputs such as fertiliser nitrogen or pesticides. However, because they deal with complex systems and uncertain phenomena, site-specific calibration is usually a prerequisite to ensure their predictions are realistic. This statement implies that some experimental knowledge on the system to be simulated should be available prior to any modelling attempt, and raises a tremendous limitation to practical applications of models. Because the demand for more general simulation results is high, modellers have nevertheless taken the bold step of extrapolating a model tested within a limited sample of real conditions to a much larger domain. While methodological questions are often disregarded in this extrapolation process, they are specifically addressed in this paper, and in particular the issue of models a priori parameterisation. We thus implemented and tested a standard procedure to parameterize the soil components of a modified version of the CERES models. The procedure converts routinely-available soil properties into functional characteristics by means of pedo-transfer functions. The resulting predictions of soil water and nitrogen dynamics, as well as crop biomass, nitrogen content and leaf area index were compared to observations from trials conducted in five locations across Europe (southern Italy, northern Spain, northern France and northern Germany). In three cases, the model’s performance was judged acceptable when compared to experimental errors on the measurements, based on a test of the model’s root mean squared error (RMSE). Significant deviations between observations and model outputs were however noted in all sites, and could be ascribed to various model routines. In decreasing importance, these were: water balance, the turnover of soil organic matter, and crop N uptake. A better match to field observations could therefore be achieved by visually adjusting related parameters, such as field-capacity water content or the size of soil microbial biomass. As a result, model predictions fell within the measurement errors in all sites for most variables, and the model’s RMSE was within the range of published values for similar tests. We conclude that the proposed a priori method yields acceptable simulations with only a 50% probability, a figure which may be greatly increased through a posteriori calibration. Modellers should thus exercise caution when extrapolating their models to a large sample of pedo-climatic conditions for which they have only limited information.

Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches

We propose a new kernel estimation of the cumulative distribution function based on transformation and on bias reducing techniques. We derive the optimal bandwidth that minimises the asymptotic integrated mean squared error. The simulation results show that our proposed kernel estimation improves alternative approaches when the variable has an extreme value distribution with heavy tail and the sample size is small.

Start-up of horizontal anaerobic reactors with sludge blanket and fixed bed for wastewater treatment from coffee processing by wet method

In this study it was evaluated the start-up procedures of anaerobic treatment system with three horizontal anaerobic reactors (R1, R2 and R3), installed in series, with volume of 1.2 L each. R1 had sludge blanket, and R2 and R3 had half supporter of bamboo and coconut fiber, respectively. As an affluent, it was synthesized wastewater from mechanical pulping of the coffee fruit by wet method, with a mean value of total chemical oxygen demand (CODtotal) of 16,003 mg L-1. The hydraulic retention time (HRT) in each reactor was 30 h. The volumetric organic loading (VOL) applied in R1 varied from 8.9 to 25.0 g of CODtotal (L d)-1. The mean removal efficiencies of CODtotal varied from 43 to 97% in the treatment system (R1+R2+R3), stabilizing above 80% after 30 days of operation. The mean content of methane in the biogas were of 70 to 76%, the mean volumetric production was 1.7 L CH4 (L reactor d)-1 in the system, and the higher conversions were around at 0.20 L CH4 (g CODremoved)-1 in R1 and R2. The mean values of pH in the effluents ranged from 6.8 to 8.3 and the mean values of total volatile acids remained below 200 mg L-1 in the effluent of R3. The concentrations of total phenols of the affluent ranged from 45 to 278 mg L-1, and the mean removal efficiency was of 52%. The start-up of the anaerobic treatment system occurred after 30 days of operation as a result of inoculation with anaerobic sludge with active microbiota.

The effect of an aerobic training program on the electrical remodeling of the heart: high-frequency components of the signal-averaged electrocardiogram are predictors of the maximal aerobic power

Increased heart rate variability (HRV) and high-frequency content of the terminal region of the ventricular activation of signal-averaged ECG (SAECG) have been reported in athletes. The present study investigates HRV and SAECG parameters as predictors of maximal aerobic power (VO2max) in athletes. HRV, SAECG and VO2max were determined in 18 high-performance long-distance (25 ± 6 years; 17 males) runners 24 h after a training session. Clinical visits, ECG and VO2max determination were scheduled for all athletes during thew training period. A group of 18 untrained healthy volunteers matched for age, gender, and body surface area was included as controls. SAECG was acquired in the resting supine position for 15 min and processed to extract average RR interval (Mean-RR) and root mean squared standard deviation (RMSSD) of the difference of two consecutive normal RR intervals. SAECG variables analyzed in the vector magnitude with 40-250 Hz band-pass bi-directional filtering were: total and 40-µV terminal (LAS40) duration of ventricular activation, RMS voltage of total (RMST) and of the 40-ms terminal region of ventricular activation. Linear and multivariate stepwise logistic regressions oriented by inter-group comparisons were adjusted in significant variables in order to predict VO2max, with a P < 0.05 considered to be significant. VO2max correlated significantly (P < 0.05) with RMST (r = 0.77), Mean-RR (r = 0.62), RMSSD (r = 0.47), and LAS40 (r = -0.39). RMST was the independent predictor of VO2max. In athletes, HRV and high-frequency components of the SAECG correlate with VO2max and the high-frequency content of SAECG is an independent predictor of VO2max.

Estimating the Tobit Model with Serial Correlation: an Operational Approach

Several Authors Have Discussed Recently the Limited Dependent Variable Regression Model with Serial Correlation Between Residuals. the Pseudo-Maximum Likelihood Estimators Obtained by Ignoring Serial Correlation Altogether, Have Been Shown to Be Consistent. We Present Alternative Pseudo-Maximum Likelihood Estimators Which Are Obtained by Ignoring Serial Correlation Only Selectively. Monte Carlo Experiments on a Model with First Order Serial Correlation Suggest That Our Alternative Estimators Have Substantially Lower Mean-Squared Errors in Medium Size and Small Samples, Especially When the Serial Correlation Coefficient Is High. the Same Experiments Also Suggest That the True Level of the Confidence Intervals Established with Our Estimators by Assuming Asymptotic Normality, Is Somewhat Lower Than the Intended Level. Although the Paper Focuses on Models with Only First Order Serial Correlation, the Generalization of the Proposed Approach to Serial Correlation of Higher Order Is Also Discussed Briefly.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Altered sensory-weighting mechanisms is observed in adolescents with idiopathic scoliosis

Affiliation: Paul Allard : Département de kinésiologie, Université de Montréal