Metodologias estatísticas aplicadas a dados de análises químicas da água produzida em um campo maduro de petróleo

Produced water is characterized as one of the most common wastes generated during exploration and production of oil. This work aims to develop methodologies based on comparative statistical processes of hydrogeochemical analysis of production zones in order to minimize types of high-cost interventions to perform identification test fluids - TIF. For the study, 27 samples were collected from five different production zones were measured a total of 50 chemical species. After the chemical analysis was applied the statistical data, using the R Statistical Software, version 2.11.1. Statistical analysis was performed in three steps. In the first stage, the objective was to investigate the behavior of chemical species under study in each area of production through the descriptive graphical analysis. The second step was to identify a function that classify production zones from each sample, using discriminant analysis. In the training stage, the rate of correct classification function of discriminant analysis was 85.19%. The next stage of processing of the data used for Principal Component Analysis, by reducing the number of variables obtained from the linear combination of chemical species, try to improve the discriminant function obtained in the second stage and increase the discrimination power of the data, but the result was not satisfactory. In Profile Analysis curves were obtained for each production area, based on the characteristics of the chemical species present in each zone. With this study it was possible to develop a method using hydrochemistry and statistical analysis that can be used to distinguish the water produced in mature fields of oil, so that it is possible to identify the zone of production that is contributing to the excessive elevation of the water volume.

Insights into population structure of Diplodus vulgaris along the SW Portuguese coast from otolith elemental signatures

The common two-banded sea bream (Diplodus vulgaris) is an important fish in the marine ecosystems of the NW Atlantic and Mediterranean. In southern Portugal it is a major fishery resource being targeted mainly by the artisanal fleets. Although there is some knowledge of the age, growth and reproductive biology of the species, information about its population structure is scarce and somewhat limited to the Mediterranean Sea. In this study the otolith elemental signatures of 90 specimens of D. vulgaris of the same age group (2+ years) and cohort collected from the important fishery regions of SW Portugal (Sesimbra, Sagres and Faro) have been analysed by inductively coupled plasma mass spectrometry (ICP-MS). Two different methodologies have been applied: solution based analysis of the whole otoliths; representative of the entire life-history prior to capture, and laser ablation analysis of otolith cores; representative of the larval and early post-settlement phase. Whole otolith comparisons utilised Sr/Ca, Ba/Ca, Mn/Ca, Li/Ca and Ni/Ca to demonstrate regional population structure. Classification accuracy rates from linear discriminant function analyses (LDFA) of whole otolith chemistry data were high for each region; Faro - 93%, Sagres - 90% and Sesimbra - 80%. Comparison of the otolith core chemistry utilised Sr/Ca, Ba/Ca, Mn/Ca and Mg/Ca and Zn/Ca. LDFA for the otolith core chemistry also achieved accurate classification for samples from Sesimbra (73%), but there was high overlap of otolith chemistry between samples from Faro and Sagres (47 and 43% classification accuracy respectively). The whole otolith results suggest that D. vulgaris are resident in the regional fishing areas during the juvenile phase. Both the core and whole otolith chemistry data supported separation of the Sesimbra fishery region from the more southern and closely associated Faro and Sagres regions for management purposes. However, while the whole otolith data indicated that the populations at Faro and Sagres likely remained separated in the juvenile stage, the otolith core chemistry data was inconclusive as to whether recruitment to these two areas was derived, or not, from different spawning areas.

Fractional Order Modeling and Control of a Flexible Satellite

Researchers have engrossed fractional-order modeling because of its ability to capture phenomena that are nearly impossible to describe owing to its long-term memory and inherited properties. Motivated by the research in fractional modeling, a fractional-order prototype for a flexible satellite whose dynamics are governed by fractional differential equations is proposed for the first time. These relations are derived using fractional attitude dynamic description of rigid body simultaneously coupled with the fractional Lagrange equation that governs the vibration of the appendages. Two attitude controls are designed in the presence of the faults and uncertainties of the system. The first is the fractional-order feedback linearization controller, in which the stability of the internal dynamics of the system is proved. The second is the fractional-order sliding mode control, whose asymptotic stability is demonstrated using the quadratic Lyapunov function. Several nonlinear simulations are implemented to analyze the performance of the proposed controllers.

The third order Melnikov function of a quadratic center under quadratic perturbation

We study quadratic perturbations of the integrable system (1+x)dH; where H =(x²+y²)=2: We prove that the first three Melnikov functions associated to the perturbed system give rise at most to three limit cycles.

Altered endothelial function following the Fontan procedure

Objective: Thrombosis has been widely described after the Fontan procedure. The vascular endothelium plays a central role in the control of coagulation and fibrinolysis. The aim of this study was to investigate if patients undergoing a modified Fontan procedure have impaired endothelial function and fibrinolysis in the late postoperative course. Patients and methods: We compared 23 patients aged from 7 to 26 years with age-matched healthy volunteers, collecting blood samples prior to and following standardized venous occlusion testing. Plasma levels of von Willebrand factor antigen, tissue-type plasminogen activator antigen, plasminogen activator inhibitor-1, and D-dimer were measured with enzyme-linked immunosorbent assay. Results: We found increased plasma levels of von Willebrand factor antigen in patients when compared to controls (p = 0.003). At the basal condition, concentrations of tissue-type plasminogen activator antigen and plasminogen activator inhibitor-1 antigen in the plasma, as well as their activity, were not significantly different between patients and controls. Following venous occlusion, concentrations of tissue-type plasminogen activator antigen in the plasma were significantly increased both in patients and controls, compared to pre-occlusion values. D-dimer was within the reference range. Multivariate discriminant analysis differentiated patients and their controls on the basis of differences for plasminogen activator inhibitor-1 and von Willebrand factor antigen (p = 0.0016). Conclusions: Our data suggest that patients with the Fontan circulation may have endothelial dysfunction, as indicated by raised levels of von Willebrand factor. Fibrinolysis seems to be relatively preserved, as suggested by appropriate response to venous occlusion.

A monolithic smoothing-gap algorithm for contact-impact based on the signed distance function

A new algorithm has been developed for smoothing the surfaces in finite element formulations of contact-impact. A key feature of this method is that the smoothing is done implicitly by constructing smooth signed distance functions for the bodies. These functions are then employed for the computation of the gap and other variables needed for implementation of contact-impact. The smoothed signed distance functions are constructed by a moving least-squares approximation with a polynomial basis. Results show that when nodes are placed on a surface, the surface can be reproduced with an error of about one per cent or less with either a quadratic or a linear basis. With a quadratic basis, the method exactly reproduces a circle or a sphere even for coarse meshes. Results are presented for contact problems involving the contact of circular bodies. Copyright (C) 2002 John Wiley Sons, Ltd.

Single period Markowitz portfolio selection, performance gauging and duality: a variation on Luenberger's shortage function

Markowitz portfolio theory (1952) has induced research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single period portfolio selection from a theoretical perspective and generalises currently used efficiency measures into the full mean-variance space. Therefore, we introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency. Furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function thus provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimisation.

Pédiatrie 1. Formule Quadratique: une nouveauté pédiatrique et pratique pour estimer le taux de filtration glomérulaire [Quadratic formula: a new pediatric advance for glomerular filtration rate estimation].

A new formula for glomerular filtration rate estimation in pediatric population from 2 to 18 years has been developed by the University Unit of Pediatric Nephrology. This Quadratic formula, accessible online, allows pediatricians to adjust drug dosage and/or follow-up renal function more precisely and in an easy manner.

Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.

Measure Fields for Function Approximation

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

The effect of adding stinging nettle (Urtica dioica) haylage to a total mixed ration on performance and rumen function of lactating dairy cows

In vitro studies found that inclusion of dried stinging nettle (Urtica dioica) at 100 mg/g dry matter (DM) increased the pH of a rumen fluid inoculated fermentation buffer by 30% and the effect was persistent for 7 days. Our objective was to evaluate the effects of adding stinging nettle haylage to a total mixed ration on feed intake, eating and rumination activity, rumen pH, milk yield, and milk composition of lactating dairy cows. Six lactating Holstein-Friesian cows were used in a replicated 3 × 3 Latin Square design experiment with 3 treatments and 3 week periods. Treatments were a control (C) high-starch (311 g/kg DM) total mixed ration diet and two treatment diets containing 50 (N5) and 100 (N10) g nettle haylage (DM/kg) as a replacement for ryegrass silage (Lolium perenne). There was an increase (linear, P < 0.010) in the proportion of large particles and a reduction in medium (linear, P = 0.045) and fine particles (linear, P = 0.026) in the diet offered with increasing nettle inclusion. A numerical decrease (linear, P = 0.106) in DM intake (DMI) was observed as nettle inclusion in the diet increased. Milk yield averaged 20.3 kg/day and was not affected by diet. There was a decrease (quadratic, P = 0.01) in the time animals spent ruminating as nettle inclusion in the diet increased, in spite of an increase in the number of boli produced daily for the N5 diet (quadratic, P = 0.031). Animals fed the N10 diet spent less time with a rumen pH below 5.5 (P < 0.05) than cows fed the N5 diet. Averaged over an 8.5 h sampling period, there were no changes in the concentration or proportions of acetate or propionate in the rumen, but feeding nettle haylage reduced the concentrations of n-butyrate (quadratic, P < 0.001), i-butyrate (linear, P < 0.009) and n-caproate (linear, P < 0.003). Milk and fat and protein corrected milk yield were not affected when nettles replaced ryegrass silage in the diet of lactating dairy cows, despite a numerical reduction in feed intake. Rumination activity was reduced by the addition of nettle haylage to the diet, which may reflect differences in fibre structure between the nettle haylage and ryegrass silage fed. Changes observed in rumen pH suggest potential benefits of feeding nettle haylage for reducing rumen acidosis. However, the extent to which these effects were due to the fermentability and structure of the nettle haylage compared to the ryegrass silage fed, or a bioactive component of the nettles, is not certain

Comparing performance of instrumental drift correction by linear and quadratic adjusting in inductive electromagnetic data

Electromagnetic induction (EMI) method results are shown for vertical magnetic dipole (VMD) configuration by using the EM38 equipment. Performance in the location of metallic pipes and electrical cables is compared as a function of instrumental drift correction by linear and quadratic adjusting under controlled conditions. Metallic pipes and electrical cables are buried at the IAG/USP shallow geophysical test site in Sao Paulo City. Brazil. Results show that apparent electrical conductivity and magnetic susceptibility data were affected by ambient temperature variation. In order to obtain better contrast between background and metallic targets it was necessary to correct the drift. This correction was accomplished by using linear and quadratic relation between conductivity/susceptibility and temperature intending comparative studies. The correction of temperature drift by using a quadratic relation was effective, showing that all metallic targets were located as well deeper targets were also improved. (C) 2010 Elsevier B.V. All rights reserved.