Effects of dredging operations on sediment quality: contaminant mobilization in dredged sediments from the Port of Santos, SP, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the p(p)over-bar -> WZ+X cross section at root s=1.96 TeV and limits on WWZ trilinear gauge couplings

We present measurements of the process p (P) over bar -> WZ + X -> l 'nu(l ')l (l) over bar at root s = 1:96 TeV,where l and l ' are electrons or muons. Using 1 fb(-1) of data from the D0 experiment, we observe 13 candidates with an expected background of 4.5 +/- 0.6 events and measure a cross section sigma(WZ) = 2.7(-1.3)(+1.7) pb. From the number of observed events and the Z boson transverse momentum distribution, we limit the trilinear WWZ gauge couplings to -0: 17 <= lambda(Z) <= 0.21 (Delta k(Z) <= 0.29(lambda(Z) = 0) at the 95% C.L. for a form factor scale Lambda = 2 TeV. Further, assuming that Delta g(1)(Z) = Delta k(Z), we find -0.12 <= Delta k(Z) <= 0.29(lambda(Z) = 0) at the 95% C. L. These are the most restrictive limits on the WWZ couplings available to date.

Strength of the trilinear Higgs boson coupling in technicolor models

We discuss the strength of the trilinear Higgs boson coupling in technicolor (or composite) models in a model independent way. The coupling is determined as a function of a very general ansatz for the technicolor self-energy, and turns out to be equal or smaller than the one of the Standard Model Higgs boson depending on the dynamics of the theory. (c) 2006 Published by Elsevier B.V.

Limits on anomalous trilinear gauge couplings from WW -> e(+)e(-), WW -> e(+/-)mu(-/+), and WW ->mu(+)mu(-) events from pp collisions at root s=1.96 TeV

Limits are set on anomalous WW gamma and WWZ trilinear gauge couplings using W+W--> e(+)nu(e)e(-)(nu) over bar (e), W+W--> e(+/-)nu(e)mu(-/+)nu(mu), and W+W-->mu(+)nu(mu)mu(-)(nu) over bar (mu) events. The data set was collected by the Run II D0 detector at the Fermilab Tevatron Collider and corresponds to approximately 250 pb(-1) of integrated luminosity at root s=1.96 TeV. Under the assumption that the WW gamma couplings are equal to the WWZ couplings and using a form factor scale of Lambda=2.0 TeV, the combined 95% C.L. one-dimensional coupling limits from all three channels are -0.32

Scalar bosons in minimal and ultraminimal technicolor: Masses, trilinear couplings, and widths

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of the WW Production Cross Section with Dilepton Final States in pp Collisions at s=1.96 TeV and Limits on Anomalous Trilinear Gauge Couplings

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measurement of trilinear gauge boson couplings from WW plus WZ -> lvjj events in p(p)over-bar collisions at root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

The strength of trilinear higgs boson coupling in composite models

In this work we discuss the strength of the trilinear Higgs boson coupling in composite models in a model independent way. The coupling is determined as a function of a very general ansatz for the fermionic self-energy, and turns out to be equal or smaller than the one of the Standard Model Higgs boson depending on the dynamics of the theory. © World Scientific Publishing Company.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.