Severity of Brazilian sickle cell disease patients: severity scores and feasibility of the Bayesian network model use

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Redes dinâmicas de sensores sem fio ZigBee para aplicações de monitoramento e controle

Pós-graduação em Engenharia Elétrica - FEB

Effect of steeping conditions (sulfur dioxide, lactic acid, and temperature) on starch yield, starch quality, and germ quality from the Intermittent Milling and Dynamic Steeping process (IMDS) for a Brazilian corn hybrid

Effect of lactic acid, SO2, temperature, and their interactions were assessed on the dynamic steeping of a Brazilian dent corn (hybrid XL 606) to determine the ideal relationship among these variables to improve the wet-milling process for starch and corn by-products production. A 2x2x3 factorial experimental design was used with SO2 levels of 0.05 and 0.1% (w/v), lactic acid levels of 0 and 0.5% (v/v), and temperatures of 52, 60, and 68degreesC. Starch yield was used as deciding factor to choose the best treatment. Lactic acid added in the steep solution improved the starch yield by an average of 5.6 percentage points. SO2 was more available to break down the structural protein network at 0.1% than at the 0.05% level. Starch-gluten separation was difficult at 68degreesC. The lactic acid and SO2 concentrations and steeping temperatures for better starch recovery were 0.5, 0.1, and 52degreesC, respectively. The Intermittent Milling and Dynamic Steeping (IMDS) process produced, on average, 1.4% more starch than the conventional 36- hr steeping process. Protein in starch, oil content in germ, and germ damage were used as quality factors. Total steep time can be reduced from 36 hr for conventional wet-milling to 8 hr for the IMDS process.

The structure of a native L-amino acid oxidase, the major component of the Vipera ammodytes ammodytes venomic, reveals dynamic active site and quaternary structure stabilization by divalent ions

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

Estimation of the volumetric oxygen tranfer coefficient (KLa) from the gas balance and using a neural network technique

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

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Branch and bound algorithm for transmission network expansion planning using DC model

This paper presents an algorithm to solve the network transmission system expansion planning problem using the DC model which is a mixed non-linear integer programming problem. The major feature of this work is the use of a Branch-and-Bound (B&B) algorithm to directly solve mixed non-linear integer problems. An efficient interior point method is used to solve the non-linear programming problem at each node of the B&B tree. Tests with several known systems are presented to illustrate the performance of the proposed method. ©2007 IEEE.

Distribution network planning using a constructive heuristic algorithm

An optimization technique to solve distribution network planning (DNP) problem is presented. This is a very complex mixed binary nonlinear programming problem. A constructive heuristic algorithm (CHA) aimed at obtaining an excellent quality solution for this problem is presented. In each step of the CHA, a sensitivity index is used to add a circuit or a substation to the distribution network. This sensitivity index is obtained solving the DNP problem considering the numbers of circuits and substations to be added as continuous variables (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an efficient nonlinear optimization solver. A local improvement phase and a branching technique were implemented in the CHA. Results of two tests using a distribution network are presented in the paper in order to show the ability of the proposed algorithm. ©2009 IEEE.

A specialized genetic algorithm to solve the short term transmission network expansion planning

In this paper, the short term transmission network expansion planning (STTNEP) is solved through a specialized genetic algorithm (SGA). A complete AC model of the transmission network is used, which permits the formulation of an integrated power system transmission network expansion planning problem (real and reactive power planning). The characteristics of the proposed SGA to solve the STTNEP problem are detailed and an interior point method is employed to solve nonlinear programming problems during the solution steps of the SGA. Results of tests carried out with two electrical energy systems show the capabilities of the SGA and also the viability of using the AC model to solve the STTNEP problem. © 2009 IEEE.

Specialized genetic algorithm for transmission network expansion planning considering reliability

This paper presents a methodology to solve the transmission network expansion planning problem (TNEP) considering reliability and uncertainty in the demand. The proposed methodology provides an optimal expansion plan that allows the power system to operate adequately with an acceptable level of reliability and in an enviroment with uncertainness. The reliability criterion limits the expected value of the reliability index (LOLE - Loss Of Load Expectation) of the expanded system. The reliability is evaluated for the transmission system using an analytical technique based in enumeration. The mathematical model is solved, in a efficient way, using a specialized genetic algorithm of Chu-Beasley modified. Detailed results from an illustrative example are presented and discussed. © 2009 IEEE.

Synchronization enhancement via an oscillatory bath in a network of self-excited cells

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Single-cell expression profiling reveals a dynamic state of cardiac precursor cells in the early mouse embryo

In the early vertebrate embryo, cardiac progenitor/precursor cells (CPs) give rise to cardiac structures. Better understanding their biological character is critical to understand the heart development and to apply CPs for the clinical arena. However, our knowledge remains incomplete. With the use of single-cell expression profiling, we have now revealed rapid and dynamic changes in gene expression profiles of the embryonic CPs during the early phase after their segregation from the cardiac mesoderm. Progressively, the nascent mesodermal gene Mesp1 terminated, and Nkx2-5+/Tbx5+ population rapidly replaced the Tbx5low+ population as the expression of the cardiac genes Tbx5 and Nkx2-5 increased. At the Early Headfold stage, Tbx5-expressing CPs gradually showed a unique molecular signature with signs of cardiomyocyte differentiation. Lineage-tracing revealed a developmentally distinct characteristic of this population. They underwent progressive differentiation only towards the cardiomyocyte lineage corresponding to the first heart field rather than being maintained as a progenitor pool. More importantly, Tbx5 likely plays an important role in a transcriptional network to regulate the distinct character of the FHF via a positive feedback loop to activate the robust expression of Tbx5 in CPs. These data expands our knowledge on the behavior of CPs during the early phase of cardiac development, subsequently providing a platform for further study.