Causas de variação nos preços de bovinos nelore elite no Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise multivariada e variabilidade espacial na estimativa da erodibilidade de um argissolo vermelho-amarelo

A erodibilidade é um fator de extrema importância na caracterização da perda de solo, representando os processos que regulam a infiltração de água e sua resistência à desagregação e o transporte de partículas. Assim, por meio da análise de dependência espacial dos componentes principais da erodibilidade (fator K), objetivou-se estimar a erodibilidade do solo em uma área de nascentes da microbacia do Córrego do Tijuco, Monte Alto-SP, e analisar a variabilidade espacial das variáveis granulométricas do solo ao longo do relevo. A erodibilidade média da área foi considerada alta, e a análise de agrupamento k-means apontou para uma formação de cinco grupos: no primeiro, os altos teores de areia grossa (AG) e média (AM) condicionaram sua distribuição nas áreas planas; o segundo, caracterizado pelo alto teor de areia fina (AF), distribui-se nos declives mais convexos; o terceiro, com altos teores de silte e areia muito fina (AMF), concentrou-se nos maiores declives e concavidades; o quarto, com maior teor de argila, seguiu as zonas de escoamento de água; e o quinto, com alto teor de matéria orgânica (MO) e areia grossa (AG), distribui-se nas proximidades da zona urbana. A análise de componentes principais (ACP) mostrou quatro componentes com 87,4 % das informações, sendo o primeiro componente principal (CP1) discriminado pelo transporte seletivo de partículas principalmente em zonas pontuais de maior declividade e acúmulo de sedimentos; o segundo (CP2), discriminado pela baixa coesão entre as partículas, mostra acúmulo da areia fina nas áreas de menor cota em toda a área de concentração de água; o terceiro (CP3), discriminado pela maior agregação do solo, concentra-se principalmente nas bases de grandes declives; e o quarto (CP4), discriminado pela areia muito fina, distribui-se ao longo das declividades nas maiores altitudes. Os resultados sugerem o comportamento granulométrico do solo, que se mostra suscetível ao processo erosivo devido às condições texturais superficiais e à movimentação do relevo.

The variability and predictors of quality of AIDS care services in Brazil

Background: Since establishing universal free access to antiretroviral therapy in 1996, the Brazilian Health System has increased the number of centers providing HIV/AIDS outpatient care from 33 to 540. There had been no formal monitoring of the quality of these services until a survey of 336 AIDS health centers across 7 Brazilian states was undertaken in 2002. Managers of the services were asked to assess their clinics according to parameters of service inputs and service delivery processes. This report analyzes the survey results and identifies predictors of the overall quality of service delivery.Methods: The survey involved completion of a multiple-choice questionnaire comprising 107 parameters of service inputs and processes of delivering care, with responses assessed according to their likely impact on service quality using a 3-point scale. K-means clustering was used to group these services according to their scored responses. Logistic regression analysis was performed to identify predictors of high service quality.Results: The questionnaire was completed by 95.8% (322) of the managers of the sites surveyed. Most sites scored about 50% of the benchmark expectation. K-means clustering analysis identified four quality levels within which services could be grouped: 76 services (24%) were classed as level 1 (best), 53 (16%) as level 2 (medium), 113 (35%) as level 3 (poor), and 80 (25%) as level 4 (very poor). Parameters of service delivery processes were more important than those relating to service inputs for determining the quality classification. Predictors of quality services included larger care sites, specialization for HIV/AIDS, and location within large municipalities.Conclusion: The survey demonstrated highly variable levels of HIV/AIDS service quality across the sites. Many sites were found to have deficiencies in the processes of service delivery processes that could benefit from quality improvement initiatives. These findings could have implications for how HIV/AIDS services are planned in Brazil to achieve quality standards, such as for where service sites should be located, their size and staffing requirements. A set of service delivery indicators has been identified that could be used for routine monitoring of HIV/AIDS service delivery for HIV/AIDS in Brazil (and potentially in other similar settings).

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Ajuste de modelos não lineares e estimativas de parâmetros genéticos para produção de ovos de uma linhagem de poedeiras White Leghorn

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Metodologias para reconhecimento de padrões em sistemas SHM utilizando a técnica da Impedância Eletromecânica (E/M)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fibroblastos associados ao câncer e correlação com parãmetros patológicos em melanomas cutâneos caninos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise espacial e temporal de atributos do solo e da produtividade das culturas visando manejo específico em áreas agrícolas

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biogeographic Distribution Patterns and Their Correlates in the Diverse Frog Fauna of the Atlantic Forest Hotspot

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

Speech perception and cortical auditory evoked potentials in cochlear implant users with auditory neuropathy spectrum disorders

Objective: To characterize the PI component of long latency auditory evoked potentials (LLAEPs) in cochlear implant users with auditory neuropathy spectrum disorder (ANSD) and determine firstly whether they correlate with speech perception performance and secondly whether they correlate with other variables related to cochlear implant use. Methods: This study was conducted at the Center for Audiological Research at the University of Sao Paulo. The sample included 14 pediatric (4-11 years of age) cochlear implant users with ANSD, of both sexes, with profound prelingual hearing loss. Patients with hypoplasia or agenesis of the auditory nerve were excluded from the study. LLAEPs produced in response to speech stimuli were recorded using a Smart EP USB Jr. system. The subjects' speech perception was evaluated using tests 5 and 6 of the Glendonald Auditory Screening Procedure (GASP). Results: The P-1 component was detected in 12/14 (85.7%) children with ANSD. Latency of the P-1 component correlated with duration of sensorial hearing deprivation (*p = 0.007, r = 0.7278), but not with duration of cochlear implant use. An analysis of groups assigned according to GASP performance (k-means clustering) revealed that aspects of prior central auditory system development reflected in the P-1 component are related to behavioral auditory skills. Conclusions: In children with ANSD using cochlear implants, the P-1 component can serve as a marker of central auditory cortical development and a predictor of the implanted child's speech perception performance. (c) 2012 Elsevier Ireland Ltd. All rights reserved.

Estimation of scots pine defoliation by the common pine sawfly (Diprion pini L.) using multi-temporal radar data

In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multi-temporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine (Pinus sylvestris L.) defoliation. Three methods were tested: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, I assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, >20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates.

Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).

EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study

In young, first-episode, productive, medication-naive patients with schizophrenia, EEG microstates (building blocks of mentation) tend to be shortened. Koenig et al. [Koenig, T., Lehmann, D., Merlo, M., Kochi, K., Hell, D., Koukkou, M., 1999. A deviant EEG brain microstate in acute, neuroleptic-naïve schizophrenics at rest. European Archives of Psychiatry and Clinical Neuroscience 249, 205–211] suggested that shortening concerned specific microstate classes. Sequence rules (microstate concatenations, syntax) conceivably might also be affected. In 27 patients of the above type and 27 controls, from three centers, multichannel resting EEG was analyzed into microstates using k-means clustering of momentary potential topographies into four microstate classes (A–D). In patients, microstates were shortened in classes B and D (from 80 to 70 ms and from 94 to 82 ms, respectively), occurred more frequently in classes A and C, and covered more time in A and less in B. Topography differed only in class B where LORETA tomography predominantly showed stronger left and anterior activity in patients. Microstate concatenation (syntax) generally were disturbed in patients; specifically, the class sequence A→C→D→A predominated in controls, but was reversed in patients (A→D→C→A). In schizophrenia, information processing in certain classes of mental operations might deviate because of precocious termination. The intermittent occurrence might account for Bleuler's “double bookkeeping.” The disturbed microstate syntax opens a novel physiological comparison of mental operations between patients and controls.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.