Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Reduced hippocampal GABAergic function in Wistar audiogenic rats

Epilepsy is a neurological disorder associated with excitatory and inhibitory imbalance within the underlying neural network. This study evaluated inhibitory γ-amino-butyric acid (GABA)ergic modulation in the CA1 region of the hippocampus of male Wistar rats and Wistar audiogenic rats (aged 90 ± 3 days), a strain of inbred animals susceptible to audiogenic seizures. Field excitatory postsynaptic potentials and population spike complexes in response to Schaffer collateral fiber stimulation were recorded in hippocampal slices before and during application of picrotoxin (50 µM, 60 min), a GABA A antagonist, and the size of the population spike was quantified by measuring its amplitude and slope. In control audiogenic-resistant Wistar rats (N = 9), picrotoxin significantly increased both the amplitude of the population spike by 51 ± 19% and its maximum slope by 73 ± 21%. In contrast, in slices from Wistar audiogenic rats (N = 6), picrotoxin caused no statistically significant change in population spike amplitude (33 ± 46%) or slope (11 ± 29%). Data are reported as means ± SEM. This result indicates a functional reduction of GABAergic neurotransmission in hippocampal slices from Wistar audiogenic rats.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Cryptococcus neoformans isolation from swallow (Hirundo rustica) excreta in Iran

Cryptococcus neoformans is an encapsulated yeast that can cause cryptococcosis, a life-threatening infection that mainly occurs in immunocompromised patients. The major environmental sources of C. neoformans have been shown to be soil contaminated with avian droppings. In the present study, we evaluated the isolation of C. neoformans from swallow (Hirundo rustica) excreta in two northern cities of Iran. Ninety-seven swallow droppings were evaluated and 498 yeast-like colonies were isolated and identified as Rhodotorula spp. (62.8%), Candida spp. (28.5%)and C. neoformans (8.7%). Cryptococcus neoformans was isolated from 5/97 (5.2%) of collected samples. Min-Max colony forming units (CFU) per one gram for the positive samples were 3-10 C. neoformans colonies. The total mean CFU per one gram for the positive samples was 4.8. The results of this study demonstrate that excreta of swallow may harbor different species of potentially pathogenic yeasts, mainly C. neoformans, and may be capable of disseminating these fungi in the environment.

Pregnant crack addicts in a psychiatric unit

OBJECTIVE: In this study we aim to characterize a sample of 85 pregnant crack addicts admitted for detoxification in a psychiatric inpatient unit. METHOD: Cross-sectional study. Sociodemographic, clinical, obstetric and lifestyle information were evaluated. RESULTS: Age of onset for crack use varied from 11 to 35 years (median = 21). Approximately 25% of the patients smoked more than 20 crack rocks in a typical day of use (median = 10; min-max = 1-100). Tobacco (89.4%), alcohol (63.5%) and marijuana (51.8%) were the drugs other than crack most currently used. Robbery was reported by 32 patients (41.2%), imprisonment experience by 21 (24.7%), trade of sex for money/drugs by 38 (44.7%), home desertion by 33 (38.8%); 15.3% were positive for HIV, 5.9% for HCV, 1.2% for HBV and 8.2% for syphilis. After discharge from the psychiatric unit, only 25% of the sample followed the proposed treatment in the chemical dependency outpatient service. CONCLUSION: Greater risky behaviors for STD, as well as high rates of maternal HIV and Syphilis were found. Moreover, the high rates of concurrent use of other drugs and involvement in illegal activities contribute to show their chaotic lifestyles. Prevention and intervention programs need to be developed to address the multifactorial nature of this problem.

Integração de dados do quickbird e atributos do terreno no mapeamento digital de solos por redes neurais artificiais

No presente estudo, foi realizada uma avaliação de diferentes variáveis ambientais no mapeamento digital de solos em uma região no norte do Estado de Minas Gerais, utilizando redes neurais artificiais (RNA). Os atributos do terreno declividade e índice topográfico combinado (CTI), derivados de um modelo digital de elevação, três bandas do sensor Quickbird e um mapa de litologia foram combinados, e a importância de cada variável para discriminação das unidades de mapeamento foi avaliada. O simulador de redes neurais utilizado foi o "Java Neural Network Simulator", e o algoritmo de aprendizado, o "backpropagation". Para cada conjunto testado, foi selecionada uma RNA para a predição das unidades de mapeamento; os mapas gerados por esses conjuntos foram comparados com um mapa de solos produzido com o método convencional, para determinação da concordância entre as classificações. Essa comparação mostrou que o mapa produzido com o uso de todas as variáveis ambientais (declividade, índice CTI, bandas 1, 2 e 3 do Quickbird e litologia) obteve desempenho superior (67,4 % de concordância) ao dos mapas produzidos pelos demais conjuntos de variáveis. Das variáveis utilizadas, a declividade foi a que contribuiu com maior peso, pois, quando suprimida da análise, os resultados da concordância foram os mais baixos (33,7 %). Os resultados demonstraram que a abordagem utilizada pode contribuir para superar alguns dos problemas do mapeamento de solos no Brasil, especialmente em escalas maiores que 1:25.000, tornando sua execução mais rápida e mais barata, sobretudo se houver disponibilidade de dados de sensores remotos de alta resolução espacial a custos mais baixos e facilidade de obtenção dos atributos do terreno nos sistemas de informação geográfica (SIG).