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.

Estudo da estrutura e da solvatação do HNP-3, um antibiótico natural, por dinâmica molecular

The structure and hydration of the HNP-3 have been derived from molecular dynamics data using root mean square deviation, radial and energy distributions. Three antiparallel beta sheets were found to be preserved. 15 intramolecular hydrogen bonds were identified together with 36 hydrogen bonds on the backbone and 35 on the side chain atoms. From the point of view of the hydration dynamics, the analysis shows a high solvent accessibility of the monomer and attractive interactions with water molecules.

REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Production of baruzeiro seedling in different protected environments and substrates

The high seedlings quality is essential for deployment of homogeneous orchards. This study evaluated the baruzeiro (Dipteryx alata Vog) seedlings formation on different substrates within protected environments. It was used substrates with100% of cattle manure; 100% of cassava stems; 100% of vermiculite; 50% of cattle manure + 50% of cassava stems; 50% of cattle manure + 50% of vermiculite; 50% of cassava stems + 50% of vermiculite; and + ⅓ of cattle manure + ⅓ of cassava stems + ⅓ of vermiculite. These substrates were tested in protected areas: greenhouse; black shade net of 50% shading; and aluminized thermo-reflective screen of 50% shading. A completely randomized experimental design with five replicates of four plants was adopted. Initially, data were submitted to analysis of individual variance of the substrates, in each environment of cultivation, then performing the evaluation of the residual mean square and the analysis of these environments together for comparison. The best substrate for baruzeiro seedlings was pure vermiculite. The substrates with 100% of manure and the substrate with 33.33% of the mixed studied materials can be used for seedlings formation. The environment with screen can be indicated for the production of baruzeiro seedlings, since it gave vigor to the seedlings.

Seedlings of Acrocomia aculeata in diferent substrates and protected environments

The seedling production stage is the key to achieve uniformity in tree breeding stage. This study evaluated "bocaiúva" (Acrocomia aculeata) seedling formation, with pre-germinated seeds in different substrates and protected environments, in the University of Mato Grosso do Sul State, Aquidauana, MS. As substrates, we used 100% cattle manure (M), 100% cassava branches (CB), 100% vermiculite (V), 50% cattle manure + 50% cassava branches, 50 % cattle manure + 50% vermiculite, 50% cassava branches + 50% vermiculite and ⅓ cattle manure + ⅓ cassava branches + ⅓ vermiculite. These substrates were tested in a greenhouse covered with 150 µm low density polyethylene (LDPE) film under thermo-reflective screen with 50% shading under film; black screen with 50% shading on the sides; black monofilament screen with 50% shading set on roof and sides; and aluminized thermo- reflective screen with 50% shading set on roof and sides. The completely randomized experimental design with 5 replications of 5 plants each was adopted. Initially, data were submitted to analysis of substrate individual variance in each growing environment, then performing the waste mean square evaluation and their environment joint analysis for comparison. The best growing environment is the thermo-reflective screen compared to LDPE greenhouse and black screen set. All substrates containing manure are recommended for bocaiúva seedlings formation. The pure cassava branch is not indicated for seedling, even using chemical fertilizer.

Methodology for spatialization of intense rainfall equation parameters

The aim of this study was to generate maps of intense rainfall equation parameters using interpolated maximum intense rainfall data. The study area comprised Espírito Santo State, Brazil. A total of 59 intense rainfall equations were used to interpolate maximum intense rainfall, with a 1 x 1 km spatial resolution. Maximum intense rainfall was interpolated considering recurrence of 2; 5; 10; 20; 50 and 100 years, and duration of 10; 20; 30; 40; 50; 60; 120; 240; 360; 420; 660; 720; 900; 1,140; 1,380 and 1,440 minutes, resulting in 96 maps of maximum intense rainfall. The used interpolators were inverse distance weighting and ordinary kriging, for which significance level (p-value) and coefficient of determination (R²) were evaluated for the cross-validation data, choosing the method that presented better R² to generate maps. Finally, maps of maximum intense precipitation were used to estimate, cell by cell, the intense rainfall equation parameters. In comparison with literature data, the mean percentage error of estimated intense rainfall equations was 13.8%. Maps of spatialized parameters, obtained in this study, are of simple use; once they are georeferenced, they may be imported into any geographic information system to be used for a specific area of interest.

Hydrological simulation using SWAT model in headwater basin in Southeast Brazil

Hydrological models are important tools that have been used in water resource planning and management. Thus, the aim of this work was to calibrate and validate in a daily time scale, the SWAT model (Soil and Water Assessment Tool) to the watershed of the Galo creek , located in Espírito Santo State. To conduct the study we used georeferenced maps of relief, soil type and use, in addition to historical daily time series of basin climate and flow. In modeling were used time series corresponding to the periods Jan 1, 1995 to Dec 31, 2000 and Jan 1, 2001 to Dec 20, 2003 for calibration and validation, respectively. Model performance evaluation was done using the Nash-Sutcliffe coefficient (E NS) and the percentage of bias (P BIAS). SWAT evaluation was also done in the simulation of the following hydrological variables: maximum and minimum annual daily flowsand minimum reference flows, Q90 and Q95, based on mean absolute error. E NS and P BIAS were, respectively, 0.65 and 7.2% and 0.70 and 14.1%, for calibration and validation, indicating a satisfactory performance for the model. SWAT adequately simulated minimum annual daily flow and the reference flows, Q90 and Q95; it was not suitable in the simulation of maximum annual daily flows.

Evaluation of thermorregulatory capacity of dairy buffaloes using infrared thermography

ABSTRACT The objective of this study was to evaluate the thermoregulatory response of dairy buffaloes in pre-milking and post-milking. To identify animal thermoregulatory capacity, skin surface temperatures were taken by an infrared thermometer (SST), a thermographic camera (MTBP) as well as respiratory rate records (RR). Black Globe and Humidity Index (BGHI), radiating thermal load (RTL) and enthalpy (H) were used to characterize the thermal environment. Artificial Neural Networks analyzed those indices as well as animal physiological data, using a single layer trained with the least mean square (LMS) algorithm. The results indicated that pre-milking and post-milking environments reached BGHI, RR, SST and MTBP values above thermal neutrality zone for buffaloes. In addition, limits of surface skin temperatures were mostly influenced by changing ambient conditions to the detriment of respiratory rates. It follows that buffaloes are sensitive to environmental changes and their skin temperatures are the best indicators of thermal comfort in relation to respiratory rate.

Ovarian and uterine periovulatory Doppler ultrasonography in bitches

This paper aims to describe the uterine and ovarian ultrasonographic characteristics and Doppler velocimetric features of their arteries in bitches during the periovulatory period. Fifteen estrous cycles in 10 animals were evaluated. The ultrasonographic characteristics, resistance indices (RI) and pulsatility indices (PI) of the uterus and ovaries in each animal were recorded 5 days before and after ovulation (D0). The data were statistically analyzed, and the results were expressed as the mean ± standard error of mean (P<0.05). In results the ultrasonographic features of the uterus were the same on all of the cycles and evaluated days. The uterus had an average diameter of 0.85±0.02cm. An increase in the volume of the ovaries and the diameter of the ovarian follicles were measured. Ovaries had a volume of 0.64±0.06cm³, and the follicles cavities had a diameter of 0.46 ± 0.01 cm on the day of ovulation. After ovulation, it was observed that some follicles not collapse in some cycles. Two days prior to ovulation, the uterine blood perfusion decreased. This decrease remained unchanged until ovulation. Following ovulation, we measured a gradual increase in the uterine perfusion and in the ovarian artery. This artery directed blood flow to the ovaries and increased the intra-ovarian perfusion on the day after ovulation. In conclusion, specific features are observed in the uterus and ovarian ultrasound image and Doppler values of their arteries presented on the periovulatory days and when associated allow to estimate more accurately the date of ovulation.

Fracture modes and acoustic emission characteristics of hydrogen-assisted cracking in high-strength low-alloy steel weldment

The aim of the present paper is to study the relationship between the fracture modes in hydrogen-assisted cracking (HAC) in microalloied steel and the emission of acoustic signals during the fracturing process. For this reason, a flux-cored arc weld (FCAW) was used in a high-strength low-alloy steel. The consumable used were the commercially available AWS E120T5-K4 and had a diameter of 1.6 mm. Two different shielding gases were used (CO2 and CO2+5% H2) to obtain complete phenomenon characterization. The implant test was applied with three levels of restriction stresses. An acoustic emission measurement system (AEMS) was coupled to the implant test apparatus. The output signal from the acoustic emission sensor was passed through an electronic amplifier and processed by a root mean square (RMS) voltage converter. Fracture surfaces were examined by scanning electron microscopy (SEM) and image analysis. Fracture modes were related with the intensity, the energy and the number of the peaks of the acoustic emission signal. The shielding gas CO2+5% H2 proved to be very useful in the experiments. Basically, three different fracture modes were identified in terms of fracture appearance: microvoid coalescence (MVC), intergranular (IG) and quasi-cleavage (QC). The results show that each mode of fracture presents a characteristic acoustic signal.

Application of the finite element method in cold forging processes

The demand for more efficient manufacturing processes has been increasing in the last few years. The cold forging process is presented as a possible solution, because it allows the production of parts with a good surface finish and with good mechanical properties. Nevertheless, the cold forming sequence design is very empirical and it is based on the designer experience. The computational modeling of each forming process stage by the finite element method can make the sequence design faster and more efficient, decreasing the use of conventional "trial and error" methods. In this study, the application of a commercial general finite element software - ANSYS - has been applied to model a forming operation. Models have been developed to simulate the ring compression test and to simulate a basic forming operation (upsetting) that is applied in most of the cold forging parts sequences. The simulated upsetting operation is one stage of the automotive starter parts manufacturing process. Experiments have been done to obtain the stress-strain material curve, the material flow during the simulated stage, and the required forming force. These experiments provided results used as numerical model input data and as validation of model results. The comparison between experiments and numerical results confirms the developed methodology potential on die filling prediction.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Time course of changes in heart rate and blood pressure variability in streptozotocin-induced diabetic rats treated with insulin

Autonomic neuropathy is a frequent complication of diabetes associated with higher morbidity and mortality in symptomatic patients, possibly because it affects autonomic regulation of the sinus node, reducing heart rate (HR) variability which predisposes to fatal arrhythmias. We evaluated the time course of arterial pressure and HR and indirectly of autonomic function (by evaluation of mean arterial pressure (MAP) variability) in rats (164.5 ± 1.7 g) 7, 14, 30 and 120 days after streptozotocin (STZ) injection, treated with insulin, using measurements of arterial pressure, HR and MAP variability. HR variability was evaluated by the standard deviation of RR intervals (SDNN) and root mean square of successive difference of RR intervals (RMSSD). MAP variability was evaluated by the standard deviation of the mean of MAP and by 4 indices (P1, P2, P3 and MN) derived from the three-dimensional return map constructed by plotting MAPn x [(MAPn+1) - (MAPn)] x density. The indices represent the maximum concentration of points (P1), the longitudinal axis (P2), and the transversal axis (P3) and MN represents P1 x P2 x P3 x 10-3. STZ induced increased urinary glucose in diabetic (D) rats compared to controls (C). Seven days after STZ, diabetes reduced resting HR from 380.6 ± 12.9 to 319.2 ± 19.8 bpm, increased HR variability, as demonstrated by increased SDNN, from 11.77 ± 1.67 to 19.87 ± 2.60 ms, did not change MAP, and reduced P1 from 61.0 ± 5.3 to 51.5 ± 1.8 arbitrary units (AU), P2 from 41.3 ± 0.3 to 29.0 ± 1.8 AU, and MN from 171.1 ± 30.2 to 77.2 ± 9.6 AU of MAP. These indices, as well as HR and MAP, were similar for D and C animals 14, 30 and 120 days after STZ. Seven-day rats showed a negative correlation of urinary glucose with resting HR (r = -0.76, P = 0.03) as well as with the MN index (r = -0.83, P = 0.01). We conclude that rats with short-term diabetes mellitus induced by STZ presented modified autonomic control of HR and MAP which was reversible. The metabolic control may influence these results, suggesting that insulin treatment and a better metabolic control in this model may modify arterial pressure, HR and MAP variability

Evaluation of electromyographic activity and heart rate responses to isometric exercise. The role played by muscular mass and type

The purpose of the present study was to examine the relationship between the electromyographic (EMG) activity and heart rate (HR) responses induced by isometric exercise performed by knee extension (KE) and flexion (KF) in men. Fifteen healthy male subjects, 21 ± 1.3 years (mean ± SD), were submitted to KE and KF isometric exercise tests at 100% of maximal voluntary contraction (MVC). The exercises were performed with one leg (right or left) and with two legs simultaneously, for 10 s in the sitting position with the hip and knee flexed at 90o. EMG activity (root mean square values) and HR (beats/min) were recorded simultaneously both at rest and throughout the sustained contraction. The HR responses to isometric exercise in KE and KF were similar when performed with one and two legs. However, the HR increase was always significantly higher in KE than KF (P<0.05), whereas the EMG activity was higher in KE than in KF (P<0.05), regardless of the muscle mass (one or two legs) involved in the effort. The correlation coefficients between HR response and the EMG activity during KE (r = 0.33, P>0.05) and KF (r = 0.15, P>0.05) contractions were not significant. These results suggest that the predominant mechanism responsible for the larger increase in HR response to KE as compared to KF in our study could be dependent on qualitative and quantitative differences in the fiber type composition found in each muscle group. This mechanism seems to demand a higher activation of motor units with a corresponding increase in central command to the cardiovascular centers that modulate HR control.

Relative influence of age, resting heart rate and sedentary life style in short-term analysis of heart rate variability

In order to assess the relative influence of age, resting heart rate (HR) and sedentary life style, heart rate variability (HRV) was studied in two different groups. The young group (YG) consisted of 9 sedentary subjects aged 15 to 20 years (YG-S) and of 9 nonsedentary volunteers (YG-NS) also aged 15 to 20. The elderly sedentary group (ESG) consisted of 16 sedentary subjects aged 39 to 82 years. HRV was assessed using a short-term procedure (5 min). R-R variability was calculated in the time-domain by means of the root mean square successive differences. Frequency-domain HRV was evaluated by power spectrum analysis considering high frequency and low frequency bands. In the YG the effort tolerance was ranked in a bicycle stress test. HR was similar for both groups while ESG showed a reduced HRV compared with YG. Within each group, HRV displayed a negative correlation with HR. Although YG-NS had better effort tolerance than YG-S, their HR and HRV were not significantly different. We conclude that HRV is reduced with increasing HR or age, regardless of life style. The results obtained in our short-term study agree with others of longer duration by showing that age and HR are the main determinants of HRV. Our results do not support the idea that changes in HRV are related to regular physical activity.