Serum biochemical parameters of female bronze Turkeys (Meleagris gallopavo) during egg-laying season

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

Immunological and clinical parameters of newcastle disease vaccination in bronze Turkeys (Meleagris gallopavo)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Hematological and incubation parameters of chicks from young breeders eggs: Variation with sex and incubation temperature

This experiment analyzed the effect of sex and incubation temperature on daily mass loss and eggshell conductance, embryo mortality rates, incubation duration, hematological parameters and body, liver, heart and bursa weights of neonatal chicks from young breeders. The daily mass loss was higher at incubation temperature of 39°C. The eggshell conductance rate increased with the temperature. The total and partial duration of incubation were lower for eggs incubated at 39°C. The time taken by the chick to leave the eggshell did not differ below and above the thermoneutral temperature. The total and intermediate embryo mortality rates increased with the incubation temperature, whereas the early and late embryo mortality rates were higher at incubation temperature of 39°C. Sex did not influence the analyzed parameters, while the incubation temperature did not affect the body and bursa weight and the erythrocytes characteristics. The liver weight of chicks incubated at 36°C was higher than the incubated at 39°C, however there were no differences among the liver weight from chicks incubated at 36 and 39°C and those incubated at 37.5°C. The number of heterophils and the heterophil/lymphocyte ratio (H/L ratio) increased following the temperature, whereas the number of lymphocytes decreased at high temperatures. The other leukocyte parameters did not suffer influence of temperature. Males and females presented similar response to variation of incubation temperatures (36, 37.5 and 39°C) and demonstrated higher sensibility to temperatures above the thermoneutral. Moreover, temperatures below the thermoneutral demonstrated to be better for improvement of hatchability and development of chicks from light eggs. © Asian Network for Scientific Information, 2010.

Determination of broiler femur parameters at different growth phases

The objective of this experiment was to analyse macroscopically the femur and determine the biochemical values at 8, 22 and 42 days of age, producing basic results that can help to understand the pathogeny of the locomotor problems and the broiler physiologic growth. A total of 60 Cobb male broilers were distributed in three age groups (8, 22 and 42 days of age) of 20 birds. All macroscopic measurements, except the cranial compact layer increased (p<0.05) over the course of the ages. The cranial compact layer presented the biggest measure at 22 days of age. The ash percentage increased (p<0.05) until 22 days of age, but at 42 days of age this values decreased. Calcium and phosphorus percentage in ash increased (p<0.05) until 22 days of age and evidenced constancy from 22 to 42 days of age. The biomechanical adaptation capacity of femur was evidenced by the increase of the macroscopic measures over the course of the ages and the different behavior of cranial compact layer at 22 days of age suggested an adaptation attempt of the immature bone to muscle mass increase. The ash and mineral percentage confirmed that the relative growth occurs with more intensity until 21 days of age. The calcium and phosphorus percentage evidenced the physiologic balance acting on the deposition of these minerals in femur. © Asian Network for Scientific Information, 2011.

Wireless mesh networks planning based on parameters of quality of service

The use of QoS parameters to evaluate the quality of service in a mesh network is essential mainly when providing multimedia services. This paper proposes an algorithm for planning wireless mesh networks in order to satisfy some QoS parameters, given a set of test points (TPs) and potential access points (APs). Examples of QoS parameters include: probability of packet loss and mean delay in responding to a request. The proposed algorithm uses a Mathematical Programming model to determine an adequate topology for the network and Monte Carlo simulation to verify whether the QoS parameters are being satisfied. The results obtained show that the proposed algorithm is able to find satisfactory solutions.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

Transmission network expansion planning for the Colombian electrical system: Connecting the Ituango hydroelectric power plant

The hydroelectric power plant Hidroltuango represents a major expansion for the Colombian electrical system (with a total capacity of 2400 MW). This paper analyzes the possible interconnections and investments involved in connecting Hidroltuango, in order to strengthen the Colombian national transmission system. A Mixed Binary Linear Programming (MBLP) model was used to solve the Multistage Transmission Network Expansion Planning (MTEP) problem of the Colombian electrical system, taking the N-1 safety criterion into account. The N-1 safety criterion indicates that the transmission system must be expanded so that the system will continue to operate properly if an outage in a system element (within a pre-defined set of contingencies) occurs. The use of a MBLP model guaranteed the convergence with existing classical optimization methods and the optimal solution for the MTEP using commercial solvers. Multiple scenarios for generation and demand were used to consider uncertainties within these parameters. The model was implemented using the algebraic modeling language AMPL and solved using the commercial solver CPLEX. The proposed model was then applied to the Colombian electrical system using the planning horizon of 2018-2025. (C) 2014 Elsevier B.V. All rights reserved.

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

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

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A radial basis function network (RBFN) for function approximation

A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.

Fatores de risco nutricionais associados à mortalidade em pacientes com lesão renal aguda de acordo com os critérios do Acute Kidney Injury Network (AKIN)

Pós-graduação em Fisiopatologia em Clínica Médica - FMB

Influence of thermal parameters on the dendritic arm spacing and the microhardness of Al-5.5wt.%Sn alloy directionally solidified

As ligas Al-Sn são amplamente utilizados em aplicações tribológicas. Nesse estudo, análises térmica, microestrutural e dureza (HV) foram realizadas ao longo de um lingote da liga Al-5,5%Sn, obtido por solidificação direcional horizontal transitória. Os principais parâmetros analisados incluem a velocidade de deslocamento da isoterma liquidus (V_L) e a taxa de resfriamento (T_R). Esses parâmetros térmicos desempenham um papel fundamental na formação da microestrutura. A microestrutura dendrítica foi caracterizada através dos espaçamentos dentríticos primários (λ₁), os quais foram determinados, experimentalmente, e correlacionados com V_L, e T_R. O comportamento apresentado pela liga Al- 5,5% Sn, durante a solidificação,é semelhante ao de outras ligas de alumínio, isto é, observa-se rede dendrítica mais grosseira com a diminuição da taxa de resfriamento, indicando que a imiscibilidade entre o alumínio e estanho não tem um efeito significativo sobre o relação entre o espaçamento dendrítico primário e taxa de resfriamento. A dependência da microdureza em V_L, T_R e no λ₁ foi também analisada. Verificaram-se menores valores de HV para maiores T_R. Por outro lado, os valores HV aumentam com valores crescentes de λ₁.

Fatores de risco nutricionais associados à mortalidade em pacientes com lesão renal aguda de acordo com os critérios do Acute Kidney Injury Network (AKIN)

Pós-graduação em Fisiopatologia em Clínica Médica - FMB

Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.