Juan Pablo Agnelli

Bio: Juan Pablo Agnelli is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Estimación de parámetros y clasificación de datos

Abstract: Tesis (Doctor en Matematica)--Universidad Nacional de Cordoba. Facultad de Matematica, Astronomia y Fisica, 2011.

Thermal Detection of Embedded Tumors Using Infrared Imaging

Abstract: Breast cancer is the most common cancer among women. Statistics released by American Cancer Society (1999) show that every 1 in every 8 women in the United States is likely to get breast cancer during her lifetime. Thermography, also known as thermal or infrared imaging, is a procedure to determine if an abnormality is present in the breast tissue temperature distribution, which may indicate the presence of an embedded tumor. In the year 1982, United States Food and Drug Administration (FDA) approved thermography as an adjunct method of detecting breast cancer, which could be combined with other established techniques like mammography. Although thermography is currently used to indicate the presence of an abnormality, there are no standard protocols to interpret the abnormal thermal images and determine the size and location of an embedded tumor. This research explores the relationship between the physical characteristics of an embedded tumor and the resulting temperature distributions on the skin surface. Experiments were conducted using a resistance heater that was embedded in agar in order to simulate the heat produced by a tumor in the biological tissue. The resulting temperature distribution on the surface was imaged using an infrared camera. In order to estimate the location and heat generation rate of the source from these temperature distributions, a genetic algorithm was used as the estimation method. The genetic algorithm utilizes a finite difference scheme for the direct solution of Pennes bioheat equation. It was determined that a genetic algorithm based approach is well suited for the estimation problem and that thermography can prove to be a valuable tool in locating tumors if combined with such an algorithm.Copyright © 2004 by ASME

Milk Yield and Quality to Estimate Genetic Parameters in Buffalo Cows

Abstract: The objective of this study was to estimate genetic parameters for daily milk yield, fat and protein milk contents and their relationship with "mozzarella" cheese production using the classic instruments of the quantitative genetics. A total of 5130 daily milk yields records, be­longing to 6 herds in South Italy were analyzed. The traits studied were: accumulated 270-day milk yield, milk fat and protein percentages, and milk yield day and mozzarella production. Descriptive statistics of the variable studied have been obtained with the procedure MEANS and FREQ, while the variation sources have been investigated using procedure GLM. With the objective to characterize the effects of greater impact on the production of milk (kg/days), fat and protein content (%) and "mozzarella" production (kg/days), has been used analysis of variance (ANOVA). On average, buffalo cow’s milk production during lactation was 9.21 ± 2.79 kg/d with 8.73% of fat and 4.98% of protein. Heritability estimates were low. The genetic correlation estimates between milk yield and % of fat and % of protein were low. These results showed that the genes with the affect on milk yield have an antagonistic effect on % of fat and % of protein traits. Its suggests that selection to increase milk yield, would in the long term probably cause a reduction in milk constituents.