3 resultados para Artificial induction
em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo
Resumo:
The effects of estradiol benzoate (EB) and estradiol cypionate (EC) on induction of ovulation after a synchronized LH surge and on fertility of Bos indicus females submitted to timed AI (TAI) were evaluated. In Experiment 1, ovariectomized Nelore heifers were used to evaluate the effect of EB (n = 5) and EC (n = 5) on the circulating LH profile. The LH surge timing (19.6 and 50.5 h; P = 0.001), magnitude (20.5 and 9.4 ng/mL; P = 0.005), duration (8.6 and 16.5 h; P = 0.001), and area under the LH curve (158.6 and 339.4 ng/mL; P = 0.01) differed between the EB and EC treatments, respectively. In Experiment 2 (follicular responses; n = 60) and 3 (pregnancy per AI; P/AI; n = 953) suckled Bos indicus beef cows submitted to an estradiol/progesterone-based synchronization protocol were assigned to receive one of two treatments to induce synchronized ovulation: 1 mg of EB im 24 h after progesterone (P4) device removal or 1 mg of EC im at P4 device removal. There was no difference (P > 0.05) between EB and EC treatments on follicular responses (maximum diameter of the ovulatory follicle, 13.1 vs. 13.9 mm; interval from progesterone device removal to ovulation, 70.2 vs. 68.5 h; and ovulation rate, 77.8 vs. 82.8%, respectively). In addition, P/AI was similar (P < 0.22) between the cows treated with EB (57.5%; 277/482) and EC (61.8%; 291/471). In conclusion, despite pharmacologic differences, both esters of estradiol administered either at P4 device removal (EC) or 24 h later (EB) were effective in inducing an LH surge which resulted in synchronized ovulations and similar P/AI in suckled Bos indicus beef cows submitted to TAI. (C) 2012 Elsevier Inc. All rights reserved.
Resumo:
This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.
Resumo:
This work proposes the development of an Adaptive Neuro-fuzzy Inference System (ANFIS) estimator applied to speed control in a three-phase induction motor sensorless drive. Usually, ANFIS is used to replace the traditional PI controller in induction motor drives. The evaluation of the estimation capability of the ANFIS in a sensorless drive is one of the contributions of this work. The ANFIS speed estimator is validated in a magnetizing flux oriented control scheme, consisting in one more contribution. As an open-loop estimator, it is applied to moderate performance drives and it is not the proposal of this work to solve the low and zero speed estimation problems. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink(R) software. To determine the benefits of the proposed model, a practical system was implemented using a voltage source inverter (VSI) to drive the motor and the vector control including the ANFIS estimator, which is carried out by the Real Time Toolbox from Matlab/Simulink(R) software and a data acquisition card from National Instruments.