29 resultados para likelihood-based inference

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The objectives of the present study were to evaluate factors associated with estrous synchronization responses and pregnancy per insemination (P/AI) in Bos indicus beef cows submitted to progesterone-based fixed-time artificial insemination (FTAI) protocols. A total of 2388 cows (1869 Nellore and 519 crossbred Nellore x Angus) from 10 commercial farms were evaluated to determine the relationships among breed, body condition score (BCS) on the first day of the FTAI protocol, the occurrence of estrus between progesterone device removal and FTAI and diameter of largest ovarian follicle (LF) at FTAI on estrous synchronization responses and P/AI. Cows (n=412 primiparous; 1976 multiparous) received an intravaginal device containing progesterone or an ear implant containing norgestomet (a progestin), and an injection of estradiol at the beginning of the estrous synchronization protocol. Body condition was scored using a 1-5 scale on the first day of the FTAI protocol and at 30-60 days postpartum. Females received 300IU of equine chorionic gonadotropin (eCG) and PGF(2 alpha) on the day the progesterone device/implant was removed and were inseminated 48-60h later. At insemination, cows (n=2388) were submitted to an ultrasonographic exam to determine the diameter of the LF. Follicles were classified into four categories based on mean and standard deviation (SD) of the LF (LF1 = two SD below the mean; LF2 = mean minus one SD; LF3 = mean plus one SD; LF4 = two SD above the mean). Ovulation rate was determined in a subset of cows (n=813) by three consecutive ultrasonographic exams: (1) at time of progesterone device/implant removal, (2) at time of FTAI and (3) 48 h after FTAI. Ovulation was defined as the disappearance of a large follicle (>= 8.0 mm) that was previously recorded. Estrus was determined in a subset of the cows (n = 445) by the activation of a detection of estrous patch placed on the tail head on the day of progesterone device/implant removal. Pregnancy was diagnosed 30 days after FTAI. Pregnancy was influenced (P = 0.001) by follicle diameter [LF1 = 27.5% (81/295), LF2 = 46.6% (328/705), LF3 = 57.9% (647/1118), LF4 = 63.3% (171/270)] and the occurrence of estrus [estrus = 67.7% (174/257) and no estrus = 36.2% (68/188)]. Follicle diameter at FTAI influenced ovulation rate [LF1 = 42.5% (34/80), LF2 = 73.9% (161/218), LF3 = 95.8% (407/425), LF4 = 97.8% (88/90)], the occurrence of estrus [LF1 = 54.8% (51/93), LF2 = 33.6% (43/128), LF3 = 68.9% (126/183), LF4 = 90.2% (37/41)] and P/AI among cows that had ovulations [LF1 =32.4% (11/34), LF2 = 50.3% (81/161), LF3 = 60.0% (244/407), LF4 = 68.2% (60/88)]. Improving estrous responses between progesterone device withdrawal and FTAI and increasing the diameter of the LF at FTAI may be important aspects to achieve improved estrous synchronization responses and P/AI following progesterone/progestin and estradiol based FTAI protocols in suckled Bos indicus cows. (C) 2010 Elsevier B.V. All rights reserved.

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Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

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This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.

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The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behavior. In this paper, a system based on fuzzy logic systems is developed to overcome the problems usually found in the conventional mathematical models. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the fuzzy approach. Simulation results are presented to justify the validity of the proposed approach.

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This paper describes a novel approach for mapping lightning processes using fuzzy logic. The core regarding lightning process is to identify and to model those uncertain information on mathematical principles. In fact, the lightning process involves several nonlinear features that our current mathematical tools would not be able to model. The estimation process has been carried out using a fuzzy system based on Sugeno's architecture. Simulation results confirm that proposed approach can be efficiently used in these types of problem.