Optimizing age at first use of semen for higher fertilityin Sahiwal breeding bulls

2016 ◽  
Author(s):  
B. C. Naha ◽  
A. K. Chakravarty ◽  
M. A. Mir ◽  
M. Bhakat
Keyword(s):  
Comparative Evaluation ◽  
Regression Models ◽  
Information Criterion ◽  
Conception Rate ◽  
Coefficient Of Determination ◽  
Progeny Testing ◽  
Testing Programme ◽  
Multiple Regression Models ◽  
Improvement Programme ◽  
Selection Of

The objective of the study was to optimise the age at first use (AAFU) of semen in Sahiwal breeding bulls which will help in early selection of bulls under progeny testing programme. The data on AAFU, conception rate based on first A.I. (CRFAI), overall conception rate (OCR) and birth weight (B.WT) of 43 Sahiwal bulls during 1987 to 2013 at NDRI centre pertaining to 8 sets of Sahiwal improvement programme at ICAR-NDRI, Karnal, India were adjusted for significant environmental influences and subsequently analyzed. Simple and multiple regression models were used for prediction of CRFAI and OCR of Sahiwal bulls. Comparative evaluation of three developed models (I to III) have showed that Model III, having AAFU and B.WT which fulfill the accuracy of model as revealed by high coefficient of determination, low mean sum of square to due error, low conceptual predictive value and low Bayesian information criterion . The results showed that average predicted CRFAI was highest (49.34%) at less than 5 years and lowest (44.79%) at > 6 years of age at first A.I. /use. Similarly average predicted OCR was highest (48.50%) at less than 5 years and lowest (44.56%) at >6 years of age at first A.I. / use of Sahiwal bulls. In organized herd under progeny testing programme, Sahiwal bulls should be used prior to 5 years which is expected to result in 4.45% better CRFAI and 3.94% better OCR in comparison to Sahiwal bulls used after 6 years of age.

Optimum age at first semen freezing in relation to fertility of Sahiwal breeding bulls

2018 ◽  
Author(s):  
B. C. Naha ◽  
A. K. Chakravarty ◽  
M. A. Mir ◽  
M. Bhakat ◽  
Ramendra Das ◽  
...  
Keyword(s):  
Birth Weight ◽  
Regression Models ◽  
Dairy Herd ◽  
Information Criterion ◽  
Conception Rate ◽  
Coefficient Of Determination ◽  
National Dairy Research Institute ◽  
Multiple Regression Models ◽  
Improvement Programme ◽  
Selection Of

Early selection of bulls having optimum age at first semen freezing play an important role in improving reproductive performance in a dairy herd. Twenty seven years data (1987-2013) on age at first semen freezing (AAFSF), conception rate based on first A.I. (CRFAI) , overall conception rate (OCR) and birth weight (B.WT) of 41 Sahiwal bulls belonging to 8 sets of Sahiwal improvement programme at ICAR-National Dairy Research Institute, Karnal, Haryana, India; were adjusted against environmental effects and subsequently analysed. Simple and multiple regression models were used for prediction of CRFAI and OCR of Sahiwal bulls. Among the three developed models (I to III), it was observed that Model III having age at first semen freezing and birth weight fulfil the accuracy of model i.e.; having high coefficient of determination (R2) value (CRFAI = 67% and OCR= 69%), low mean error sum of square (MSSe), low conceptual predictive value (CP value) and low Bayesian information criterion (BIC). The results revealed that optimum age at first semen freezing of Sahiwal bulls should be 2.5 - 3.0 years for 3.10% higher conception rate based on first A.I. (48.86%) and 4.39% higher overall conception rate (48.78%) in comparison to Sahiwal bulls with more than 3.5 years of age (CRFAI :- 45.76% and OCR :- 44.39%).

scholarly journals A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1063
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Walter Esfrain Pereira ◽  
Manoel Bandeira de Albuquerque
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Linear Measurements ◽  
Maximum Width ◽  
Bias Index ◽  
Different Shapes ◽  
Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

scholarly journals Drying kinetics of jabuticaba pulp by regression models

2019 ◽  
Vol 41 (1) ◽  
Author(s):  
Thais Destefani Ribeiro Furtado ◽  
Joel Augusto Muniz ◽  
Edilson Marcelino Silva ◽  
Jaqueline Gonçalves Fernandes
Keyword(s):  
Regression Models ◽  
Information Criterion ◽  
Drying Kinetics ◽  
Moisture Loss ◽  
Model Parameters ◽  
Drying Rate ◽  
Southern Brazil ◽  
Drying Time ◽  
Kinetics Of ◽  
Selection Of

Abstract Jabuticaba tree is native to the Atlantic Forest in Southern Brazil, and its fruit is widely consumed in the fresh form, but it is highly perishable, requiring conservation techniques. The aim of this study was to describe the drying kinetics of jabuticaba pulp at temperatures of 50 and 60°C, comparing the Henderson, Simple Three-Parameter Exponential, Lewis, Thompson, Fick and Wang and Sing regression models and estimating the Absolute Drying Rate (ADR) for the best model. Parameters were estimated using the SAS software. The evaluation of the quality in the adjustment and selection of models was made based on the adjusted determination coefficient, Residual Standard Deviation and Akaike Information Criterion. Models presented good adjustment to data, and the Lewis model was the most suitable to describe the drying kinetics of jabuticaba pulp at temperatures of 50 and 60°C, with drying rate of 0.000063 and 0.000082 g of water/s respectively. ADR indicated that in one third of the drying time, 70% of moisture loss occurred at both temperatures and after this period, there was a deceleration of moisture loss until stabilization, when equilibrium moisture content is reached.

scholarly journals Adjusting weight growth curve of male quails Coturnix japonica reared in the semi-arid region of the State of Pernambuco

2018 ◽  
Vol 41 (1) ◽  
pp. 42563 ◽  
Author(s):  
Leandro Ricardo Rodrigues de Lucena ◽  
Marco Aurélio Carneiro de Holanda ◽  
Mônica Calixto Ribeiro de Holanda ◽  
Marcelo Lopes dos Anjos
Keyword(s):  
Growth Curve ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Normal Regression ◽  
Weight Growth ◽  
Semi Arid Region ◽  
Log Normal ◽  
Squared Residuals

This study adjusted different regression models to describe the growth pattern of meat quails from birth to 42 days of age. Data of 300 male quails were used. Weight and height information of all quails were collected weekly from the 1st to the 42nd day of age. Body weight of poultry was subjected to the polynomial, logistic, Gompertz, Weibull, and log-normal regression models. The criteria used to choose the best model to explain the growth curve of quails were the coefficient of determination of the model, Akaike’s information criterion, sum of squared residuals and Willmott’s index. For all the models used, the variables age and height were significant to explain the weight of quails. The polynomial (R² = 99.99%, AIC = 24.68, SSR = 27.5, d = 0.9999) and log-normal (R² = 99.60%, AIC = -17.5, SSR = 107.15, d = 0.9989) models presented the best fit criteria and were recommended to explain the growth of quails.

scholarly journals Mathematical models for metric features extraction from RGB-D sensor

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Elton Fernandes Dos Santos ◽  
Laurimar Gonçalves Vendrusculo ◽  
Luciano Bastos Lopes ◽  
Scheila Geiele Kamchen ◽  
Isabella C. F. S. Condotta
Keyword(s):  
Regression Models ◽  
Flat Surface ◽  
Average Distance ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Target Area ◽  
Depth Images ◽  
White Cardboard ◽  
Set Up

The use of the RGB-D camera has been applied in several fields of science. That popularization is due to the emergence of technologies such as the Intel® RealSense™ D400 series. However, despite the actual demand from some potential users, few studies concern the characterization of these sensors for object measurements. Our study sought to estimate models dealing with calculating the area and length between targets or points within RGB and depth images.  An experiment was set up with white cardboard fixed on a flat surface with colored pins. We measured the distance between the camera and cardboard by calculating the average distance from the pixels belonging to the target area. The Information Criterion AIC and BIC associated with R2 were performed to select the best models. Polynomial and power regression models reached the highest coefficient of determination and smallest values of AIC and BIC.

scholarly journals A methodology for the design of experiments in computational intelligence with multiple regression models

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2721 ◽  
Author(s):  
Carlos Fernandez-Lozano ◽  
Marcos Gestal ◽  
Cristian R. Munteanu ◽  
Julian Dorado ◽  
Alejandro Pazos
Keyword(s):  
Machine Learning ◽  
Design Of Experiments ◽  
Computational Intelligence ◽  
Regression Models ◽  
R Package ◽  
Learning Approaches ◽  
Regression Algorithms ◽  
Multiple Regression Models ◽  
Real Complex ◽  
Selection Of

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

scholarly journals LO17: A comparative evaluation of ED crowding metrics and associations with patient mortality

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S33
Author(s):  
A. McRae ◽  
I. Usman ◽  
D. Wang ◽  
G. Innes ◽  
E. Lang ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Regression Models ◽  
Wait Time ◽  
Information Criterion ◽  
Data Partition ◽  
Data Partitions ◽  
Ed Visits ◽  
Ed Crowding ◽  
Strength Of Association ◽  
Better Than

Introduction: Over 700 different input, throughput and output metrics have been used to quantify ED crowding. Of these, only ED length-of-stay (ED LOS) has been shown to be associated with mortality. No comparative evaluation of ED crowding metrics has been performed to determine which ones have the strongest association with patient mortality. The objective of this study was to compare the strength of association of common ED input, throughput and output metrics to patient mortality. Methods: Administrative data from five years of ED visits (2011-2014) at three urban EDs were linked to develop a database of over 900,000 ED visits with patient demographics, electronic time stamps for care processes, dispositions and outcomes. The data were randomly divided into three partitions of equal size. Here we report the findings from one partition of 253,938 ED visits. The remaining two data partitions will be used to validate these findings. Commonly-used crowding metrics were quantified and aggregated by day or by shift (0800-1600, 1600-2400, 2400-0800), and the shift-specific metrics assigned to each patient. The primary outcome was 7-day all-cause mortality. Multilevel logistic regression models were developed for 7-day mortality, with selected ED crowding metrics and a common set of confounders as predictors. The strength of association between the crowding metrics and mortality was compared using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC): ED crowding metrics with lower AIC and BIC have stronger associations with 7-day mortality. Results: Of 909,000 ED encounters, 124,679 (16.5%) arrived by EMS, 149,233(19.7%) were admitted, and 3,808 patients (0.5%) died within 7 days of ED arrival. Of input metrics, the model with ED wait-time was better (i.e. had a smaller AIC and BIC) than models for daily census, ED occupancy or LWBS proportion for predicting 7-day mortality. Of throughput metrics, the model with mean ED LOS was better than the model for mean MD care time. Of output metrics, the model with daily inpatient hospital occupancy was better than the model with mean boarding time. Conclusion: Based on one data partition, regression models based on the average wait-time, ED LOS and inpatient occupancy best predicted 7-day mortality. These results will be validated in the two other data partitions to confirm the best-performing ED input, throughput and output metrics.

scholarly journals PHENOLOGY OF Calotropis procera (Ait.) W.T. Aiton ACCESSIONS BASED ON MORPHOPHYSIOLOGICAL CHARACTERISTICS

2019 ◽  
Vol 32 (2) ◽  
pp. 543-551
Author(s):  
ISAIAS VITORINO BATISTA DE ALMEIDA ◽  
MAÍLSON MONTEIRO DO RÊGO ◽  
FABIANE RABELO DA COSTA BATISTA ◽  
ELIZANILDA RAMALHO DO RÊGO ◽  
RISELANE DE LUCENA ALCÂNTARA BRUNO
Keyword(s):  
Regression Models ◽  
Soil Analysis ◽  
Germplasm Collection ◽  
Northeastern Brazil ◽  
Coefficient Of Determination ◽  
Calotropis Procera ◽  
Soil Fertilization ◽  
Physiological Indexes ◽  
Beginning Of Flowering ◽  
Selection Of

ABSTRACT The objective of this work was to evaluate the phenology of Calotropis procera accessions based on morphophysiological characteristics. Seeds of 70 C. procera accessions were collected between October 2015 and January 2016 in areas where the species naturally occurs in Northeastern Brazil. These accessions make up the current germplasm collection of the National Institute of the Semi-arid (INSA). The experiment was conducted in a greenhouse at INSA, in Campina Grande - PB, from January to September in 2016. The plants were cultivated in plastic pots filled with soil. Fertilization was conducted according to soil analysis recommendations and plants were irrigated at a 3-day interval. Morphophysiological characteristics were recorded 30 days after sowing (DAS) and 30-day intercalated evaluations were conducted up to 240 DAS. In addition, physiological indexes were estimated and leaf fall, flowering, and fruiting were evaluated at 120 DAS. Mean of each characteristic was obtained using the adjustment and the selection of regression models to explain the growth of C. procera based on the coefficient of determination. The vegetative stage of C. procera occurs during the 240 DAS, with continuous fall and production of leaves, while the reproductive stage begins at 153 DAS, continuing until 222 DAS, depending on the accession, making emission of inflorescences constant on plants after the beginning of flowering. The physiological indexes are efficient to estimate the growth of C. procera accessions.

scholarly journals Genetic parameters for post weaning growth of Nellore cattle using polinomyals and trigonometric functions in random regression models

2009 ◽  
Vol 66 (4) ◽  
pp. 522-528 ◽  
Author(s):  
Osmar Jesus Macedo ◽  
Décio Barbin ◽  
Gerson Barreto Mourão
Keyword(s):  
Regression Models ◽  
Growth Curves ◽  
Information Criterion ◽  
The Other ◽  
Random Regression ◽  
Trigonometric Functions ◽  
Coefficient Estimates ◽  
Covariance Functions ◽  
Nellore Cattle ◽  
Selection Of

Covariance functions and random regression models have been considered as an alternative for data adjustment, in sequence, stemming from the same animal along time and which presents a structured pattern of covariance. Aiming to evaluate the performance of random regression models based on the Legendre, modified Jacobi and trigonometric functions, data concerning the weights of Nellore breed animals were used from birth to the 800th day of life, in models that assumed direct additive and animal permanent environmental effects coefficients. The Schwarz Bayesian information criterion (BIC) led to the selection of the models Legendre of order six (ML6), Jacobi of order five (MJ5) and trigonometric of order six (MT6), the ML6 model presenting the lowest BIC. At the extremity of the interval, the MJ5 model presented lower variance of component estimates than those obtained through the ML6 model, however the estimates were in accordance to the medium part of the interval; while the estimates from the MT6 model were oscillating and different from those obtained through the other models. At the extremity of the interval, the heritability coefficient estimates (<img src="/img/revistas/sa/v66n4/h4_circ.gif" align="absmiddle">2) obtained through the MJ5 model were lower than those obtained through the ML6 model, however, in the medium part of the interval, they were in accordance, remaining between 0.2 and 0.3. The values obtained through the MT6 model were different from those obtained through the other models, remaining between 0.35 and 0.40 on the first 285th days and then dropping to 0.01 on the 800th days of life. The means of the estimated growth curves started to distance from the data mean tendency from the 470th days on, and in this interval, the MT6 model was the most suitable.

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