Using Genetic Algorithm to Estimate Hydraulic Parameters of Unconfined Aquifers

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Asghar Asghari Moghaddam

2009-03-01

Full Text Available Nowadays, optimization techniques such as Genetic Algorithms (GA have attracted wide attention among scientists for solving complicated engineering problems. In this article, pumping test data are used to assess the efficiency of GA in estimating unconfined aquifer parameters and a sensitivity analysis is carried out to propose an optimal arrangement of GA. For this purpose, hydraulic parameters of three sets of pumping test data are calculated by GA and they are compared with the results of graphical methods. The results indicate that the GA technique is an efficient, reliable, and powerful method for estimating the hydraulic parameters of unconfined aquifer and, further, that in cases of deficiency in pumping test data, it has a better performance than graphical methods.

Estimation of genetic parameters for carcass traits in Japanese quail ...

African Journals Online (AJOL)

The aim of this study was to estimate genetic parameters of some carcass characteristics in the Japanese quail. For this aim, carcass weight (Cw), breast weight (Bw), leg weight (Lw), abdominal fat weight (AFw), carcass yield (CP), breast percentage (BP), leg percentage (LP) and abdominal fat percentage (AFP) were ...

Estimates of genetic parameters and path analysis in lentil (lens culinaris medik)

International Nuclear Information System (INIS)

Younis, N.; Hanif, M.; Sadiq, S.; Abbas, G.; Asghar, M.J.; Haq, M.A.

2008-01-01

These studies were conducted to determine the genetic parameters and character association in elite lines 0 lentil (Lens culinaris Medik).Genetic parameters like genotypic and phenotypic variances, coefficients of variation heritability, genetic advance, correlation coefficients and path coefficients were estimated. Significant variation was noted for all the traits. High heritability estimates were observed for all the traits except number of primary branches per plant. In general phenotypic coefficients of variability were greater than their corresponding genotypic coefficient of variability. Higher estimates of heritability and genetic advance were observed for seed yield (97.10%, 90.71%), harvest index (96.20%, 63.29%) and maturity days (95.90%, 63.39%) indicating the these characters are mainly controlled by additive genes and selection of such traits might be effective for the improvement of seed yield. Days to flower, plant height, number of primary branches, biological yield, harvest index and hundred seed weight had positive direct effect on seed yield. Biological yield, hundred seed weight and harvest index also had positive and highly significant genotypic and phenotypic correlation with seed yield. Hence these traits could be used for the improvement of seed yield resulting in the evolution of high yielding varieties 0 lentil. (author)

Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle

Science.gov (United States)

2009-01-01

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits. PMID:21637450

Genetic parameter estimation of reproductive traits of Litopenaeus vannamei

Science.gov (United States)

Tan, Jian; Kong, Jie; Cao, Baoxiang; Luo, Kun; Liu, Ning; Meng, Xianhong; Xu, Shengyu; Guo, Zhaojia; Chen, Guoliang; Luan, Sheng

2017-02-01

In this study, the heritability, repeatability, phenotypic correlation, and genetic correlation of the reproductive and growth traits of L. vannamei were investigated and estimated. Eight traits of 385 shrimps from forty-two families, including the number of eggs (EN), number of nauplii (NN), egg diameter (ED), spawning frequency (SF), spawning success (SS), female body weight (BW) and body length (BL) at insemination, and condition factor (K), were measured,. A total of 519 spawning records including multiple spawning and 91 no spawning records were collected. The genetic parameters were estimated using an animal model, a multinomial logit model (for SF), and a sire-dam and probit model (for SS). Because there were repeated records, permanent environmental effects were included in the models. The heritability estimates for BW, BL, EN, NN, ED, SF, SS, and K were 0.49 ± 0.14, 0.51 ± 0.14, 0.12 ± 0.08, 0, 0.01 ± 0.04, 0.06 ± 0.06, 0.18 ± 0.07, and 0.10 ± 0.06, respectively. The genetic correlation was 0.99 ± 0.01 between BW and BL, 0.90 ± 0.19 between BW and EN, 0.22 ± 0.97 between BW and ED, -0.77 ± 1.14 between EN and ED, and -0.27 ± 0.36 between BW and K. The heritability of EN estimated without a covariate was 0.12 ± 0.08, and the genetic correlation was 0.90 ± 0.19 between BW and EN, indicating that improving BW may be used in selection programs to genetically improve the reproductive output of L. vannamei during the breeding. For EN, the data were also analyzed using body weight as a covariate (EN-2). The heritability of EN-2 was 0.03 ± 0.05, indicating that it is difficult to improve the reproductive output by genetic improvement. Furthermore, excessive pursuit of this selection is often at the expense of growth speed. Therefore, the selection of high-performance spawners using BW and SS may be an important strategy to improve nauplii production.

Short communication: Estimates of genetic parameters for dairy fertility in New Zealand.

Science.gov (United States)

Amer, P R; Stachowicz, K; Jenkins, G M; Meier, S

2016-10-01

Reproductive performance of dairy cows in a seasonal calving system is especially important as cows are required to achieve a 365-d calving interval. Prior research with a small data set has identified that the genetic evaluation model for fertility could be enhanced by replacing the binary calving rate trait (CR42), which gives the probability of a cow calving within the first 42d since the planned start of calving at second, third, and fourth calving, with a continuous version, calving season day (CSD), including a heifer calving season day trait expressed at first calving, removing milk yield, retaining a probability of mating trait (PM21) which gives the probability of a cow being mated within the first 21d from the planned start of mating, and first lactation body condition score (BCS), and including gestation length (GL). The aim of this study was to estimate genetic parameters for the proposed new model using a larger data set and compare these with parameters used in the current system. Heritability estimates for CSD and PM21 ranged from 0.013 to 0.019 and from 0.031 to 0.058, respectively. For the 2 traits that correspond with the ones used in the current genetic evaluation system (mating trait, PM21 and BCS) genetic correlations were lower in this study compared with previous estimates. Genetic correlations between CSD and PM21 across different parities were also lower than the correlations between CR42 and PM21 reported previously. The genetic correlation between heifer CSD and CSD in first parity was 0.66. Estimates of genetic correlations of BCS with CSD were higher than those with PM21. For GL, direct heritability was estimated to be 0.67, maternal heritability was 0.11, and maternal repeatability was 0.22. Direct GL had moderate to high and favorable genetic correlations with evaluated fertility traits, whereas corresponding residual correlations remain low, which makes GL a useful candidate predictor trait for fertility in a multiple trait

Genetic parameter estimation for long endurance trials in the Uruguayan Criollo horse.

Science.gov (United States)

López-Correa, R D; Peñagaricano, F; Rovere, G; Urioste, J I

2018-06-01

The aim of this study was to estimate the genetic parameters of performance in a 750-km, 15-day ride in Criollo horses. Heritability (h 2 ) and maternal lineage effects (mt 2 ) were obtained for rank, a relative placing measure of performance. Additive genetic and maternal lineage (rmt) correlations among five medium-to-high intensity phase ranks (pRK) and final rank (RK) were also estimated. Individual records from 1,236 Criollo horses from 1979 to 2012 were used. A multivariate threshold animal model was applied to the pRK and RK. Heritability was moderate to low (0.156-0.275). Estimates of mt 2 were consistently low (0.04-0.06). Additive genetic correlations between individual pRK and RK were high (0.801-0.924), and the genetic correlations between individual pRKs ranged from 0.763 to 0.847. The pRK heritabilities revealed that some phases were explained by a greater additive component, whereas others showed stronger genetic relationships with RK. Thus, not all pRK may be considered as similar measures of performance in competition. © 2018 Blackwell Verlag GmbH.

Estimation of genetic parameters for body weight at different ages in ...

African Journals Online (AJOL)

The objective of the present study is to estimate genetic parameters of birth weight (BW, n = 3005), weaning weight (WW, n = 2800), 6 months weight (6 MW, n = 2600), 9 months weight (9 MW, n = 1990) and yearling weight (YW, n = 1450) of Mehraban sheep, collected during 1995 - 2007 at Mehraban sheep Breeding ...

Genome-wide association study of swine farrowing traits. Part I: genetic and genomic parameter estimates.

Science.gov (United States)

Schneider, J F; Rempel, L A; Rohrer, G A

2012-10-01

The primary objective of this study was to determine genetic and genomic parameters among swine (Sus scrofa) farrowing traits. Genetic parameters were obtained using MTDFREML. Genomic parameters were obtained using GENSEL. Genetic and residual variances obtained from MTDFREML were used as priors for the Bayes C analysis of GENSEL. Farrowing traits included total number born (TNB), number born alive (NBA), number born dead (NBD), number stillborn (NSB), number of mummies (MUM), litter birth weight (LBW), and average piglet birth weight (ABW). Statistically significant heritabilities included TNB (0.09, P = 0.048), NBA (0.09, P = 0.041), LBW (0.20, P = 0.002), and ABW (0.26, P NBA (0.97, P NBA-LBW (0.56, P NBA (0.06), NBD (0.00), NSB (0.01), MUM (0.00), LBW (0.11), and ABW (0.31). Limited information is available in the literature about genomic parameters. Only the GP estimate for NSB is significantly lower than what has been published. The GP estimate for ABW is greater than the estimate for heritability found in this study. Other traits with significant heritability had GP estimates half the value of heritability. This research indicates that significant genetic markers will be found for TNB, NBA, LBW, and ABW that will have either immediate use in industry or provide a roadmap to further research with fine mapping or sequencing of areas of significance. Furthermore, these results indicate that genomic selection implemented at an early age would have similar annual progress as traditional selection, and could be incorporated along with traditional selection procedures to improve genetic progress of litter traits.

Genetic parameter estimates for weaning weight and Kleiber ratio in goats

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China Supakorn

2012-04-01

Full Text Available The research was conducted to evaluate the factors affecting on weaning weight (WW and Kleiber ratio (KR and toestimate genetic parameters for two traits in goats. The fixed factors affecting both traits indicated that year-season of birth,sex, birth type and regression of the Thai Native (TN, Boer (BO and Saanen (SA influenced on WW and KR (P<0.05. Malesin this population were heavier (P<0.05 than females. Weaning weights and KR of single kid were significantly higher (P<0.05 than other birth rearing types. Bivariate analysis of three models (Model 1: without maternal genetic effect, Model 2:with maternal genetic effect and am = 0, and Model 3: with maternal genetic effect and am  0 were used to estimate geneticparameters for this research. Estimated direct heritabilities from all models were 0.26 to 0.38 for WW, and 0.22 to 0.35 for KR.Estimated maternal heritabilities from Model 2 and 3 were 0.09 and 0.12 for WW and 0.08 and 0.11 for KR, respectively. Thedirect genetic and phenotypic correlations between WW and KR were positive and moderate values. Maternal genetic correlationsbetween them were positive and of low values. An antagonistic direct-maternal correlations from Model 3 withintraits and between traits indicated that offspring of does with superior maternal abilities probably may provide an inferiordirect genetic effect in the same trait and between traits. It was therefore possible to rapidly improve WW and KR in thisgoat population through selection, while the adverse effects of direct-maternal correlation within and between traits shouldbe considered. The best fit model would be a model including maternal genetic effect without a direct-maternal genetic covariance.

Genetic parameters in a Swine Population

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Dana Popa

2010-05-01

Full Text Available The estimation of the variance-covariance components is a very important step in animal breeding because these components are necessary for: estimation of the genetic parameters, prediction of the breeding value and design of animal breeding programs. The estimation of genetic parameters is the first step in the development of a swine breeding program, using artificial insemination. Various procedures exist for estimation of heritability. There are three major procedures used for estimating heritability: analysis of variance (ANOVA, parents-offspring regression and restricted maximum likelihood (REML. By using ANOVA methodology or regression method it is possible to obtain aberrant values of genetic parameters (negative or over unit value of heritability coefficient, for example which can not be interpreting because is out of biological limits.

Estimates of genetic parameters of body weight in descendants of x-irradiated rat spermatogonia

International Nuclear Information System (INIS)

Gianola, D.; Chapman, A.B.; Rutledge, J.J.

1977-01-01

Effects of nine generations of 450 R per generation of ancestral spermatogonial x irradiation of inbred rats on genetic parameters of body weight at 3, 6 and 10 weeks of age and of weight gains between these periods were studied. Covariances among relatives were estimated by mixed model and regression techniques in randomly selected lines with (R) and without (C) radiation history. Analyses of the data were based on five linear genetic models combining additive direct, additive indirect (maternal), dominance and environmental effects. Parameters in these models were estimated by generalized least-squares. A model including direct and indirect genetic effects fit more closely to the data in both R and C lines. Overdominance of induced mutations did not seem to be present. Ancestral irradiation increased maternal additive genetic variances of body weights and gains but not direct genetic variances. Theoretically, due to a negative direct-maternal genetic correlation, within full-sib family selection would be ineffective in increasing body weight at six weeks in both R and C lines. However, progress from mass selection would be expected to be faster in the R lines

Estimation of genetic parameters for growth traits in a breeding program for rainbow trout (Oncorhynchus mykiss) in China.

Science.gov (United States)

Hu, G; Gu, W; Bai, Q L; Wang, B Q

2013-04-26

Genetic parameters and breeding values for growth traits were estimated in the first and, currently, the only family selective breeding program for rainbow trout (Oncorhynchus mykiss) in China. Genetic and phenotypic data were collected for growth traits from 75 full-sibling families with a 2-generation pedigree. Genetic parameters and breeding values for growth traits of rainbow trout were estimated using the derivative-free restricted maximum likelihood method. The goodness-of-fit of the models was tested using Akaike and Bayesian information criteria. Genetic parameters and breeding values were estimated using the best-fit model for each trait. The values for heritability estimating body weight and length ranged from 0.20 to 0.45 and from 0.27 to 0.60, respectively, and the heritability of condition factor was 0.34. Our results showed a moderate degree of heritability for growth traits in this breeding program and suggested that the genetic and phenotypic tendency of body length, body weight, and condition factor were similar. Therefore, the selection of phenotypic values based on pedigree information was also suitable in this research population.

Estimation of Genetic Parameters for Fat Deposition and Carcass Traits in Broilers

NARCIS (Netherlands)

Zerehdaran, S.; Vereijken, A.L.J.; Arendonk, van J.A.M.; Waaij, van der E.H.

2004-01-01

Abdominal and subcutaneous fat are regarded as the main sources of waste in the slaughterhouse. Fat stored intramuscularly is regarded a favorite trait related to meat quality. The objective of current study was to estimate genetic parameters for fat deposition in the 3 different parts of body and

Estimation of genetic parameters related to eggshell strength using random regression models.

Science.gov (United States)

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

Genetic and phenotypic parameter estimates for growth traits of Hainan Black goat in southern China

NARCIS (Netherlands)

Zhou, Han Lin; Gu, Li Hong; Sun, Yanyan; Xu, Tie Shan; Rong, Guang

2015-01-01

Genetic improvement of the growth of Hainan Black goats is a major concern as the breed is an important meat-type goat raised in southern China. To estimate genetic and phenotypic parameters for growth traits for this breed, a population of 1354 Hainan Black goats born and maintained at the

The effect of selection on genetic parameter estimates

African Journals Online (AJOL)

Unknown

The South African Journal of Animal Science is available online at ... A simulation study was carried out to investigate the effect of selection on the estimation of genetic ... The model contained a fixed effect, random genetic and random.

Estimation of heritability and genetic correlation of body weight gain and growth curve parameters in Korean native chicken

Directory of Open Access Journals (Sweden)

Prabuddha Manjula

2018-01-01

Full Text Available Objective This study estimated the genetic parameters for body weight gain and growth curve parameter traits in Korean native chicken (KNC. Methods A total of 585 F1 chickens were used along with 88 of their F0 birds. Body weights were measured every 2 weeks from hatching to 20 weeks of age to measure weight gain at 2-week intervals. For each individual, a logistic growth curve model was fitted to the longitudinal growth dataset to obtain three growth curve parameters (α, asymptotic final body weight; β, inflection point; and γ, constant scale that was proportional to the overall growth rate. Genetic parameters were estimated based on the linear-mixed model using a restricted maximum likelihood method. Results Heritability estimates of body weight gain traits were low to high (0.057 to 0.458. Heritability estimates for α, β, and γ were 0.211±0.08, 0.249±0.09, and 0.095±0.06, respectively. Both genetic and phenotypic correlations between weight gain traits ranged from −0.527 to 0.993. Genetic and phenotypic correlation between the growth curve parameters and weight gain traits ranged from −0.968 to 0.987. Conclusion Based on the results of this study population, we suggest that the KNC could be used for selective breeding between 6 and 8 weeks of age to enhance the overall genetic improvement of growth traits. After validation of these results in independent studies, these findings will be useful for further optimization of breeding programs for KNC.

Estimation of genetic parameters for test day records of dairy traits in the first three lactations

Directory of Open Access Journals (Sweden)

Ducrocq Vincent

2005-05-01

Full Text Available Abstract Application of test-day models for the genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (covariance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (covariance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate genetic parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (covariance matrices for the test-day model and can provide consistent genetic parameters.

Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles

NARCIS (Netherlands)

Tenghe, A.M.M.; Bouwman, A.C.; Berglund, B.; Strandberg, E.; Blom, J.; Veerkamp, R.F.

2015-01-01

The aim of this study was to define endocrine fertility traits from in-line milk progesterone (P4) records and to estimate genetic parameters for these traits. Correlations of classical fertility (calving interval and calving to first service) and milk production traits with endocrine fertility

Estimation of Genetic and Phenotypic Parameters for Udder Morphology Traits in Different Dairy Sheep Genotypes

Directory of Open Access Journals (Sweden)

Pavol Makovický

2017-01-01

Full Text Available Knowledge of genetic parameters is the basis of sound livestock improvement programmes. Genetic parameters have been estimated for linear udder traits: Udder depth (UD, Cistern depth (CD, Teat position (TP, Teat size (TS and external udder measurements: Rear udder depth (RUD, Cistern depth (CDe, Teat length (TL and Teat angle (TA – 1275 linear assessments (381 ewes and 1185 external udder measurements (355 ewes were included in the analysis for each character of 9 genotypes. Nine breeds and genotypes were included in these experiments: purebred Improved Valachian (IV, Tsigai (T, Lacaune (LC ewes, and IV and T crosses with genetic portion of Lacaune and East Friesian (EF – 25 %, 50 % and 75 %. Primary data were processed using restricted maximum likelihood (REML methodology and the multiple‑trait animal model, using programs REMLF90 and VCE 4.0. High genetic correlations were found between UD and RUD (0.86, CD and CD(e (0.93, TP and TA (0.90, TS and TL (0.94. The highest heritabilities were estimated for exact measurements of TL and CD (0.35–0.39 and subjectively assessed TA and TS (0.32 – 0.33.

Comparison of Genetic Parameters Estimation of Fatty Acids from Gas Chromatography and FT-IR in Holsteins

DEFF Research Database (Denmark)

Poulsen, Nina Aagaard; Eskildsen, Carl Emil; Skov, T.

or on fatty acids data measured from gas chromatography in 371 Danish Holstein cows. Results showed similar heritability estimates and strong genomic correlations for most of the fatty acids. However, for some fatty acids, the choice of data affected the genetic parameter estimation, which may be due...

A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

Energy Technology Data Exchange (ETDEWEB)

Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping

2015-01-15

A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations.

A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

International Nuclear Information System (INIS)

Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping

2015-01-01

A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations

Genetic Parameters for carcass composition and pork quality estimated in a commercial production chain

NARCIS (Netherlands)

Wijk, van H.J.; Arts, D.J.G.; Matthews, J.O.; Webster, M.; Ducro, B.J.; Knol, E.F.

2005-01-01

Breeding goals in pigs are subject to change and are directed much more toward retail carcass yield and meat quality because of the high economic value of these traits. The objective of this study was to estimate genetic parameters of growth, carcass, and meat quality traits. Carcass components

Estimation of Genetic Parameters of Kleiber Ratio and Growth Traits in Kurdish Sheep

Directory of Open Access Journals (Sweden)

Davoud Ali Saghi

2016-11-01

Full Text Available Introduction Kurdish sheep breed is one of the most important native breeds of Iran. They are fat-tailed, large-sized, well adapted to the mountainous regions in northern Khorasan province and mainly raising for meat production under pastoral production system (28. Feed efficiency is a major component in the profitability of the small ruminant enterprise, because quality of range and pasture is low in poor environmental conditions in Iran. Growth rate and feed efficiency are two traits of great economic importance in sheep production and also Kleiber ratio has been suggested to be a useful indicator for these traits (2. There was no information regarding genetic parameters for growth traits in Kurdish sheep. Thus, the main objective of the present research was to estimate (covariance components and genetic parameters for pre- and post-weaning growth traits and Kleiber ratio in Kurdish sheep. Material and Methods In this study, the records of growth traits from 5144 lambs (from 161 rams and 1982 ewes were used. The data were collected during a 17-year period (1996–2013 in Kurdish sheep Breeding Station located in Shirvan city of northern Khorasan province. Traits investigated were average daily gain from birth to weaning (ADG0-3, average daily gain from weaning to six months of age (ADG3-6, average daily gain from six to nine months of age (ADG6-9, average daily gain from nine to twelve months of age (ADG9-12 and Kleiber ratios (KR defined as: KR1=ADG0-3/(BW30.75 KR2=ADG3-6/(BW60.75 KR3=ADG6-9/(BW90.75 KR4=ADG9-12/(BW120.75 Test of significance for the fixed effects to be included in the final functional model for each trait and calculation of least squares means was accomplished using GLM procedure of SAS software (24. The considered fixed effects were year of lambing (1996-2013, sex of lamb (male and female, type of birth (single and twin and age of ewe (1–7 years old. (Co variance components and genetic parameters were estimated applying

Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

Directory of Open Access Journals (Sweden)

Y. H. Lee

2006-12-01

Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

Estimation of genetic parameters and genetic trends for weight and body measurements at birth in sheep populations in Thailand

Directory of Open Access Journals (Sweden)

China Supakorn

2013-02-01

Full Text Available The main objective of this study was to estimate the genetic parameters and genetic trends for birth weight (BW,heart girth (HG, and body length (BL at birth of sheep populations in Thailand. Data were collected during 1998 to 2011 fromfour livestock research and testing stations. Fixed effect testing showed that sex, herd, contemporary group and breed groupgreatly influenced on the investigated traits (P<0.05. The log likelihood ratio test showed that all traits significantly affectedby maternal additive genetic effect as well as covariance between animal effects. Estimated direct heritabilities from multivariate analysis of the model for BW, HG and BL were 0.32, 0.52 and 0.54, while estimated maternal heritabilities were 0.23,0.14 and 0.14, respectively. Positive correlations were found among direct additive genetic (0.29 to 0.97, maternal additivegenetic (0.23 to 0.95, and phenotype (0.18 to 0.96. Direct-maternal correlations within traits (-0.68 for BW, -0.92 for HG and-0.89 for BL and between traits (-0.89 to -0.08 were antagonistic effect. Direct additive genetic trends for BW, HG and BL inthe second period (2005 to 2011 were significantly increased (0.02 kg/year, 0.89 and 0.73 cm/year while maternal additivegenetic trends characteristically depicted significantly decreased (-0.01 kg/year, -0.92 and -0.72 cm/year.

Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model

Energy Technology Data Exchange (ETDEWEB)

Marseguerra, M.; Zio, E.; Canetta, R. [Polytechnic of Milan, Dept. of Nuclear Engineering, Milano (Italy)

2005-07-01

The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)

Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model

International Nuclear Information System (INIS)

Marseguerra, M.; Zio, E.; Canetta, R.

2005-01-01

The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)

Estimation of genetic parameters for reproductive traits in Shall sheep.

Science.gov (United States)

Amou Posht-e-Masari, Hesam; Shadparvar, Abdol Ahad; Ghavi Hossein-Zadeh, Navid; Hadi Tavatori, Mohammad Hossein

2013-06-01

The objective of this study was to estimate genetic parameters for reproductive traits in Shall sheep. Data included 1,316 records on reproductive performances of 395 Shall ewes from 41 sires and 136 dams which were collected from 2001 to 2007 in Shall breeding station in Qazvin province at the Northwest of Iran. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Test of significance to include fixed effects in the statistical model was performed using the general linear model procedure of SAS. The effects of lambing year and ewe age at lambing were significant (Psheep.

Genetic Algorithms for Estimating Effective Parameters in a Lumped Reactor Model for Reactivity Predictions

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico

2001-01-01

The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account

Morphological and agronomical characterization and estimates of genetic parameters of sesbania Scop. (Leguminosae accessions

Directory of Open Access Journals (Sweden)

Veasey E.A.

1999-01-01

Full Text Available Twenty-two accessions of seven Sesbania (Leguminosae species: S. emerus, S. rostrata, S. tetraptera, S. exasperata (annuals, S. grandiflora, S. sesban and S. virgata (perennials, used for ruminant fodder, firewood, wood products, soil improvement, and human food, were investigated, with the aim of characterizing both inter- and intraspecific genetic variability, estimating genetic parameters for the characters evaluated and appraising the forage potential of the accessions. These were planted at the Instituto de Zootecnia, Nova Odessa, SP, Brazil, in a randomized complete block design with 22 treatments and four replications. Seventeen morphological and 17 agronomic characters were evaluated. Genetic parameters coefficient of intraspecific genetic diversity (bi and coefficient of intraspecific genetic variation (CVgi were obtained for the species represented by more than one accession. Highly significant differences were observed among as well as within species for most characters, showing considerable genetic variability. S. exasperata showed intraspecific genetic variability for the largest number of morphological characters. The same was observed for S. sesban for the agronomic characters. Most of the characters gave high bi values, above 0.80, indicating the possibility of selecting superior genotypes. The CVgi values, on the other hand, which indicate the magnitude of the existing genetic variability relative to the character mean, varied according to the species and character evaluated. Differences between annual and perennial species were observed, with higher biomass yields presented by the annuals at the first cut and by the perennials after the second cut, reaching the highest yield at the third cut. The annual species had higher seed production. Accession NO 934 of S. sesban gave the highest biomass yields and regrowth vigor, showing promise as a forage legume plant.

Genetic parameter and breeding value estimation of donkeys' problem-focused coping styles.

Science.gov (United States)

Navas González, Francisco Javier; Jordana Vidal, Jordi; León Jurado, José Manuel; Arando Arbulu, Ander; McLean, Amy Katherine; Delgado Bermejo, Juan Vicente

2018-05-12

Donkeys are recognized therapy or leisure-riding animals. Anecdotal evidence has suggested that more reactive donkeys or those more easily engaging flight mechanisms tend to be easier to train compared to those displaying the natural donkey behaviour of fight. This context brings together the need to quantify such traits and to genetically select donkeys displaying a neutral reaction during training, because of its implication with handler/rider safety and trainability. We analysed the scores for coping style traits from 300 Andalusian donkeys from 2013 to 2015. Three scales were applied to describe donkeys' response to 12 stimuli. Genetic parameters were estimated using multivariate models with year, sex, husbandry system and stimulus as fixed effects and age as a linear and quadratic covariable. Heritabilities were moderate, 0.18 ± 0.020 to 0.21 ± 0.021. Phenotypic correlations between intensity and mood/emotion or response type were negative and moderate (-0.21 and -0.25, respectively). Genetic correlations between the same variables were negative and moderately high (-0.46 and -0.53, respectively). Phenotypic and genetic correlations between mood/emotion and response type were positive and high (0.92 and 0.95, respectively). Breeding values enable selection methods that could lead to endangered breed preservation and genetically selecting donkeys for the uses that they may be most suitable. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimating the kinetic parameters of activated sludge storage using weighted non-linear least-squares and accelerating genetic algorithm.

Science.gov (United States)

Fang, Fang; Ni, Bing-Jie; Yu, Han-Qing

2009-06-01

In this study, weighted non-linear least-squares analysis and accelerating genetic algorithm are integrated to estimate the kinetic parameters of substrate consumption and storage product formation of activated sludge. A storage product formation equation is developed and used to construct the objective function for the determination of its production kinetics. The weighted least-squares analysis is employed to calculate the differences in the storage product concentration between the model predictions and the experimental data as the sum of squared weighted errors. The kinetic parameters for the substrate consumption and the storage product formation are estimated to be the maximum heterotrophic growth rate of 0.121/h, the yield coefficient of 0.44 mg CODX/mg CODS (COD, chemical oxygen demand) and the substrate half saturation constant of 16.9 mg/L, respectively, by minimizing the objective function using a real-coding-based accelerating genetic algorithm. Also, the fraction of substrate electrons diverted to the storage product formation is estimated to be 0.43 mg CODSTO/mg CODS. The validity of our approach is confirmed by the results of independent tests and the kinetic parameter values reported in literature, suggesting that this approach could be useful to evaluate the product formation kinetics of mixed cultures like activated sludge. More importantly, as this integrated approach could estimate the kinetic parameters rapidly and accurately, it could be applied to other biological processes.

Model comparisons and genetic and environmental parameter ...

African Journals Online (AJOL)

arc

Model comparisons and genetic and environmental parameter estimates of growth and the ... breeding strategies and for accurate breeding value estimation. The objectives ...... Sci. 23, 72-76. Van Wyk, J.B., Fair, M.D. & Cloete, S.W.P., 2003.

Estimation of genetic parameters for body weight at different ages in ...

African Journals Online (AJOL)

ONOS

2010-08-09

Aug 9, 2010 ... and yearling weight (YW, n = 1450) of Mehraban sheep, collected during 1995 - 2007 at Mehraban sheep. Breeding Station in Hamedan ... Key words: Mehraban sheep, heritability, genetic correlation, body weight traits. ..... parameters and genetic change for reproduction, weight, and wool characteristic of ...

variance components and genetic parameters for live weight

African Journals Online (AJOL)

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Against this background the present study estimated the (co)variance .... Starting values for the (co)variance components of two-trait models were ..... Estimates of genetic parameters for weaning weight of beef accounting for direct-maternal.

Genetic parameters for ostrich incubation traits in South Africa ...

African Journals Online (AJOL)

Genetic parameters for ostrich incubation traits in South Africa. Z Brand, S Cloete, I Malecki, C Brown. Abstract. Data obtained from a pair-mated ostrich flock located at Oudtshoorn, South Africa, were used to estimate genetic parameters for egg weight (EWT), weight of day-old chicks (CWT), water loss to 21 (WL21) and 35 ...

Genetic parameters and genetic trends in the Chinese × European Tiameslan composite pig line. I. Genetic parameters

Directory of Open Access Journals (Sweden)

Legault Christian

2000-01-01

Full Text Available Abstract Genetic parameters of body weight at 4 (W4 w, 8 (W8 w and 22 (W22 w weeks of age, days from 20 to 100 kg (DT, average backfat thickness at 100 kg (ABT, teat number (TEAT, number of good teats (GTEAT, total number of piglets born (TNB, born alive (NBA and weaned (NW per litter, and birth to weaning survival rate (SURV were estimated in the Chinese × European Tiameslan composite line using restricted maximum likelihood methodology applied to a multiple trait animal model. Performance data from a total of 4 881 males and 4 799 females from 1 341 litters were analysed. Different models were fitted to the data in order to estimate the importance of maternal effects on production traits, as well as genetic correlations between male and female performance. The results showed the existence of significant maternal effects on W4w, W8w and ABT and of variance heterogeneity between sexes for W22w, DT, ABT and GTEAT. Genetic correlations between sexes were 0.79, 0.71 and 0.82, respectively, for W22w, DT and ABT and above 0.90 for the other traits. Heritability estimates were larger than (ABT and TEAT or similar to (other traits average literature values. Some genetic antagonism was evidenced between production traits, particularly W4w, W8w and ABT, and reproductive traits.

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

Science.gov (United States)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

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Maria Gabriela Campolina Diniz Peixoto

2014-05-01

Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Genetic parameters and genetic and phenotypic trends of performance traits of equines from the Brazilian Army

OpenAIRE

Dornelles, Mariana de Almeida; Araújo, Ronyere Olegário de; Everling, Dionéia Magda; Weber, Tomás; Lopes, Jader Silva; Pacheco, Paulo Santana; Breda, Fernanda Cristina; Rorato, Paulo Roberto Nogara

2012-01-01

The objective of this research was to compare the magnitude of genetic parameters (coefficients of heritability and genetic correlation) as estimated by the Restricted Maximum Likelihood (REML) method and Bayesian Inference, and to estimate the genetic and phenotypic trends to the traits height at the withers (HW24) and weight at 24 months of age (W24). The average heritability estimated by Bayesian Inference to HW24 was 0.47, and it was lower than that obtained by REML bi-trait analysis (0.5...

Parameter Estimation

DEFF Research Database (Denmark)

Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian

2011-01-01

of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to generate a set......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....

Estimation of riverbank soil erodibility parameters using genetic ...

Indian Academy of Sciences (India)

Tapas Karmaker

2017-11-07

Nov 7, 2017 ... process. Therefore, this is a study to verify the applicability of inverse parameter ... successful modelling of the riverbank erosion, precise estimation of ..... For this simulation, about 40 iterations are found to attain the convergence. ..... rithm for function optimization: a Matlab implementation. NCSU-IE TR ...

Genetic parameter estimates for carcass traits and visual scores including or not genomic information.

Science.gov (United States)

Gordo, D G M; Espigolan, R; Tonussi, R L; Júnior, G A F; Bresolin, T; Magalhães, A F Braga; Feitosa, F L; Baldi, F; Carvalheiro, R; Tonhati, H; de Oliveira, H N; Chardulo, L A L; de Albuquerque, L G

2016-05-01

The objective of this study was to determine whether visual scores used as selection criteria in Nellore breeding programs are effective indicators of carcass traits measured after slaughter. Additionally, this study evaluated the effect of different structures of the relationship matrix ( and ) on the estimation of genetic parameters and on the prediction accuracy of breeding values. There were 13,524 animals for visual scores of conformation (CS), finishing precocity (FP), and muscling (MS) and 1,753, 1,747, and 1,564 for LM area (LMA), backfat thickness (BF), and HCW, respectively. Of these, 1,566 animals were genotyped using a high-density panel containing 777,962 SNP. Six analyses were performed using multitrait animal models, each including the 3 visual scores and 1 carcass trait. For the visual scores, the model included direct additive genetic and residual random effects and the fixed effects of contemporary group (defined by year of birth, management group at yearling, and farm) and the linear effect of age of animal at yearling. The same model was used for the carcass traits, replacing the effect of age of animal at yearling with the linear effect of age of animal at slaughter. The variance and covariance components were estimated by the REML method in analyses using the numerator relationship matrix () or combining the genomic and the numerator relationship matrices (). The heritability estimates for the visual scores obtained with the 2 methods were similar and of moderate magnitude (0.23-0.34), indicating that these traits should response to direct selection. The heritabilities for LMA, BF, and HCW were 0.13, 0.07, and 0.17, respectively, using matrix and 0.29, 0.16, and 0.23, respectively, using matrix . The genetic correlations between the visual scores and carcass traits were positive, and higher correlations were generally obtained when matrix was used. Considering the difficulties and cost of measuring carcass traits postmortem, visual scores of

Estimates of genetic parameters, genetic trends, and inbreeding in a crossbred dairy sheep research flock in the United States.

Science.gov (United States)

Murphy, T W; Berger, Y M; Holman, P W; Baldin, M; Burgett, R L; Thomas, D L

2017-10-01

For the past 2 decades, the Spooner Agriculture Research Station (ARS) of the University of Wisconsin-Madison operated the only dairy sheep research flock in North America. The objectives of the present study were to 1) obtain estimates of genetic parameters for lactation and reproductive traits in dairy ewes, 2) estimate the amount of genetic change in these traits over time, and 3) quantify the level of inbreeding in this flock over the last 20 yr. Multiple-trait repeatability models (MTRM) were used to analyze ewe traits through their first 6 parities. The first MTRM jointly analyzed milk (180-d-adjusted milk yield [180d MY]), fat (180-d-adjusted fat yield [180d FY]), and protein (180-d-adjusted protein yield [180d PY]) yields adjusted to 180 d of lactation; number of lambs born per ewe lambing (NLB); and lactation average test-day somatic cell score (LSCS). A second MTRM analyzed 180d MY, NLB, LSCS, and percentage milk fat (%F) and percentage milk protein (%P). The 3 yield traits were moderately heritable (0.26 to 0.32) and strongly genetically correlated (0.91 to 0.96). Percentage milk fat and %P were highly heritable (0.53 and 0.61, respectively) and moderately genetically correlated (0.61). Milk yield adjusted to 180 d was negatively genetically correlated with %F and %P (-0.31 and -0.34, respectively). Ewe prolificacy was not significantly ( > 0.67) genetically correlated with yield traits, %P, or LSCS but lowly negatively correlated with %F (-0.26). Lactation somatic cell score was unfavorably genetically correlated with yield traits (0.28 to 0.39) but not significantly ( > 0.09) correlated with %F, %P, and NLB. Within-trait multiple-trait models through the first 4 parities revealed that 180d MY, 180d FY, 180d PY, %F, and %P were strongly genetically correlated across parity (0.67 to 1.00). However, the genetic correlations across parity for NLB and LSCS were somewhat lower (0.51 to 0.96). Regressing predicted breeding values for 180d MY, without and with

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

Science.gov (United States)

Michael J. Firko; Jane Leslie Hayes

1990-01-01

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of heritability (h2) of resistance. Sibling analysis and...

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

Directory of Open Access Journals (Sweden)

C. I. Cho

2016-05-01

Full Text Available The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs, and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK, fat yield (FAT, protein yield (PROT, and solids-not-fat yield (SNF. The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP of the third to fifth order (L3–L5, fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order. The residual variances in the models were either homogeneous (HOM or heterogeneous (15 classes, HET15; 60 classes, HET60. A total of nine models (3 orders of polynomials×3 types of residual variance including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC and/or Schwarz Bayesian information criteria (BIC statistics to identify the model(s of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF and L4-HET15 (FAT, which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first

Crop yield, genetic parameter estimation and selection of sacha inchi in central Amazon

Directory of Open Access Journals (Sweden)

Mágno Sávio Ferreira Valente

2017-06-01

Full Text Available In Brazil, sacha inchi oil is produced by hand from plant materials with no breeding or detailed information about the chemical composition of seeds. In addition, most of the current information on the agronomic traits of this species originates from research carried out in the Peruvian Amazon. In order to promote the research and cultivation of sacha inchi in the Brazilian territory, this study aimed to analyze, in the central Amazon region, different accessions of this oilseed for characteristics of production and quality of fruits and seeds, as well as to estimate genetic parameters, through mixed models, with identification of superior accessions, for breeding purposes. A total of 37 non-domesticated accessions were evaluated in a randomized block design, with five replications and two plants per plot. The average oil content in seeds was 29.07 % and unsaturated fatty acids amounted to 91.5 % of the total fat content. For the yield traits, the estimates of individual broad-sense heritability were moderate (~0.33, while the heritability based on the average of progenies resulted in a selective accuracy of approximately 0.85. The use of the selection index provided simultaneous gains for yield traits (> 40 % and oil yield. A high genetic variability was observed for the main traits of commercial interest for the species, as well as promising perspectives for the development of superior varieties for agro-industrial use.

Genetic Parameters of Reproductive and Meat Quality Traits in Korean Berkshire Pigs

Directory of Open Access Journals (Sweden)

Joon-Ho Lee

2015-10-01

Full Text Available Genetic parameters of Berkshire pigs for reproduction, carcass and meat quality traits were estimated using the records from a breeding farm in Korea. For reproduction traits, 2,457 records of the total number of piglets born (TNB and the number of piglets born alive (NBA from 781 sows and 53 sires were used. For two carcass traits which are carcass weight (CW and backfat thickness (BF and for 10 meat quality traits which are pH value after 45 minutes (pH45m, pH value after 24 hours (pH24h, lightness in meat color (LMC, redness in meat color (RMC, yellowness in meat color (YMC, moisture holding capacity (MHC, drip loss (DL, cooking loss (CL, fat content (FC, and shear force value (SH, 1,942 pig records were used to estimate genetic parameters. The genetic parameters for each trait were estimated using VCE program with animal model. Heritability estimates for reproduction traits TNB and NBA were 0.07 and 0.06, respectively, for carcass traits CW and BF were 0.37 and 0.57, respectively and for meat traits pH45m, pH24h, LMC, RMC, YMC, MHC, DL, CL, FC, and SH were 0.48, 0.15, 0.19, 0.36, 0.28, 0.21, 0.33, 0.45, 0.43, and 0.39, respectively. The estimate for genetic correlation coefficient between CW and BF was 0.27. The Genetic correlation between pH24h and meat color traits were in the range of −0.51 to −0.33 and between pH24h and DL and SH were −0.41 and −0.32, respectively. The estimates for genetic correlation coefficients between reproductive and meat quality traits were very low or zero. However, the estimates for genetic correlation coefficients between reproductive traits and drip and cooking loss were in the range of 0.12 to 0.17 and −0.14 to −0.12, respectively. As the estimated heritability of meat quality traits showed medium to high heritability, these traits may be applicable for the genetic improvement by continuous measurement. However, since some of the meat quality traits showed negative genetic correlations with

Estimates of genetic parameters and environmental effects for measures of hunting performance in Finnish hounds.

Science.gov (United States)

Liinamo, A E; Karjalainen, L; Ojala, M; Vilva, V

1997-03-01

Data from field trials of Finnish Hounds between 1988 and 1992 in Finland were used to estimate genetic parameters and environmental effects for measures of hunting performance using REML procedures and an animal model. The original data set included 28,791 field trial records from 5,666 dogs. Males and females had equal hunting performance, whereas experience acquired by age improved trial results compared with results for young dogs (P Hounds with respect to their hunting ability should be based on animal model BLUP methods instead of mere performance testing. The evaluation system of field trials should also be revised for more reliability.

Genetic parameters and relationships of faecal worm egg count with ...

African Journals Online (AJOL)

user

2014-07-09

Jul 9, 2014 ... Genetic parameter estimates for FEC and its relationship with wool traits were assessed in ...... (Table 4). Studies in Australian Merinos yielded a lower rg estimate of −0.20 where regression analysis was ..... Stochastic model.

Estimating non-genetic and genetic parameters of pre-weaning growth traits in Raini Cashmere goat.

Science.gov (United States)

Barazandeh, Arsalan; Moghbeli, Sadrollah Molaei; Vatankhah, Mahmood; Mohammadabadi, Mohammadreza

2012-04-01

Data and pedigree information used in the present study were 3,022 records of kids obtained from the breeding station of Raini goat. The studied traits were birth weight (BW), weaning weight (WW), average daily gain from birth to weaning (ADG) and Kleiber ratio at weaning (KR). The model included the fixed effects of sex of kid, type of birth, age of dam, year of birth, month of birth, and age of kid (days) as covariate that had significant effects, and random effects direct additive genetic, maternal additive genetic, maternal permanent environmental effects and residual. (Co) variance components were estimated using univariate and multivariate analysis by WOMBAT software applying four animal models including and ignoring maternal effects. Likelihood ratio test used to determine the most appropriate models. Heritability (h(a)(2)) estimates for BW, WW, ADG, and KR according to suitable model were 0.12 ± 0.05, 0.08 ± 0.06, 0.10 ± 0.06, and 0.06 ± 0.05, respectively. Estimates of the proportion of maternal permanent environmental effect to phenotypic variance (c(2)) were 0.17 ± 0.03, 0.07 ± 0.03, and 0.07 ± 0.03 for BW, WW, and ADG, respectively. Genetic correlations among traits were positive and ranged from 0.53 (BW-ADG) to 1.00 (WW-ADG, WW-KR, and ADG-KR). The maternal permanent environmental correlations between BW-WW, BW-ADG, and WW-ADG were 0.54, 0.48, and 0.99, respectively. Results indicated that maternal effects, especially maternal permanent environmental effects are an important source of variation in pre-weaning growth trait and ignoring those in the model redound incorrect genetic evaluation of kids.

Genetic parameters for longevity in Holteins cows

Directory of Open Access Journals (Sweden)

Elisa Junqueira Oliveira

2012-12-01

Full Text Available The milk yield has been the most selected trait in dairy cattle breeding programs. However various studies have shown a decline in adaptive and longevity traits in herds that are under selection for improving production, especially in taurine breeds, as the Holstein, who was highly selected for milk production. The aim of this study was to estimate genetic parameters for first lactation 305-day milk yield (Y305 and for longevity traits and to verify the association among them, in high production Holstein cows. The data sets used were from Agrindus Farm, with calving occurring between 1989 and 2005. The traits analyzed were Y305, productive life (PL, calculated as the length of lactation days from the first day of lactation until the culling, and age at culling (AC. Variance components were estimated by Restricted Maximum Likelihood, applying multi-trait animal model. Heritability estimates for Y305, PL and AC were, respectively, 0.35, 0.07 and 0.10. Heritability estimates for PL and AC suggest small genetic variability to get genetic gains by direct selection for these traits, because they are influenced by decisions of voluntary and involuntary culling, being largely affected by factors related to the environment. It is difficult to measure these traits because it is necessary to evaluate culling of animals and causes of culling. The magnitude of the heritability estimate for Y305 evidences the existence of reasonable additive genetic variability, which allows efficiency by selecting for this trait. The genetic correlations between Y305 and PL was 0.02 and between Y305 and AC was 0.01, suggesting small genetic association between Y305 and longevity traits. In this case, the selection for Y305 is viable due to high heritability estimate and the favorable and almost null genetic correlation between Y305 and the longevity traits. Some studies have used to analyze longevity traits as threshold and since this trait has great economic importance, it

Marker-based estimation of genetic parameters in genomics.

Directory of Open Access Journals (Sweden)

Zhiqiu Hu

Full Text Available Linear mixed model (LMM analysis has been recently used extensively for estimating additive genetic variances and narrow-sense heritability in many genomic studies. While the LMM analysis is computationally less intensive than the Bayesian algorithms, it remains infeasible for large-scale genomic data sets. In this paper, we advocate the use of a statistical procedure known as symmetric differences squared (SDS as it may serve as a viable alternative when the LMM methods have difficulty or fail to work with large datasets. The SDS procedure is a general and computationally simple method based only on the least squares regression analysis. We carry out computer simulations and empirical analyses to compare the SDS procedure with two commonly used LMM-based procedures. Our results show that the SDS method is not as good as the LMM methods for small data sets, but it becomes progressively better and can match well with the precision of estimation by the LMM methods for data sets with large sample sizes. Its major advantage is that with larger and larger samples, it continues to work with the increasing precision of estimation while the commonly used LMM methods are no longer able to work under our current typical computing capacity. Thus, these results suggest that the SDS method can serve as a viable alternative particularly when analyzing 'big' genomic data sets.

Estimation of Genetic Parameters for Real-time Ultrasound Measurements for Hanwoo Cows at Different Ages and Pregnancy Status

Directory of Open Access Journals (Sweden)

J. H. Lee

2014-02-01

Full Text Available The purpose of this study was to estimate genetic parameters of ultrasound measurements for longissimus dorsi muscle area (LMA, backfat thickness (BFT, and marbling score (MS in Hanwoo cows (N = 3,062 at the ages between 18 and 42 months. Data were collected from 100 Hanwoo breeding farms in Gyeongbuk province, Korea, in 2007 and 2008. The cows were classified into four different age groups, i.e. 18 to 22 months (the first pregnancy period, 23 to 27 (the first parturition, 28 to 32 (the second pregnancy, and 33 to 42 (the second parturition, respectively. For each age group, a multi-trait animal model was used to estimate variance components and heritabilities of the three traits. The averages of LMA, BFT, and MS measurements across the cows of all age groups were 50.1 cm2, 4.62 mm, and 3.04, respectively and heritability estimates were 0.09, 0.10, and 0.08 for the respective traits. However, when the data were analyzed in different age groups, heritability estimates of LMA and BFT were 0.24 and 0.47, respectively, for the cows of 18 to 22 months of age, and 0.21 for MS in the 28 to 32 months old cows. When the cows of all age groups were used, the estimates of genetic (phenotypic correlations were 0.43 (0.35, −0.06 (0.34 and 0.21 (0.32 between LMA and BFT, LMA and MS, and BFT and MS, respectively. However, in the cow age group between 28 and 32 (18 and 22 months, the estimates of genetic (phenotypic correlations were 0.05 (0.29, −0.15 (0.24 and 0.38 (0.24, for the respective pairs of traits. These results suggest that genetic, environmental, and phenotypic variations differ depending on cow age, such that care must be taken when ultrasound measurements are applied to selection of cows for meat quality.

Genetic parameters estimate for milk and mozzarella cheese yield, fat and protein percentage in dairy buffaloes in Brazil

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H. Tonhati

2010-02-01

Full Text Available The aim of this study was analyze the (covariance components and genetic and phenotypic relationships in the following traits: accumulated milk yield at 270 days (MY270, observed until 305 days of lactation; accumulated milk yield at 270 days (MY270/ A and at 305 days (MY305, observed until 335 days of lactation; mozzarella cheese yield (MCY and fat (FP and protein (PP percentage, observed until 335 days of lactation. The (covariance components were estimated by Restricted Maximum Likelihood methodology in analyses single, two and three-traits using animal models. Heritability estimated for MY270, MY270/A, MY305, MCY, FP and PP were 0.22; 0.24, 0.25, 0.14, 0.29 and 0.40 respectively. The genetic correlations between MCY and the variables MY270, MY270/A, MY305, PP and FP was: 0.85; 1.00; 0.89; 0.14 and 0.06, respectively. This way, the selection for the production of milk in long period should increase MCY. However, in the search of animals that produce milk with quality, the genetic parameters suggest that another index should be composed allying these studied traits.

Estimates of Genetic Parameters of Production Traits for Khuzestan Buffaloes of Iran using Repeated-Records Animal Model

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Maryam Baharizadeh

2012-10-01

Full Text Available Buffalo milk yield records were obtained from monthly records of the Animal Breeding Organization of Iran from 1992 to 2009 in 33 herds raised in the Khuzestan province. Variance components, heritability and repeatability were estimated for milk yield, fat yield, fat percentage, protein yield and protein percentage. These estimates were carried out through single trait animal model using DFREML program. Herd-year-season was considered as fixed effect in the model. For milk production traits, age at calving was fitted as a covariate. The additive genetic and permanent environmental effects were also included in the model. The mean values (±SD for milk yield, fat yield, fat percentage, protein yield and protein percentage were 2285.08±762.47 kg, 144.35±54.86 kg, 6.25±0.90%, 97.30±26.73 kg and 4.19±0.27%, respectively. The heritability (±SE of milk yield, fat yield, fat percentage, protein yield and protein percentage were 0.093±0.08, 0.054±0.06, 0.043±0.05, 0.093±0.16 and zero, respectively. These estimates for repeatability were 0.272, 0.132, 0.043, 0.674 and 0.0002, respectively. Lower values of genetic parameter estimates require more data and reliable pedigree records.

Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk

DEFF Research Database (Denmark)

Buitenhuis, Albert Johannes; Sundekilde, Ulrik; Poulsen, Nina Aagaard

2013-01-01

Small components and metabolites in milk are significant for the utilization of milk, not only in dairy food production but also as disease predictors in dairy cattle. This study focused on estimation of genetic parameters and detection of quantitative trait loci for metabolites in bovine milk. F...... for lactic acid to >0.8 for orotic acid and β-hydroxybutyrate. A single SNP association analysis revealed 7 genome-wide significant quantitative trait loci [malonate: Bos taurus autosome (BTA)2 and BTA7; galactose-1-phosphate: BTA2; cis-aconitate: BTA11; urea: BTA12; carnitine: BTA25...

Genetic parameters and genetic and phenotypic trends of performance traits of equines from the Brazilian Army

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Mariana de Almeida Dornelles

2012-06-01

Full Text Available The objective of this research was to compare the magnitude of genetic parameters (coefficients of heritability and genetic correlation as estimated by the Restricted Maximum Likelihood (REML method and Bayesian Inference, and to estimate the genetic and phenotypic trends to the traits height at the withers (HW24 and weight at 24 months of age (W24. The average heritability estimated by Bayesian Inference to HW24 was 0.47, and it was lower than that obtained by REML bi-trait analysis (0.52; however, the value estimated to W24 (0.39 was higher than that obtained by REML bi-trait analysis (0.38. The genetic correlation estimate between W24 and HW24 traits obtained by the REML method (0.66 was lower than that obtained by the Bayesian Inference Method (0.72. From the regression of the average additive genetic merit in the year of birth of the animals, it was found that the averaged genetic values of the animals for HW24 showed a genetic trend near zero (-0.0008cm/year, and the averaged genetic values for W24 showed a negative trend of -0.38 kg/year. The values to the direct heritability estimated for HW24 and W24 suggest that the direct selection for these traits can provide genetic gain in this population. The genetic correlation between the traits, high and positive, suggests that the selection for HW24 should promote increase in W24 at this age. The genetic trends obtained for the traits studied, near zero, indicate that the selection performed produced a slight reduction of the weight of the animals at 24 months of age; however, it did not promote increase in height at the wither at this same age, in this population.

Genetic parameters for litter size in Black Slavonian pigs

Energy Technology Data Exchange (ETDEWEB)

Skorput, D.; Gorjanc, G.; Dikic, M.; Lujovic, Z.

2014-06-01

The objective of this study was to estimate genetic parameters for litter size of Black Slavonian pigs using the repeatability, multiple trait, and random regression models, and to consider the possibility to increase litter size in Black Slavonian pigs by selection. A total of 4,733 litter records from the first to the sixth parity from sows that farrowed between January 1998 and December 2010 were included in the analysis. Individual record consisted of the following variables: breeding organisation (eight regions), parity (1-6), service boar, and farrowing season (monthyear interaction). Estimation of all the covariance components with three different models was based on the residual maximum likelihood method. Estimate of additive genetic variance and heritability for number of piglets born alive with repeatability model was 0.23 and 0.10, respectively. Estimates of additive genetic variance with multiple trait and random regression model were in a wider range from 0.05 to 0.65 across parities, and heritabilities were estimated in the range between 0.03 and 0.26. Estimates of phenotypic and additive genetic correlations were much smoother with random regression model in comparison with multiple trait model. Due to unexpected changes of variances along trajectory obtained with multiple trait and random regression model, the best option for genetic evaluation of litter size for now could be the use of repeatability model. With increasing number of data with proper data structure alternative modelling of litter size of Black Slavonian pig using multiple trait and random regression model could be taken into consideration. (Author)

Genetic parameters for litter size in Black Slavonian pigs

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Dubravko Skorput

2014-02-01

Full Text Available The objective of this study was to estimate genetic parameters for litter size of Black Slavonian pigs using the repeatability, multiple trait, and random regression models, and to consider the possibility to increase litter size in Black Slavonian pigs by selection. A total of 4733 litter records from the first to the sixth parity from sows that farrowed between January 1998 and December 2010 were included in the analysis. Individual record consisted of the following variables: breeding organisation (eight regions, parity (1-6, service boar, and farrowing season (month-year interaction. Estimation of all the covariance components with three different models was based on the residual maximum likelihood method. Estimate of additive genetic variance and heritability for number of piglets born alive with repeatability model was 0.23 and 0.10, respectively. Estimates of additive genetic variance with multiple trait and random regression model were in a wider range from 0.05 to 0.65 across parities, and heritabilities were estimated in the range between 0.03 and 0.26. Estimates of phenotypic and additive genetic correlations were much smoother with random regression model in comparison with multiple trait model. Due to unexpected changes of variances along trajectory obtained with multiple trait and random regression model, the best option for genetic evaluation of litter size for now could be the use of repeatability model. With increasing number of data with proper data structure alternative modelling of litter size of Black Slavonian pig using multiple trait and random regression model could be taken into consideration.

Estimation of genetic parameters for morphological and functional traits in a Menorca horse population

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Sole, M.; Cervantes, I.; Gutierrez, J. P.; Gomez, M. D.; Valera, M.

2014-06-01

Functional conformation and performance in Classic and Menorca Dressage are the main selection criteria in the Menorca Horse breeding program. Menorca Dressage is an alternative Classical Dressage discipline which is exclusive of the Menorca Island, but including a series of movements that the animals perform in the traditional festivities called Jaleo Menorquin. One of these movements involves the horse raising its forelimbs and standing or walking on its hindlimbs, which is called el bot. To make the Menorca horse breed more competitive in the equestrian market, it is necessary to understand the genetic background that characterizes the aptitude for Menorca Dressage and its relationship with conformation traits. The analysed data consisted of 15 conformation traits from 347 Menorca horses (200 males and 147 females), with 1,550 performance records in Menorca Dressage competitions. Genetic parameters were estimated using linear and threshold animal models. The heritabilities for heights and lengths were high (0.45-0.76), those for angulations and binary conformation traits were low to moderate (0.10-0.36) as were the scores for dressage performance (0.13-0.21). The results suggest that the analyzed traits could be used as an efficient tool for selecting breeding horses. (Author)

Estimation of genetic parameters for morphological and functional traits in a Menorca horse population

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Marina Solé

2014-01-01

Full Text Available Functional conformation and performance in Classic and Menorca Dressage are the main selection criteria in the Menorca Horse breeding program. Menorca Dressage is an alternative Classical Dressage discipline which is exclusive of the Menorca Island, but including a series of movements that the animals perform in the traditional festivities called “Jaleo Menorquín”. One of these movements involves the horse raising its forelimbs and standing or walking on its hindlimbs, which is called “el bot”. To make the Menorca horse breed more competitive in the equestrian market, it is necessary to understand the genetic background that characterizes the aptitude for Menorca Dressage and its relationship with conformation traits. The analysed data consisted of 15 conformation traits from 347 Menorca horses (200 males and 147 females, with 1,550 performance records in Menorca Dressage competitions. Genetic parameters were estimated using linear and threshold animal models. The heritabilities for heights and lengths were high (0.45-0.76, those for angulations and binary conformation traits were low to moderate (0.10-0.36 as were the scores for dressage performance (0.13-0.21. The results suggest that the analyzed traits could be used as an efficient tool for selecting breeding horses.

A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference Genetics Selection Evolution 2010, 42:29

DEFF Research Database (Denmark)

Ødegård, Jørgen; Meuwissen, Theo HE; Heringstad, Bjørg

2010-01-01

Background In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where...... records exist for the parents). Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to the sire-dam model). Conclusions The new algorithm to estimate genetic parameters via Gibbs sampling solves the bias problems typically occurring in animal...... individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (co)variance components within an animal threshold model framework. Methods In the proposed algorithm, individuals are classified as either "informative...

Genetic divergence of rubber tree estimated by multivariate techniques and microsatellite markers

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Lígia Regina Lima Gouvêa

2010-01-01

Full Text Available Genetic diversity of 60 Hevea genotypes, consisting of Asiatic, Amazonian, African and IAC clones, and pertaining to the genetic breeding program of the Agronomic Institute (IAC, Brazil, was estimated. Analyses were based on phenotypic multivariate parameters and microsatellites. Five agronomic descriptors were employed in multivariate procedures, such as Standard Euclidian Distance, Tocher clustering and principal component analysis. Genetic variability among the genotypes was estimated with 68 selected polymorphic SSRs, by way of Modified Rogers Genetic Distance and UPGMA clustering. Structure software in a Bayesian approach was used in discriminating among groups. Genetic diversity was estimated through Nei's statistics. The genotypes were clustered into 12 groups according to the Tocher method, while the molecular analysis identified six groups. In the phenotypic and microsatellite analyses, the Amazonian and IAC genotypes were distributed in several groups, whereas the Asiatic were in only a few. Observed heterozygosity ranged from 0.05 to 0.96. Both high total diversity (H T' = 0.58 and high gene differentiation (Gst' = 0.61 were observed, and indicated high genetic variation among the 60 genotypes, which may be useful for breeding programs. The analyzed agronomic parameters and SSRs markers were effective in assessing genetic diversity among Hevea genotypes, besides proving to be useful for characterizing genetic variability.

Using genetic data to estimate diffusion rates in heterogeneous landscapes.

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Roques, L; Walker, E; Franck, P; Soubeyrand, S; Klein, E K

2016-08-01

Having a precise knowledge of the dispersal ability of a population in a heterogeneous environment is of critical importance in agroecology and conservation biology as it can provide management tools to limit the effects of pests or to increase the survival of endangered species. In this paper, we propose a mechanistic-statistical method to estimate space-dependent diffusion parameters of spatially-explicit models based on stochastic differential equations, using genetic data. Dividing the total population into subpopulations corresponding to different habitat patches with known allele frequencies, the expected proportions of individuals from each subpopulation at each position is computed by solving a system of reaction-diffusion equations. Modelling the capture and genotyping of the individuals with a statistical approach, we derive a numerically tractable formula for the likelihood function associated with the diffusion parameters. In a simulated environment made of three types of regions, each associated with a different diffusion coefficient, we successfully estimate the diffusion parameters with a maximum-likelihood approach. Although higher genetic differentiation among subpopulations leads to more accurate estimations, once a certain level of differentiation has been reached, the finite size of the genotyped population becomes the limiting factor for accurate estimation.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

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Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

Science.gov (United States)

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.

Approximate maximum likelihood estimation for population genetic inference.

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Bertl, Johanna; Ewing, Gregory; Kosiol, Carolin; Futschik, Andreas

2017-11-27

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.

Genetic estimation of hot carcass weight in indigenous Matebele ...

African Journals Online (AJOL)

Genetic parameter estimation for simple carcass traits has been confined to the improved goat breeds worldwide unlike in the unimproved breeds in developing countries where goats are numerous. Variance components for additive direct, additive maternal, permanent environmental maternal effects, the covariance ...

Genetic parameters of performance traits in Sul-Mato-Grossenses naturalized sheep

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Daniele Portela de Oliveira

2014-02-01

Full Text Available Estimates of genetic parameters are important to study characteristics that are to be included in a breeding program of a genetic group. The information of 594 weights from 211 lambs of a genetic group of naturalized Sul-mato-grossenses sheep belonging to Manoel de Barros Foundation and breeding at Centro Tecnologico de Ovinos from Anhanguera-Uniderp University was used. The estimation of variance components in unicaracter and bicaracter analysis were carried out through Bayesian inference. Estimates of heritability ranged from unicaracter analyses (0.22 to 0.47 and the bicaracter analyses (0.13 to 0.78. The maternal environmental permanent effect was higher in birth weight and average daily gain from birth to 50 days in 24.2% and 19.5%, respectively, in the observed variation. Estimates of heritability, maternal environmental permanent effect participation, phenotypic and genetic correlations indicate that selection for average daily gain from birth to 90 days would imply increases in weight at 50 days, weight at 90 days and average daily gain from 50 to 90 days of lambs with no significant increase in birth weight and average daily gain birth at 50 days.

A termination criterion for parameter estimation in stochastic models in systems biology.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven

2015-11-01

Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model. Copyright © 2015. Published by Elsevier Ireland Ltd.

Genetic Parameter Estimates of Carcass Traits under National Scale Breeding Scheme for Beef Cattle

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ChangHee Do

2016-08-01

Full Text Available Carcass and price traits of 72,969 Hanwoo cows, bulls and steers aged 16 to 80 months at slaughter collected from 2002 to 2013 at 75 beef packing plants in Korea were analyzed to determine heritability, correlation and breeding value using the Multi-Trait restricted maximum likelihood (REML animal model procedure. The traits included carcass measurements, scores and grades at 24 h postmortem and bid prices at auction. Relatively high heritability was found for maturity (0.41±0.031, while moderate heritability estimates were obtained for backfat thickness (0.20±0.018, longissimus muscle (LM area (0.23±0.020, carcass weight (0.28±0.019, yield index (0.20±0.018, yield grade (0.16±0.017, marbling (0.28±0.021, texture (0.14±0.016, quality grade (0.26±0.016 and price/kg (0.24±0.025. Relatively low heritability estimates were observed for meat color (0.06±0.013 and fat color (0.06±0.012. Heritability estimates for most traits were lower than those in the literature. Genetic correlations of carcass measurements with characteristic scores or quality grade of carcass ranged from −0.27 to +0.21. Genetic correlations of yield grade with backfat thickness, LM area and carcass weight were 0.91, −0.43, and −0.09, respectively. Genetic correlations of quality grade with scores of marbling, meat color, fat color and texture were −0.99, 0.48, 0.47, and 0.98, respectively. Genetic correlations of price/kg with LM area, carcass weight, marbling, meat color, texture and maturity were 0.57, 0.64, 0.76, −0.41, −0.79, and −0.42, respectively. Genetic correlations of carcass price with LM area, carcass weight, marbling and texture were 0.61, 0.57, 0.64, and −0.73, respectively, with standard errors ranging from ±0.047 to ±0.058. The mean carcass weight breeding values increased by more than 8 kg, whereas the mean marbling scores decreased by approximately 0.2 from 2000 through 2009. Overall, the results suggest that genetic improvement of

Genetic parameters and relationships of faecal worm egg count with ...

African Journals Online (AJOL)

The costs of internal parasite control and treatment are potentially very high in grazing sheep. Faecal worm egg count (FEC) has been suggested as a suitable criterion for selection for resistance to nematode infestation in livestock. Genetic parameter estimates for FEC and its relationship with wool traits were assessed in ...

Estimation of Genetic Parameters for Direct and Maternal Effects in Growth Traits of Sangsari Sheep Using Gibbs Sampling

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Zohreh Yousefi

2016-11-01

Full Text Available Introduction Small ruminants, especially native breed types, play an important role in livelihoods of a considerable part of human population in the tropics from socio-economic aspects. Therefore, integrated attempt in terms of management and genetic improvement to enhance production is of crucial importance. Knowledge of genetic variation and co-variation among traits is required for both the design of effective sheep breeding programs and the accurate prediction of genetic progress from these programs. Body weight and growth traits are one of the economically important traits in sheep production, especially in Iran where lamb sale is the main source of income for sheep breeders while other products are in secondary importance. Although mutton is the most important source of protein in Iran, meat production from the sheep does not cover the increasing consumer demand. On the other hand, increase in sheep number to increase meat production has been limited by low quality and quantity of forage range. Therefore, enhancing meat production should be achieved by selecting the animals that have maximum genetic merit as next generation parents. To design an efficient improvement program and genetic evaluation system for maximization response to selection for economically important traits, accurate estimates of the genetic parameters and the genetic relationships between the traits are necessary. Studies of various sheep breeds have shown that both direct and maternal genetic influences are of importance for lamb growth. When growth traits are included in the breeding goal, both direct and maternal genetic effects should be taken into account in order to achieve optimum genetic progress. The objective of this study was to estimate the variance components and heritability, for growth traits, by fitting six animal models in the Sangsari sheep using Gibbs sampling. Material and Method Sangsari is a fat-tailed and relatively small sized breed of sheep

In silico exploration of the impact of pasture larvae contamination and anthelmintic treatment on genetic parameter estimates for parasite resistance in grazing sheep.

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Laurenson, Y C S M; Kyriazakis, I; Bishop, S C

2012-07-01

A mathematical model was developed to investigate the impact of level of Teladorsagia circumcincta larval pasture contamination and anthelmintic treatment on genetic parameter estimates for performance and resistance to parasites in sheep. Currently great variability is seen for published correlations between performance and resistance, with estimates appearing to vary with production environment. The model accounted for host genotype and parasitism in a population of lambs, incorporating heritable between-lamb variation in host-parasite interactions, with genetic independence of input growth and immunological variables. An epidemiological module was linked to the host-parasite interaction module via food intake (FI) to create a grazing scenario. The model was run for a population of lambs growing from 2 mo of age, grazing on pasture initially contaminated with 0, 1,000, 3,000, or 5,000 larvae/kg DM, and given either no anthelmintic treatment or drenched at 30-d intervals. The mean population values for FI and empty BW (EBW) decreased with increasing levels of initial larval contamination (IL(0)), with non-drenched lambs having a greater reduction than drenched ones. For non-drenched lambs the maximum mean population values for worm burden (WB) and fecal egg count (FEC) increased and occurred earlier for increasing IL(0), with values being similar for all IL(0) at the end of the simulation. Drenching was predicted to suppress WB and FEC, and cause reduced pasture contamination. The heritability of EBW for non-drenched lambs was predicted to be initially high (0.55) and decreased over time with increasing IL(0), whereas drenched lambs remained high throughout. The heritability of WB and FEC for all lambs was initially low (∼0.05) and increased with time to ∼0.25, with increasing IL(0) leading to this value being reached at faster rates. The genetic correlation between EBW and FEC was initially ∼-0.3. As time progressed the correlation tended towards 0, before

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication

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Madsen Per

2007-07-01

Full Text Available Abstract In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.

Genetic parameters on Bali cattle progeny test population

Science.gov (United States)

Hariansyah, A. R.; Raharjo, A.; Zainuri, A.; Parwoto, Y.; Prasetiyo, D.; Prastowo, S.; Widyas, N.

2018-03-01

Bali cattle (Bos javanicus) is Indonesian indigenous cattle with having superior genetics potential on fitness traits in tropical environment and low feed quality. Bali Cattle Breeding Center Pulukan Indonesia conducted progeny test per annum in order to select bulls using offspring’s phenotype. This paper aimed to estimate the genetic parameters of yearling weight in Bali cattle progeny test populations and to observe the variation between periods in the above breeding center. Data were collected from the year of 2013 to 2014. There were four bulls (3 tests, 1 AI control) in 2013 and five bulls (4 tests, 1 AI) in 2014. Thirty breeding females were allocated per paddock per bull and allowed to mate naturally. In total 80 and 104 offspring’s records were obtained from 2013 and 2014 data, respectively. We built half-sib family model to estimate the additive genetic variance due to the sire and later estimate the breeding value (EBV) of each sire. Results showed that in 2013 the heritability (h2) for yearling weight was 0.19 while in 2014 was 0.79. In both years, tested bulls had higher EBV compared to the control bulls. The remarkable difference of heritability between years was due to the variations among bull candidates which might differ every year with regards to their origins. The fact that the EBV of tested bulls were higher than the control bulls gave us insight that despite the conservation policy and the continuous departure of Bali cattle bulls outside the Island, the population could still maintain its genetic quality.

Genetic parameters and path analysis in cowpea genotypes grown in the Cerrado/Pantanal ecotone.

Science.gov (United States)

Lopes, K V; Teodoro, P E; Silva, F A; Silva, M T; Fernandes, R L; Rodrigues, T C; Faria, T C; Corrêa, A M

2017-05-18

Estimating genetic parameters in plant breeding allows us to know the population potential for selecting and designing strategies that can maximize the achievement of superior genotypes. The objective of this study was to evaluate the genetic potential of a population of 20 cowpea genotypes by estimating genetic parameters and path analysis among the traits to guide the selection strategies. The trial was conducted in randomized block design with four replications. Its morphophysiological components, components of green grain production and dry grain yield were estimated from genetic use and correlations between the traits. Phenotypic correlations were deployed through path analysis into direct and indirect effects of morphophysiological traits and yield components on dry grain yield. There were significant differences (P < 0.01) between the genotypes for most the traits, indicating the presence of genetic variability in the population and the possibility of practicing selection. The population presents the potential for future genetic breeding studies and is highly promising for the selection of traits dry grain yield, the number of grains per pod, and hundred grains mass. A number of grains per green pod is the main determinant trait of dry grain yield that is also influenced by the cultivar cycle and that the selection for the dry grain yield can be made indirectly by selecting the green pod mass and green pod length.

Genetic parameters of Visual Image Analysis primal cut carcass traits of commercial prime beef slaughter animals.

Science.gov (United States)

Moore, K L; Mrode, R; Coffey, M P

2017-10-01

Visual Image analysis (VIA) of carcass traits provides the opportunity to estimate carcass primal cut yields on large numbers of slaughter animals. This allows carcases to be better differentiated and farmers to be paid based on the primal cut yields. It also creates more accurate genetic selection due to high volumes of data which enables breeders to breed cattle that better meet the abattoir specifications and market requirements. In order to implement genetic evaluations for VIA primal cut yields, genetic parameters must first be estimated and that was the aim of this study. Slaughter records from the UK prime slaughter population for VIA carcass traits was available from two processing plants. After edits, there were 17 765 VIA carcass records for six primal cut traits, carcass weight as well as the EUROP conformation and fat class grades. Heritability estimates after traits were adjusted for age ranged from 0.32 (0.03) for EUROP fat to 0.46 (0.03) for VIA Topside primal cut yield. Adjusting the VIA primal cut yields for carcass weight reduced the heritability estimates, with estimates of primal cut yields ranging from 0.23 (0.03) for Fillet to 0.29 (0.03) for Knuckle. Genetic correlations between VIA primal cut yields adjusted for carcass weight were very strong, ranging from 0.40 (0.06) between Fillet and Striploin to 0.92 (0.02) between Topside and Silverside. EUROP conformation was also positively correlated with the VIA primal cuts with genetic correlation estimates ranging from 0.59 to 0.84, whereas EUROP fat was estimated to have moderate negative correlations with primal cut yields, estimates ranged from -0.11 to -0.46. Based on these genetic parameter estimates, genetic evaluation of VIA primal cut yields can be undertaken to allow the UK beef industry to select carcases that better meet abattoir specification and market requirements.

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

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Chuii Khim Chong

2012-06-01

Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms

Genetic parameters and environmental effects on temperament score and reproductive traits of Nellore cattle.

Science.gov (United States)

Barrozo, D; Buzanskas, M E; Oliveira, J A; Munari, D P; Neves, H H R; Queiroz, S A

2012-01-01

Animal temperament is a trait of economic relevance and its use as a selection criterion requires the identification of environmental factors that influence this trait, as well as the estimation of its genetic variability and interrelationship with other traits. The objectives of this study were to evaluate the effect of the covariates dam age at calving (ADC), long yearling age (YA) and long yearling weight (YW) on temperament score (T) and to estimate genetic parameters for T, scrotal circumference (SC) at long YA and age at first calving (AFC) in Nellore cattle participating in a selection program. The traits were analyzed by the restricted maximum likelihood method under a multiple-trait animal model. For all traits, contemporary group was included as a fixed effect and additive genetic and residual as random effects. In addition to these effects, YA, YW and ADC were considered for analyzing T. In the case of SC and AFC, the effect of long YW was included as a covariate. Genetic parameters were estimated for and between traits. The three covariates significantly influenced T. The heritability estimates for T, SC and AFC were 0.18 ± 0.02, 0.53 ± 0.04 and 0.23 ± 0.08, respectively. The genetic correlations between T and SC, and T and AFC were -0.07 ± 0.17 and -0.06 ± 0.19, respectively. The genetic correlation estimated between SC and AFC was -0.57 ± 0.16. In conclusion, a response to selection for T, SC and AFC is expected and selection for T does not imply correlated responses with the other traits.

Optomechanical parameter estimation

International Nuclear Information System (INIS)

Ang, Shan Zheng; Tsang, Mankei; Harris, Glen I; Bowen, Warwick P

2013-01-01

We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cramér–Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation–maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cramér–Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry and fundamental tests of quantum mechanics. (paper)

A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers

Science.gov (United States)

Heidari, M.; Ranjithan, S.R.

1998-01-01

In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is

Genetic parameters and correlations among linear type traits in the ...

African Journals Online (AJOL)

The main objective of this study was to estimate the genetic parameters and relationships of 10 linear type traits in the first lactation of Holstein dairy cows. 3274 records for type traits was used (Ag, angularity; Sta, stature; Bdp, body depth; Rw, rump width; Rs, rear leg side view; Fa, foot angle; Fu, fore udder attachment; Ruh, ...

Estimates of genetics and phenotypics parameters for the yield and quality of soybean seeds.

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Zambiazzi, E V; Bruzi, A T; Guilherme, S R; Pereira, D R; Lima, J G; Zuffo, A M; Ribeiro, F O; Mendes, A E S; Godinho, S H M; Carvalho, M L M

2017-09-27

Estimating genotype x environment (GxE) parameters for quality and yield in soybean seed grown in different environments in Minas Gerais State was the goal of this study, as well as to evaluate interaction effects of GxE for soybean seeds yield and quality. Seeds were produced in three locations in Minas Gerais State (Lavras, Inconfidentes, and Patos de Minas) in 2013/14 and 2014/15 seasons. Field experiments were conducted in randomized blocks in a factorial 17 x 6 (GxE), and three replications. Seed yield and quality were evaluated for germination in substrates paper and sand, seedling emergence, speed emergency index, mechanical damage by sodium hypochlorite, electrical conductivity, speed aging, vigor and viability of seeds by tetrazolium test in laboratory using completely randomized design. Quadratic component genotypic, GXE variance component, genotype determination coefficient, genetic variation coefficient and environmental variation coefficient were estimated using the Genes software. Percentage analysis of genotypes contribution, environments and genotype x environment interaction were conducted by sites combination two by two and three sites combination, using the R software. Considering genotypes selection of broad adaptation, TMG 1179 RR, CD 2737 RR, and CD 237 RR associated better yield performance at high physical and physiological potential of seed. Environmental effect was more expressive for most of the characters related to soybean seed quality. GxE interaction effects were expressive though genotypes did not present coincidental behavior in different environments.

Genetic Parameters and the Impact of Off-Types for Theobroma cacao L. in a Breeding Program in Brazil

Science.gov (United States)

DuVal, Ashley; Gezan, Salvador A.; Mustiga, Guiliana; Stack, Conrad; Marelli, Jean-Philippe; Chaparro, José; Livingstone, Donald; Royaert, Stefan; Motamayor, Juan C.

2017-01-01

Breeding programs of cacao (Theobroma cacao L.) trees share the many challenges of breeding long-living perennial crops, and genetic progress is further constrained by both the limited understanding of the inheritance of complex traits and the prevalence of technical issues, such as mislabeled individuals (off-types). To better understand the genetic architecture of cacao, in this study, 13 years of phenotypic data collected from four progeny trials in Bahia, Brazil were analyzed jointly in a multisite analysis. Three separate analyses (multisite, single site with and without off-types) were performed to estimate genetic parameters from statistical models fitted on nine important agronomic traits (yield, seed index, pod index, % healthy pods, % pods infected with witches broom, % of pods other loss, vegetative brooms, diameter, and tree height). Genetic parameters were estimated along with variance components and heritabilities from the multisite analysis, and a trial was fingerprinted with low-density SNP markers to determine the impact of off-types on estimations. Heritabilities ranged from 0.37 to 0.64 for yield and its components and from 0.03 to 0.16 for disease resistance traits. A weighted index was used to make selections for clonal evaluation, and breeding values estimated for the parental selection and estimation of genetic gain. The impact of off-types to breeding progress in cacao was assessed for the first time. Even when present at <5% of the total population, off-types altered selections by 48%, and impacted heritability estimations for all nine of the traits analyzed, including a 41% difference in estimated heritability for yield. These results show that in a mixed model analysis, even a low level of pedigree error can significantly alter estimations of genetic parameters and selections in a breeding program. PMID:29250097

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

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Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

Genetic parameters, phenotypic, genotypic and environmental correlations and genetic variability on sunflower in the Brazilian Savannah

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Ellen Grippi Lira

Full Text Available ABSTRACT: Sunflower (Helianthus annuus L. is an annual crop that stands out for its production of high quality oil and for an efficient selection, being necessary to estimate the components of genetic and phenotypic variance. This study aimed to estimate genetic parameters, phenotypic, genotypic and environmental correlations and genetic variability on sunflower in the Brazilian Savannah, evaluating the characters grain yield (YIELD, days to start flowering (DFL based on flowering date in R5, chapter length (CL, weight of a thousand achenes (WTA, plant height (H and oil content (OilC of 16 sunflower genotypes. The experiment was conducted at Embrapa Cerrados, Planaltina, DF, situated at 15º 35’ 30”S latitude, 47º 42’ 30”W longitude and 1.007m above sea level, in soil classified as dystroferric Oxisol. The experimental design used was a complete randomized block with four replicates. The nature for the effects of genotypes and blocks was fixed. Except for the character chapter length, genetic variance was the main component of the phenotypic variance among the genotypes, indicating high genetic variability and experimental efficiency with proper environmental control. In absolute terms, the genetic correlations were superior to phenotypic and environmental. The high values reported for heritability and selective accuracy indicated efficiency of phenotypic selection. Results showed high genetic variability among genotypes, which may contribute to the genetic improvement of sunflower.

Determinatıon of Some Genetic Parameters, Phenotypic, Genetic and Environmental Trends and Environmental Factors Affecting Milk Yield Traits of Brown Swiss Cattle

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Muhammet Hanifi Selvi

2016-01-01

Full Text Available In this study, genetic parameters, macro environmental factors and genetic, phenotypic and environmental trends for actual and 305 day milk yield of Brown Swiss cattle reared in Research Farm of Agricultural College at Atatürk University were estimated. Estimated breeding values that were used for calculation of the genetic trend and genetic parameters were estimated by using MTDFREML computer package program. Environmental factors affecting on actual and 305day milk yields were analysed by using Harvey statistic package program. While effects of the years and parities on the actual and 305-day milk yields were highly significant, the influence of the calving season was found to be insignificant. Environmental and phenotypic trends for actual and 305-day milk yields were determined as -33.2 kg and -29.0 kg; and -27.8±19.1 kg/year and -25.9±8.7 kg/year respectively. Genetic trends for actual and 305-day milk yields were calculated as 5.4±3.8 kg and 3.1±3.4 kg. Heritability’s for actual and 305-day milk yields were 0.21±0.12 and 0.16±0.14 respectively. Repeatability values for actual and 305-day milk yield were found as 0.29 and 0.33 respectively.

Applied parameter estimation for chemical engineers

CERN Document Server

Englezos, Peter

2000-01-01

Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynam

Genetic parameters of body weight and prolificacy in pigeons

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Beaumont Catherine

2000-07-01

Full Text Available Abstract Genetic parameters of body weight at weaning and of prolificacy were estimated in three commercial lines of pigeons selected by BLUP (Best Linear Unbiased Prediction on both traits. The model of analysis took into account the direct genetic effects for both traits and the effect of parental permanent environment for body weight. Depending on the line considered, body weight varied from 556.7 g to 647.6 g and prolificacy ranged from 12.5 to 16.8 pigeons weaned per couple of parents per year. Heritability of body weight was high, varying between 0.46 and 0.60, and permanent environment was responsible for 6% to 9% of the total variability. On the contrary, prolificacy was poorly heritable (0.04 to 0.12. They were highly and negatively correlated (-0.77 to -0.82. Body weight showed significant genetic trends in lines B and C. No significant genetic difference could be observed between males and females for both traits.

Improved Estimates of Thermodynamic Parameters

Science.gov (United States)

Lawson, D. D.

1982-01-01

Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.

Genetic parameters for fecal egg count and body weight in Katahdin lambs

Science.gov (United States)

The objective of this study was to estimate genetic parameters for fecal egg count at weaning (WFEC) and post weaning (PWFEC), and weights at birth (BW), weaning (WW) and post weaning (PWW) in Katahdin lambs by investigating direct additive, maternal additive, maternal permanent environmental and ma...

Performance of penalized maximum likelihood in estimation of genetic covariances matrices

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Meyer Karin

2011-11-01

Full Text Available Abstract Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered. Methods An extensive simulation study was carried out to investigate the reduction in average 'loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should

Genetic parameters of eventing horse competition in France

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Chanu Isabelle

2001-03-01

Full Text Available Abstract Genetic parameters of eventing horse competitions were estimated. About 13 000 horses, 30 000 annual results during 17 years and 110 000 starts in eventing competitions during 8 years were recorded. The measures of performance were logarithmic transformations of annual earnings, annual earnings per start, and annual earnings per place, and underlying variables responsible for ranks in each competition. Heritabilities were low (0.11/0.17 for annual results, 0.07 for ranks. Genetic correlations between criteria were high (greater than 0.90 except between ranks and earnings per place (0.58 or per start (0.67. Genetic correlations between ages (from 5 to 10 years old were also high (more than 0.85 and allow selection on early performances. The genetic correlation between the results in different levels of competition (high/international and low/amateur was near 1. Genetic correlations of eventing with other disciplines, which included partial aptitude needed for eventing, were very low for steeplechase races (0.18 and moderate with sport: jumping (0.45, dressage (0.58. The results suggest that selection on jumping performance will lead to some positive correlated response for eventing performance, but much more response could be obtained if a specific breeding objective and selection criteria were developed for eventing.

Genetic Parameters of Common Wheat in Nepal

OpenAIRE

Bal Krishna Joshi; Dhruba Bahadur Thapa; Madan Raj Bhatta

2015-01-01

Knowledge on variation within traits and their genetics are prerequisites in crop improvement program. Thus, in present paper we aimed to estimate genetic and environmental indices of common wheat genotypes. For the purpose, eight quantitative traits were measured from 30 wheat genotypes, which were in randomized complete block design with 3 replicates. Components of variance and covariance were estimated along with heritability, genetic gain, realized heritability, coheritability and correla...

Genetic parameters for production and fertility in spring-calving Irish dairy cattle

NARCIS (Netherlands)

Evans, R.; Buckley, F.; Dillon, P.; Veerkamp, R.F.

2001-01-01

The objective of this study was to estimate genetic parameters for milk production and selected fertility traits in Irish dairy cattle. Data were derived from 74 seasonal spring-calving dairy herds with a potential cow population of 6,783 in the 1999 calving season. The average 305-day yields (kg)

Genetic parameters and trends of morphometric traits of GIFT tilapia under selection for weight gain

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Rafael Vilhena Reis Neto

2014-08-01

Full Text Available The main factor considered in breeding programs for fish is growth, which can be assessed in terms of a gain in either weight or body measurements. This study was undertaken to evaluate the morphometric traits of GIFT strain tilapia (Oreochromis sp. selected for weight gain. The data set used contained information on 6,650 animals. The genetic values of 8,590 animals in a relationship matrix of five generations were predicted. The following morphometric measurements were evaluated: standard length; body depth and body width. Body area and volume were also calculated. Bi-character analyses involving morphometric traits were used to estimate (covariance components. Heritability, larval and fingerling common environmental effects were estimated for each trait, together with the genetic and phenotypic correlations between traits. Bayesian procedures were utilised by Gibbs chains, and the convergence of the chains was tested using the Heidelberger and Welch method. Genetic trends were estimated by segmented regression of the fish breeding values of the generations considered in this study. Estimates of heritability (0.28 a 0.31 had moderate to high magnitudes for all traits. Genetic correlations between traits were all above 0.8, and the genetic gains were satisfactory from the third generation onwards. From the estimates of the genetic parameters and genetic gain the morphometric traits evaluated have good potential for selection.

Perils of parsimony: properties of reduced-rank estimates of genetic covariance matrices.

Science.gov (United States)

Meyer, Karin; Kirkpatrick, Mark

2008-10-01

Eigenvalues and eigenvectors of covariance matrices are important statistics for multivariate problems in many applications, including quantitative genetics. Estimates of these quantities are subject to different types of bias. This article reviews and extends the existing theory on these biases, considering a balanced one-way classification and restricted maximum-likelihood estimation. Biases are due to the spread of sample roots and arise from ignoring selected principal components when imposing constraints on the parameter space, to ensure positive semidefinite estimates or to estimate covariance matrices of chosen, reduced rank. In addition, it is shown that reduced-rank estimators that consider only the leading eigenvalues and -vectors of the "between-group" covariance matrix may be biased due to selecting the wrong subset of principal components. In a genetic context, with groups representing families, this bias is inverse proportional to the degree of genetic relationship among family members, but is independent of sample size. Theoretical results are supplemented by a simulation study, demonstrating close agreement between predicted and observed bias for large samples. It is emphasized that the rank of the genetic covariance matrix should be chosen sufficiently large to accommodate all important genetic principal components, even though, paradoxically, this may require including a number of components with negligible eigenvalues. A strategy for rank selection in practical analyses is outlined.

Genetic and phenotypic parameters of productivity traits on the first three lactations in Gyr cattle herds

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Albuquerque Maria do Socorro Maués

1999-01-01

Full Text Available Records of Gyr cows selected for milk production were obtained from the National Gyr Dairy Cattle Breeding Program (Embrapa/CNPGL and analyzed, in order to estimate genetic parameters for the first three lactations and to verify the effects of some environmental factors on milk production from 1979 to 1994. Genetic parameters were estimated by REML with an animal model and a group of fixed effects that included classes of herd, year, season and age at calving. Milk production means and standard deviations were 2,183 kg, 707 kg; 2,682 kg, 762 kg and 2,638 kg, 851 kg, for first, second, and third lactations, respectively. Heritability estimates were 0.20, 0.12, and 0.19 for first, second, and third lactations, respectively, and repeatability was 0.44. Genetic correlation estimates were: 0.68 between first and second lactations, 0.84 between first and third lactations and 1.0 between second and third lactations. Results confirm other research for specialized dairy breeds and firmly suggest that even in breeds of Indian origin the best time to make selection decisions is during the first lactation.

An alternative covariance estimator to investigate genetic heterogeneity in populations.

Science.gov (United States)

Heslot, Nicolas; Jannink, Jean-Luc

2015-11-26

For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship. We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy. This alternative

Parameter estimation in plasmonic QED

Science.gov (United States)

Jahromi, H. Rangani

2018-03-01

We address the problem of parameter estimation in the presence of plasmonic modes manipulating emitted light via the localized surface plasmons in a plasmonic waveguide at the nanoscale. The emitter that we discuss is the nitrogen vacancy centre (NVC) in diamond modelled as a qubit. Our goal is to estimate the β factor measuring the fraction of emitted energy captured by waveguide surface plasmons. The best strategy to obtain the most accurate estimation of the parameter, in terms of the initial state of the probes and different control parameters, is investigated. In particular, for two-qubit estimation, it is found although we may achieve the best estimation at initial instants by using the maximally entangled initial states, at long times, the optimal estimation occurs when the initial state of the probes is a product one. We also find that decreasing the interqubit distance or increasing the propagation length of the plasmons improve the precision of the estimation. Moreover, decrease of spontaneous emission rate of the NVCs retards the quantum Fisher information (QFI) reduction and therefore the vanishing of the QFI, measuring the precision of the estimation, is delayed. In addition, if the phase parameter of the initial state of the two NVCs is equal to πrad, the best estimation with the two-qubit system is achieved when initially the NVCs are maximally entangled. Besides, the one-qubit estimation has been also analysed in detail. Especially, we show that, using a two-qubit probe, at any arbitrary time, enhances considerably the precision of estimation in comparison with one-qubit estimation.

Parameter Estimation in Continuous Time Domain

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Gabriela M. ATANASIU

2016-12-01

Full Text Available This paper will aim to presents the applications of a continuous-time parameter estimation method for estimating structural parameters of a real bridge structure. For the purpose of illustrating this method two case studies of a bridge pile located in a highly seismic risk area are considered, for which the structural parameters for the mass, damping and stiffness are estimated. The estimation process is followed by the validation of the analytical results and comparison with them to the measurement data. Further benefits and applications for the continuous-time parameter estimation method in civil engineering are presented in the final part of this paper.

Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

OpenAIRE

Frank M. You; Qijian Song; Gaofeng Jia; Yanzhao Cheng; Scott Duguid; Helen Booker; Sylvie Cloutier

2016-01-01

The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic ...

Genetic parameters for racing records in trotters using linear and generalized linear models.

Science.gov (United States)

Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

2012-09-01

Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS

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muhammad zahid rashid

2011-04-01

Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR,  moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes

Genetic parameters of rumination time and feed efficiency traits in primiparous Holstein cows under research and commercial conditions.

Science.gov (United States)

Byskov, M V; Fogh, A; Løvendahl, P

2017-12-01

Feed efficiency has the potential to be improved both through feeding, management, and breeding. Including feed efficiency in a selection index is limited by the fact that dry matter intake (DMI) recording is only feasible under research facilities, resulting in small data sets and, consequently, uncertain genetic parameter estimates. As a result, the need to record DMI indicator traits on a larger scale exists. Rumination time (RT), which is already recorded in commercial dairy herds by a sensor-based system, has been suggested as a potential DMI indicator. However, RT can only be a DMI indicator if it is heritable, correlates with DMI, and if the genetic parameters of RT in commercial herd settings are similar to those in research facilities. Therefore, the objective of our study was to estimate genetic parameters for RT and the related traits of DMI in primiparous Holstein cows, and to compare genetic parameters of rumination data between a research herd and 72 commercial herds. The estimated heritability values were all moderate for DMI (0.32-0.49), residual feed intake (0.23-0.36), energy-corrected milk (ECM) yield (0.49-0.70), and RT (0.14-0.44) found in the research herd. The estimated heritability values for ECM were lower for the commercial herds (0.08-0.35) than that for the research herd. The estimated heritability values for RT were similar for the 2 herd types (0.28-0.32). For the research herd, we found negative individual level correlations between RT and DMI (-0.24 to -0.09) and between RT and RFI (-0.34 to -0.03), and we found both positive and negative correlations between RT and ECM (-0.08 to 0.09). For the commercial herds, genetic correlations between RT and ECM were both positive and negative (-0.27 to 0.10). In conclusion, RT was not found to be a suitable indicator trait for feed intake and only a weak indicator of feed efficiency. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Four-dimensional parameter estimation of plane waves using swarming intelligence

International Nuclear Information System (INIS)

Zaman Fawad; Munir Fahad; Khan Zafar Ullah; Qureshi Ijaz Mansoor

2014-01-01

This paper proposes an efficient approach for four-dimensional (4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional (2D) direction of arrival, namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose, a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte—Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise. (interdisciplinary physics and related areas of science and technology)

Risk factor meta-analysis and Bayesian estimation of genetic parameters and breeding values for hypersensibility to cutaneous habronematidosis in donkeys.

Science.gov (United States)

Navas González, Francisco Javier; Jordana Vidal, Jordi; Camacho Vallejo, María Esperanza; León Jurado, Jose Manuel; de la Haba Giraldo, Manuel Rafael; Barba Capote, Cecilio; Delgado Bermejo, Juan Vicente

2018-03-15

Cutaneous habronematidosis (CH) is a highly prevalent seasonally recurrent skin disease that affects donkeys as a result from the action of spirurid stomach worm larvae. Carrier flies mistakenly deposit these larvae on previous skin lesions or on the moisture of natural orifices, causing distress and inflicting relapsing wounds to the animals. First, we carried out a meta-analysis of the predisposing factors that could condition the development of CH in Andalusian donkeys. Second, basing on the empirical existence of an inter and intrafamilial variation previously addressed by owners, we isolated the genetic background behind the hypersensibility to this parasitological disease. To this aim, we designed a Bayesian linear model (BLM) to estimate the breeding values and genetic parameters for the hypersensibility to CH as a way to infer the potential selection suitability of this trait, seeking the improvement of donkey conservation programs. We studied the historical record of the cases of CH of 765 donkeys from 1984 to 2017. Fixed effects included birth year, birth season, sex, farm/owner, and husbandry system. Age was included as a linear and quadratic covariate. Although the effects of birth season and birth year were statistically non-significant (P > 0.05), their respective interactions with sex and farm/owner were statistically significant (P < 0.01), what translated into an increase of 40.5% in the specificity and of 0.6% of the sensibility of the model designed, when such interactions were included. Our BLM reported highly accurate genetic parameters as suggested by the low error of around 0.005, and the 95% credible interval for the heritability of ±0.0012. The CH hypersensibility heritability was 0.0346. The value of 0.1232 for additive genetic variance addresses a relatively low genetic variation in the Andalusian donkey breed. Our results suggest that farms managed under extensive husbandry conditions are the most protective ones against

Bayesian Parameter Estimation for Heavy-Duty Vehicles

Energy Technology Data Exchange (ETDEWEB)

Miller, Eric; Konan, Arnaud; Duran, Adam

2017-03-28

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.

Genetic parameters and productivity of quinoa in western Paraná

Directory of Open Access Journals (Sweden)

Edmar Soares de Vasconcelos

2016-04-01

Full Text Available Quinoa has been gaining attention because of its nutritional quality, low cholesterol and lack of gluten; in Brazil, the cultivation efforts in the different regions are mainly related to breeding. This study aimed to determine the genetic parameters and evaluate the productivity of the different genotypes of quinoa for detecting genotypes amenable to selection. The experiment was conducted in crop years 2010/11 and 2011/12 in environment 1 and environment 2, respectively. In environment 1, the evaluation of 61 genotypes was performed, and in environment 2, 31 genotypes were evaluated. The experimental design was a randomized block with two replications; each plot measured 2.0 x 5.0 m (10 m² and consisted of four rows spaced at 0.45 m. Data collected on the productivity, plant height at maturation and growth cycle were analyzed using an analysis of variance, average tests and estimates of the genetic parameters. The genotypes N24 and N08 were the only genotypes more productive than the other 46 genotypes, with values of productivity of 1446.23 and 1428.93 kg ha-1 and with a growth cycle of 117 and 111 days, respectively. The heritability values determined demonstrate the possibility of genetic gain using joint selection that involves two environments.

Genetic parameters and simultaneous selection for root yield, adaptability and stability of cassava genotypes

Directory of Open Access Journals (Sweden)

João Tomé de Farias Neto

2013-12-01

Full Text Available The objective of this work was to estimate genetic parameters and to evaluate simultaneous selection for root yield and for adaptability and stability of cassava genotypes. The effects of genotypes were assumed as fixed and random, and the mixed model methodology (REML/Blup was used to estimate genetic parameters and the harmonic mean of the relative performance of genotypic values (HMRPGV, for simultaneous selection purposes. Ten genotypes were analyzed in a complete randomized block design, with four replicates. The experiment was carried out in the municipalities of Altamira, Santarém, and Santa Luzia do Pará in the state of Pará, Brazil, in the growing seasons of 2009/2010, 2010/2011, and 2011/2012. Roots were harvested 12 months after planting, in all tested locations. Root yield had low coefficients of genotypic variation (4.25% and broad-sense heritability of individual plots (0.0424, which resulted in low genetic gain. Due to the low genotypic correlation (0.15, genotype classification as to root yield varied according to the environment. Genotypes CPATU 060, CPATU 229, and CPATU 404 stood out as to their yield, adaptability, and stability.

Effects of correcting missing daily feed intake values on the genetic parameters and estimated breeding values for feeding traits in pigs.

Science.gov (United States)

Ito, Tetsuya; Fukawa, Kazuo; Kamikawa, Mai; Nikaidou, Satoshi; Taniguchi, Masaaki; Arakawa, Aisaku; Tanaka, Genki; Mikawa, Satoshi; Furukawa, Tsutomu; Hirose, Kensuke

2018-01-01

Daily feed intake (DFI) is an important consideration for improving feed efficiency, but measurements using electronic feeder systems contain many missing and incorrect values. Therefore, we evaluated three methods for correcting missing DFI data (quadratic, orthogonal polynomial, and locally weighted (Loess) regression equations) and assessed the effects of these missing values on the genetic parameters and the estimated breeding values (EBV) for feeding traits. DFI records were obtained from 1622 Duroc pigs, comprising 902 individuals without missing DFI and 720 individuals with missing DFI. The Loess equation was the most suitable method for correcting the missing DFI values in 5-50% randomly deleted datasets among the three equations. Both variance components and heritability for the average DFI (ADFI) did not change because of the missing DFI proportion and Loess correction. In terms of rank correlation and information criteria, Loess correction improved the accuracy of EBV for ADFI compared to randomly deleted cases. These findings indicate that the Loess equation is useful for correcting missing DFI values for individual pigs and that the correction of missing DFI values could be effective for the estimation of breeding values and genetic improvement using EBV for feeding traits. © 2017 The Authors. Animal Science Journal published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Animal Science.

Erratum REML estimates of genetic parameters of sexual ...

Indian Academy of Sciences (India)

Unknown

dimorphism for wing and thorax length in. Drosophila melanogaster. Sandrine Mignon-Grasteau, Jean David, Patricia Gibert, Hélène Legout, Georges Pétavy, Brigitte Moreteau and Catherine Beaumont. J. Genet. 83, 163–170. The printed article (August 2004 issue) has a wrong figure 5. The correct figure 5 is printed below.

Genetic parameters for ewe reproductive performance and peri-parturient fecal egg counts and their genetic relationships with lamb body weights and fecal egg counts in Katahdin sheep

Science.gov (United States)

This study estimated genetic parameters for ewe reproductive traits [number of lambs born (NLB) and weaned (NLW) per ewe lambing] and peri-parturient (PPR) fecal egg counts (FEC) at lambing (PPR0) and 30 d postpartum (PPR30), and their genetic relationships with lamb BW and FEC in Katahdin sheep. Th...

Genetic parameters for reproductive traits in female Nile tilapia (Oreochromis niloticus): II. Fecundity and fertility

NARCIS (Netherlands)

Trong, T.Q.; Arendonk, van J.A.M.; Komen, J.

2013-01-01

Harvest weight is the main trait in Nile tilapia (Oreochromis niloticus) breeding programmes. The effects of selection for harvest weight on female reproductive traits are unknown. In this paper we estimate genetic parameters for reproductive traits and their correlation with harvest weight using

Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations.

Science.gov (United States)

Wientjes, Yvonne C J; Bijma, Piter; Vandenplas, Jérémie; Calus, Mario P L

2017-10-01

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations. Copyright © 2017 by the Genetics Society of America.

Genetic parameters and genetic trends in the Chinese × European Tiameslan composite pig line. II. Genetic trends

Directory of Open Access Journals (Sweden)

Legault Christian

2000-01-01

Full Text Available Abstract The Tiameslan line was created between 1983 and 1985 by mating Meishan × Jiaxing crossbred Chinese boars with sows from the Laconie composite male line. The Tiameslan line has been selected since then on an index combining average backfat thickness (ABT and days from 20 to 100 kg (DT. Direct and correlated responses to 11 years of selection were estimated using BLUP methodology applied to a multiple trait animal model. A total of 11 traits were considered, i.e.: ABT, DT, body weight at 4 (W4w, 8 (W8w and 22 (W22w weeks of age, teat number (TEAT, number of good teats (GTEAT, total number of piglets born (TNB, born alive (NBA and weaned (NW per litter, and birth to weaning survival rate (SURV. Performance data from a total of 4 881 males and 4 799 females from 1 341 litters were analysed. The models included both direct and maternal effects for ABT, W4w and W8w. Male and female performances were considered as different traits for W22w, DT and ABT. Genetic parameters estimated in another paper (Zhang et al., Genet. Sel. Evol. 32 (2000 41-56 were used to perform the analyses. Favourable phenotypic (ΔP and direct genetic trends (ΔGd were obtained for post-weaning growth traits and ABT. Trends for maternal effects were limited. Phenotypic and genetic trends were larger in females than in males for ABT (e.g. ΔGd = -0.48 vs. -0.38 mm/year, were larger in males for W22w (ΔGd = 0.90 vs. 0.58 kg/year and were similar in both sexes for DT (ΔGd = -0.54 vs. -0.55 day/year. Phenotypic and genetic trends were slightly favourable for W4w, W8w, TEAT and GTEAT and close to zero for reproductive traits.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

Science.gov (United States)

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

Science.gov (United States)

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

Genetic parameters for ewe reproductive performance and peri-parturient fecal egg counts and their genetic relationships with lamb body weights and fecal egg counts in Katahdin sheep

Science.gov (United States)

Genetic parameters for ewe reproductive traits [number of lambs born (NLB) and number of lambs weaned (NLW)] and ewe peri-parturient rise (PPR) fecal egg counts (FEC) at lambing (PPR0) and at 30-d post lambing (PPR30), and their genetic relationships with lamb BW and FEC in Katahdin sheep were estim...

Genetic algorithm approach to thin film optical parameters determination

International Nuclear Information System (INIS)

Jurecka, S.; Jureckova, M.; Muellerova, J.

2003-01-01

Optical parameters of thin film are important for several optical and optoelectronic applications. In this work the genetic algorithm proposed to solve optical parameters of thin film values. The experimental reflectance is modelled by the Forouhi - Bloomer dispersion relations. The refractive index, the extinction coefficient and the film thickness are the unknown parameters in this model. Genetic algorithm use probabilistic examination of promissing areas of the parameter space. It creates a population of solutions based on the reflectance model and then operates on the population to evolve the best solution by using selection, crossover and mutation operators on the population individuals. The implementation of genetic algorithm method and the experimental results are described too (Authors)

Estimating the actual subject-specific genetic correlations in behavior genetics.

Science.gov (United States)

Molenaar, Peter C M

2012-10-01

Generalization of the standard behavior longitudinal genetic factor model for the analysis of interindividual phenotypic variation to a genetic state space model for the analysis of intraindividual variation enables the possibility to estimate subject-specific heritabilities.

Genetic Algorithm Optimizes Q-LAW Control Parameters

Science.gov (United States)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Methods to estimate the genetic risk

International Nuclear Information System (INIS)

Ehling, U.H.

1989-01-01

The estimation of the radiation-induced genetic risk to human populations is based on the extrapolation of results from animal experiments. Radiation-induced mutations are stochastic events. The probability of the event depends on the dose; the degree of the damage dose not. There are two main approaches in making genetic risk estimates. One of these, termed the direct method, expresses risk in terms of expected frequencies of genetic changes induced per unit dose. The other, referred to as the doubling dose method or the indirect method, expresses risk in relation to the observed incidence of genetic disorders now present in man. The advantage of the indirect method is that not only can Mendelian mutations be quantified, but also other types of genetic disorders. The disadvantages of the method are the uncertainties in determining the current incidence of genetic disorders in human and, in addition, the estimasion of the genetic component of congenital anomalies, anomalies expressed later and constitutional and degenerative diseases. Using the direct method we estimated that 20-50 dominant radiation-induced mutations would be expected in 19 000 offspring born to parents exposed in Hiroshima and Nagasaki, but only a small proportion of these mutants would have been detected with the techniques used for the population study. These methods were used to predict the genetic damage from the fallout of the reactor accident at Chernobyl in the vicinity of Southern Germany. The lack of knowledge for the interaction of chemicals with ionizing radiation and the discrepancy between the high safety standards for radiation protection and the low level of knowledge for the toxicological evaluation of chemical mutagens will be emphasized. (author)

On parameter estimation in deformable models

DEFF Research Database (Denmark)

Fisker, Rune; Carstensen, Jens Michael

1998-01-01

Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...

Estimation of genetic and genotypic parameters for growth and ...

African Journals Online (AJOL)

Bekezela

2013-12-19

Turner et al., 1990), video ... essential for genetic evaluations, and have been reported in literature ... Soybean oil cake meal (%) ... On arrival, the pigs were treated for internal and external parasites and quarantined under.

Application of spreadsheet to estimate infiltration parameters

Directory of Open Access Journals (Sweden)

Mohammad Zakwan

2016-09-01

Full Text Available Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.

Parameter Estimation of Partial Differential Equation Models.

Science.gov (United States)

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Genetic Parameters for Body condition score, Body weigth, Milk yield and Fertility estimated using random regression models

NARCIS (Netherlands)

Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

2003-01-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields

Parameter estimation and characterization of a single-chamber microbial fuel cell for dairy wastewater treatment.

Science.gov (United States)

Sedaqatvand, Ramin; Nasr Esfahany, Mohsen; Behzad, Tayebeh; Mohseni, Madjid; Mardanpour, Mohammad Mahdi

2013-10-01

In this study, for the first time, the conduction-based model is extended, and then combined with Genetic Algorithm to estimate the design parameters of a MFC treating dairy wastewater. The optimized parameters are, then, validated. The estimated half-saturation potential of -0.13 V (vs. SHE) is in good agreement while the biofilm conductivity of 8.76×10(-4) mS cm(-1) is three orders of magnitude lower than that previously-reported for pure-culture biofilm. Simulations show that the ohmic and concentration overpotentials contribute almost equally in dropping cell voltage in which the concentration film and biofilm conductivity comprise the main resistances, respectively. Thus, polarization analysis and determining the controlling steps will be possible through that developed extension. This study introduces a reliable method to estimate the design parameters of a particular MFC and to characterize it. Copyright © 2013 Elsevier Ltd. All rights reserved.

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

Science.gov (United States)

Baker, Robert L; Leong, Wen Fung; An, Nan; Brock, Marcus T; Rubin, Matthew J; Welch, Stephen; Weinig, Cynthia

2018-02-01

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A max ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A max , because these two indices were genetically correlated with A max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular

Precision Parameter Estimation and Machine Learning

Science.gov (United States)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION

Directory of Open Access Journals (Sweden)

N. Belavendram

2010-12-01

Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.

Genetic parameters for chronic progressive lymphedema in Belgian Draught Horses.

Science.gov (United States)

De Keyser, K; Janssens, S; Peeters, L M; Foqué, N; Gasthuys, F; Oosterlinck, M; Buys, N

2014-12-01

Genetic parameters for chronic progressive lymphedema (CPL)-associated traits in Belgian Draught Horses were estimated, using a multitrait animal model. Clinical scores of CPL in the four limbs/horse (CPLclin ), skinfold thickness and hair samples (hair diameter) were studied. Due to CPLclin uncertainty in younger horses (progressive CPL character), a restricted data set (D_3+) was formed, excluding records from horses under 3 years from the complete data set (D_full). Age, gender, coat colour and limb hair pigmentation were included as fixed, permanent environment and date of recording as random effects. Higher CPLclin certainty (D_3+) increased heritability coefficients of, and genetic correlations between traits, with CPLclin heritabilities (SE) for the respective data sets: 0.11 (0.06) and 0.26 (0.05). A large proportion of the CPLclin variance was attributed to the permanent environmental effect in D_full, but less in D_3+. Date of recording explained a proportion of variance from 0.09 ± 0.03 to 0.61 ± 0.08. Additive genetic correlations between CPLclin and both skinfold thickness and hair diameter showed the latter two traits cannot be used as a direct diagnostic aid for CPL. Due to the relatively low heritability of CPLclin , selection should focus on estimated breeding values (from repeated clinical examinations) to reduce CPL occurrence in the Belgian Draught Horse. © 2014 Blackwell Verlag GmbH.

Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation

Directory of Open Access Journals (Sweden)

Li Sen

2012-03-01

Full Text Available Abstract Background The Approximate Bayesian Computation (ABC approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years. Results We evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC. Conclusions We conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from

Reionization history and CMB parameter estimation

International Nuclear Information System (INIS)

Dizgah, Azadeh Moradinezhad; Kinney, William H.; Gnedin, Nickolay Y.

2013-01-01

We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case

Reionization history and CMB parameter estimation

Energy Technology Data Exchange (ETDEWEB)

Dizgah, Azadeh Moradinezhad; Gnedin, Nickolay Y.; Kinney, William H.

2013-05-01

We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.

Estimates of genetic and environmental factors on growth and mortality in Karakul lambs

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Sayed Akbar Shiri

2016-04-01

Full Text Available Introduction Lamb production is the largest part of income in sheep industry. Therefore, the mortality rate of lambs is a key factor in profit of the sheep breeding. Mortality rate of lambs (or Lamb mortality rate in different breeds of sheep under different climatic conditions is varying from 15% to 50% and an average of 9% to 20% has been reported. Survival rate is a combination trait that is influenced by various factors such as management, weather condition, and behavior of dam and lamb, as well as genetic effects. Quantification of non-genetic effects on mortality rate can be useful in controlling lamb survival rate and increasing profitability of sheep breeding. Therefore, identification of genetic and environmental factors affecting the productive capacity of indigenous breeds in different area is the main priority that should be considered in breeding programmes. Therefore, the objective of this study was to estimate genetic and environmental factors of growth traits and mortality in Karakul lambs. To estimate the genetic and environmental parameters of Karakul lambs before weaning growth and mortality records of 4929 lambs from 207 rams and 1856 ewes at Sarakhs Karakul sheep breeding station, from 1994 to 2009 were used. Materials and Methods The data were used in this study included a total of 4929 record of lamb birth weight, 1 and 3 months of age, average daily gain from birth to weaning (growth traits before weaning and mortality rate of lambs from birth to 1, 2, 4, 8 and 14 weeks (mortality rate of lambs before weaning. Data were collected during the years 1994 to 2010 in karakul breeding station in Sarakhs. The data were edited and pedigree file and data file were prepared. Uni-variate animal model was used to estimate the genetic parameters as following: where is the vector of record, b is the vector of fixed effects (year, sex, type of birth, age of dam, a is the vector of direct additive genetic effects, m the vector of

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei

2013-09-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

Limitations to estimating bacterial cross-speciestransmission using genetic and genomic markers: inferencesfrom simulation modeling

Science.gov (United States)

Julio Andre, Benavides; Cross, Paul C.; Luikart, Gordon; Scott, Creel

2014-01-01

Cross-species transmission (CST) of bacterial pathogens has major implications for human health, livestock, and wildlife management because it determines whether control actions in one species may have subsequent effects on other potential host species. The study of bacterial transmission has benefitted from methods measuring two types of genetic variation: variable number of tandem repeats (VNTRs) and single nucleotide polymorphisms (SNPs). However, it is unclear whether these data can distinguish between different epidemiological scenarios. We used a simulation model with two host species and known transmission rates (within and between species) to evaluate the utility of these markers for inferring CST. We found that CST estimates are biased for a wide range of parameters when based on VNTRs and a most parsimonious reconstructed phylogeny. However, estimations of CST rates lower than 5% can be achieved with relatively low bias using as low as 250 SNPs. CST estimates are sensitive to several parameters, including the number of mutations accumulated since introduction, stochasticity, the genetic difference of strains introduced, and the sampling effort. Our results suggest that, even with whole-genome sequences, unbiased estimates of CST will be difficult when sampling is limited, mutation rates are low, or for pathogens that were recently introduced.

Parameter Estimation for Thurstone Choice Models

Energy Technology Data Exchange (ETDEWEB)

Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-04-24

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

A new Bayesian recursive technique for parameter estimation

Science.gov (United States)

Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis

2006-08-01

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.

Genetic parameters of calving ease using sire-maternal grandsire model in Korean Holsteins

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Mahboob Alam

2017-09-01

Full Text Available Objective Calving ease (CE is a complex reproductive trait of economic importance in dairy cattle. This study was aimed to investigate the genetic merits of CE for Holsteins in Korea. Methods A total of 297,614 field records of CE, from 2000 to 2015, from first parity Holstein heifers were recorded initially. After necessary data pruning such as age at first calving (18 to 42 mo, gestation length, and presence of sire information, final datasets for CE consisted of 147,526 and 132,080 records for service sire calving ease (SCE and daughter calving ease (DCE evaluations, respectively. The CE categories were ordered and scores ranged from CE1 to CE5 (CE1, easy; CE2, slight assistance; CE3, moderate assistance; CE4, difficult calving; CE5, extreme difficulty calving. A linear transformation of CE score was obtained on each category using Snell procedure, and a scaling factor was applied to attain the spread between 0 (CE5 and 100% (CE1. A sire-maternal grandsire model analysis was performed using ASREML 3.0 software package. Results The estimated direct heritability (h2 from SCE and DCE evaluations were 0.11±0.01 and 0.08±0.01, respectively. Maternal h2 estimates were 0.05±0.02 and 0.04±0.01 from SCE and DCE approaches, respectively. Estimates of genetic correlations between direct and maternal genetic components were −0.68±0.09 (SCE and −0.71±0.09 (DCE. The average direct genetic effect increased over time, whereas average maternal effect was low and consistent. The estimated direct predicted transmitting ability (PTA was desirable and increasing over time, but the maternal PTA was undesirable and decreasing. Conclusion The evidence on sufficient genetic variances in this study could reflect a possible selection improvement over time regarding ease of calving. It is expected that the estimated genetic parameters could be a valuable resource to formulate sire selection and breeding plans which would be directed towards the reduction of

Estimation of genetic parameters for body measurements and their ...

African Journals Online (AJOL)

Me

2014-06-05

Jun 5, 2014 ... additive genetic and maternal permanent environmental effects were regarded as an important source of ... It is a multipurpose breed, producing milk, wool ... was the protection and improvement of the breed (Safari, 1986). ... The midpoint between the hock and pin bone on the right rear leg is used to.

Ranking as parameter estimation

Czech Academy of Sciences Publication Activity Database

Kárný, Miroslav; Guy, Tatiana Valentine

2009-01-01

Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf

Comparing estimates of genetic variance across different relationship models.

Science.gov (United States)

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

Estimation of indirect genetic effects in group-housed mink (Neovison vison) should account for systematic interactions either due to kin or sex

DEFF Research Database (Denmark)

Alemu, Setegn Worku; Berg, Peer; Janss, Luc

2016-01-01

interactions in group-housed mink. Furthermore, we investigated whether systematic non-genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family...

Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture.

Science.gov (United States)

Lopes, F B; Ferreira, J L; Lobo, R B; Rosa, G J M

2017-07-06

This study was carried out to investigate (co)variance components and genetic parameters for growth traits in beef cattle using a multi-trait model by Bayesian methods. Genetic and residual (co)variances and parameters were estimated for weights at standard ages of 120 (W120), 210 (W210), 365 (W365), and 450 days (W450), and for pre- and post-weaning daily weight gain (preWWG and postWWG) in Nellore cattle. Data were collected over 16 years (1993-2009), and all animals were raised on pasture in eight farms in the North of Brazil that participate in the National Association of Breeders and Researchers. Analyses were run by the Bayesian approach using Gibbs sampler. Additive direct heritabilities for W120, W210, W365, and W450 and for preWWG and postWWG were 0.28 ± 0.013, 0.32 ± 0.002, 0.31 ± 0.002, 0.50 ± 0.026, 0.61 ± 0.047, and 0.79 ± 0.055, respectively. The estimates of maternal heritability were 0.32 ± 0.012, 0.29 ± 0.004, 0.30 ± 0.005, 0.25 ± 0.015, 0.23 ± 0.017, and 0.22 ± 0.016, respectively, for W120, W210, W365, and W450 and for preWWG and postWWG. The estimates of genetic direct additive correlation among all traits were positive and ranged from 0.25 ± 0.03 (preWWG and postWWG) to 0.99 ± 0.00 (W210 and preWWG). The moderate to high estimates of heritability and genetic correlation for weights and daily weight gains at different ages is suggestive of genetic improvement in these traits by selection at an appropriate age. Maternal genetic effects seemed to be significant across the traits. When the focus is on direct and maternal effects, W210 seems to be a good criterium for the selection of Nellore cattle considering the importance of this breed as a major breed of beef cattle not only in Northern Brazil but all regions covered by tropical pastures. As in this study the genetic correlations among all traits were high, the selection based on weaning weight might be a good choice because at this age there are two important effects (maternal

Cosmological parameter estimation using Particle Swarm Optimization

Science.gov (United States)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Cosmological parameter estimation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Prasad, J; Souradeep, T

2014-01-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite

Statistics of Parameter Estimates: A Concrete Example

KAUST Repository

Aguilar, Oscar

2015-01-01

© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise levels, models, or prior knowledge. But what can we say about the validity of such estimates, and the influence of these assumptions? This paper is concerned with methods to address these questions, and for didactic purposes it is written in the context of a concrete nonlinear parameter estimation problem. We will use the results of a physical experiment conducted by Allmaras et al. at Texas A&M University [M. Allmaras et al., SIAM Rev., 55 (2013), pp. 149-167] to illustrate the importance of validation procedures for statistical parameter estimation. We describe statistical methods and data analysis tools to check the choices of likelihood and prior distributions, and provide examples of how to compare Bayesian results with those obtained by non-Bayesian methods based on different types of assumptions. We explain how different statistical methods can be used in complementary ways to improve the understanding of parameter estimates and their uncertainties.

Genetic Parameters of Common Wheat in Nepal

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Bal Krishna Joshi

2015-12-01

Full Text Available Knowledge on variation within traits and their genetics are prerequisites in crop improvement program. Thus, in present paper we aimed to estimate genetic and environmental indices of common wheat genotypes. For the purpose, eight quantitative traits were measured from 30 wheat genotypes, which were in randomized complete block design with 3 replicates. Components of variance and covariance were estimated along with heritability, genetic gain, realized heritability, coheritability and correlated response. Differences between phenotypic and genotypic variances in heading days, maturity days and plant height were not large. Grain yield and plant height showed the highest phenotypic (18.189% and genotypic (12.06% coefficient of variances, respectively. Phenotypic covariance was higher than genotypic and environmental covariance in most of the traits. The highest heritability and realized heritability were of heading days followed by maturity days. Genetic gain for plant height was the highest. Co-heritability of 1000-grain weight with tillers number was the highest. The highest correlated response was expressed by grain yield with tillers number. This study indicates the possibility of improving wheat genotypes through selection utilizing existing variation in these traits.

Genetic parameters for female fertility, locomotion, body condition score, and linear type traits in Czech Holstein cattle

DEFF Research Database (Denmark)

Zink, Vojtech; Stipkova, M; Lassen, Jan

2011-01-01

The aim of this study was to estimate genetic parameters for fertility traits and linear type traits in the Czech Holstein dairy cattle population. Phenotypic data regarding 12 linear type traits, measured in first lactation, and 3 fertility traits, measured in each of first and second lactation,...

Estimating Soil Hydraulic Parameters using Gradient Based Approach

Science.gov (United States)

Rai, P. K.; Tripathi, S.

2017-12-01

The conventional way of estimating parameters of a differential equation is to minimize the error between the observations and their estimates. The estimates are produced from forward solution (numerical or analytical) of differential equation assuming a set of parameters. Parameter estimation using the conventional approach requires high computational cost, setting-up of initial and boundary conditions, and formation of difference equations in case the forward solution is obtained numerically. Gaussian process based approaches like Gaussian Process Ordinary Differential Equation (GPODE) and Adaptive Gradient Matching (AGM) have been developed to estimate the parameters of Ordinary Differential Equations without explicitly solving them. Claims have been made that these approaches can straightforwardly be extended to Partial Differential Equations; however, it has been never demonstrated. This study extends AGM approach to PDEs and applies it for estimating parameters of Richards equation. Unlike the conventional approach, the AGM approach does not require setting-up of initial and boundary conditions explicitly, which is often difficult in real world application of Richards equation. The developed methodology was applied to synthetic soil moisture data. It was seen that the proposed methodology can estimate the soil hydraulic parameters correctly and can be a potential alternative to the conventional method.

Inverse Modeling of Soil Hydraulic Parameters Based on a Hybrid of Vector-Evaluated Genetic Algorithm and Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Yi-Bo Li

2018-01-01

Full Text Available The accurate estimation of soil hydraulic parameters (θs, α, n, and Ks of the van Genuchten–Mualem model has attracted considerable attention. In this study, we proposed a new two-step inversion method, which first estimated the hydraulic parameter θs using objective function by the final water content, and subsequently estimated the soil hydraulic parameters α, n, and Ks, using a vector-evaluated genetic algorithm and particle swarm optimization (VEGA-PSO method based on objective functions by cumulative infiltration and infiltration rate. The parameters were inversely estimated for four types of soils (sand, loam, silt, and clay under an in silico experiment simulating the tension disc infiltration at three initial water content levels. The results indicated that the method is excellent and robust. Because the objective function had multilocal minima in a tiny range near the true values, inverse estimation of the hydraulic parameters was difficult; however, the estimated soil water retention curves and hydraulic conductivity curves were nearly identical to the true curves. In addition, the proposed method was able to estimate the hydraulic parameters accurately despite substantial measurement errors in initial water content, final water content, and cumulative infiltration, proving that the method was feasible and practical for field application.

Estimation of Genetic Parameters for First Lactation Monthly Test-day Milk Yields using Random Regression Test Day Model in Karan Fries Cattle

Directory of Open Access Journals (Sweden)

Ajay Singh

2016-06-01

Full Text Available A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM considering different order of Legendre polynomial for the additive genetic effect (4th order and the permanent environmental effect (5th order. Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11 to 0.99 (TD-4 and TD-5. The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

Three different applications of genetic algorithm (GA) search techniques on oil demand estimation

International Nuclear Information System (INIS)

Canyurt, Olcay Ersel; Oztuerk, Harun Kemal

2006-01-01

This present study develops three scenarios to analyze oil consumption and make future projections based on the Genetic algorithm (GA) notion, and examines the effect of the design parameters on the oil utilization values. The models developed in the non-linear form are applied to the oil demand of Turkey. The GA Oil Demand Estimation Model (GAODEM) is developed to estimate the future oil demand values based on Gross National Product (GNP), population, import, export, oil production, oil import and car, truck and bus sales figures. Among these models, the GA-PGOiTI model, which uses population, GNP, oil import, truck sales and import as design parameters/indicators, was found to provide the best fit solution with the observed data. It may be concluded that the proposed models can be used as alternative solution and estimation techniques for the future oil utilization values of any country

State Estimation-based Transmission line parameter identification

Directory of Open Access Journals (Sweden)

Fredy Andrés Olarte Dussán

2010-01-01

Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.

Kinetic parameter estimation from attenuated SPECT projection measurements

International Nuclear Information System (INIS)

Reutter, B.W.; Gullberg, G.T.

1998-01-01

Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters

Robust Parameter and Signal Estimation in Induction Motors

DEFF Research Database (Denmark)

Børsting, H.

This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...... in nonlinear systems, have been exposed. The main objectives of this project are: - analysis and application of theories and methods for robust estimation of parameters in a model structure, obtained from knowledge of the physics of the induction motor. - analysis and application of theories and methods...... for robust estimation of the rotor speed and driving torque of the induction motor based only on measurements of stator voltages and currents. Only contimuous-time models have been used, which means that physical related signals and parameters are estimated directly and not indirectly by some discrete...

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models.

Science.gov (United States)

Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N

2014-12-01

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. © 2014 Blackwell Verlag GmbH.

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

Science.gov (United States)

Hoshino, Takahiro; Shigemasu, Kazuo

2008-01-01

The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

Bayesian inference of genetic parameters on litter size and gestation length in Hungarian Landrace and Hungarian Large White pigs

Directory of Open Access Journals (Sweden)

Zoltán Csörnyei

2010-01-01

Full Text Available Genetic parameters of number of piglets born alive (NBA and gestation length (GL were analyzed for 39798 Hungarian Landrace (HLA, 141397 records and 70356 Hungarian Large White (HLW, 246961 records sows. Bivariate repeatability animal models were used, applying a Bayesian statistics. Estimated and heritabilitie repeatabilities (within brackets, were low for NBA, 0.07 (0.14 for HLA and 0.08 (0.17 for HLW, but somewhat higher for GL, 0.18 (0.27 for HLA and 0.26 (0.35 for HLW. Estimated genetic correlations between NBA and GL were low, -0.08 for HLA and -0.05 for HLW.

Steam condenser optimization using Real-parameter Genetic Algorithm for Prototype Fast Breeder Reactor

Energy Technology Data Exchange (ETDEWEB)

Jayalal, M.L., E-mail: jayalal@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Kumar, L. Satish, E-mail: satish@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Jehadeesan, R., E-mail: jeha@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Rajeswari, S., E-mail: raj@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Satya Murty, S.A.V., E-mail: satya@igcar.gov.in [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India); Balasubramaniyan, V.; Chetal, S.C. [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu (India)

2011-10-15

Highlights: > We model design optimization of a vital reactor component using Genetic Algorithm. > Real-parameter Genetic Algorithm is used for steam condenser optimization study. > Comparison analysis done with various Genetic Algorithm related mechanisms. > The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.

Parameter estimation and inverse problems

CERN Document Server

Aster, Richard C; Thurber, Clifford H

2005-01-01

Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...

Steam condenser optimization using Real-parameter Genetic Algorithm for Prototype Fast Breeder Reactor

International Nuclear Information System (INIS)

Jayalal, M.L.; Kumar, L. Satish; Jehadeesan, R.; Rajeswari, S.; Satya Murty, S.A.V.; Balasubramaniyan, V.; Chetal, S.C.

2011-01-01

Highlights: → We model design optimization of a vital reactor component using Genetic Algorithm. → Real-parameter Genetic Algorithm is used for steam condenser optimization study. → Comparison analysis done with various Genetic Algorithm related mechanisms. → The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.

Genetic parameters of ovarian and uterine reproductive traits in dairy cows.

Science.gov (United States)

Carthy, T R; Ryan, D P; Fitzgerald, A M; Evans, R D; Berry, D P

2015-06-01

The objective of the study was to estimate genetic parameters of detailed reproductive traits derived from ultrasound examination of the reproductive tract as well as their genetic correlations with traditional reproductive traits. A total of 226,141 calving and insemination records as well as 74,134 ultrasound records from Irish dairy cows were used. Traditional reproductive traits included postpartum interval to first service, conception, and next calving, as well as the interval from first to last service; number of inseminations, pregnancy rate to first service, pregnant within 42 d of the herd breeding season, and submission in the first 21 d of the herd breeding season were also available. Detailed reproductive traits included resumed cyclicity at the time of ultrasound examination, incidence of multiple ovulations, incidence of early postpartum ovulation, heat detection, ovarian cystic structures, embryo loss, and uterine score; the latter was a subjectively assessed on a scale of 1 (little fluid with normal uterine tone) to 4 (large quantity of fluid with a flaccid uterine tone). Variance (and covariance) components were estimated using repeatability animal linear mixed models. Heritability for all reproductive traits were generally low (0.001-0.05), with the exception of traits related to cyclicity postpartum, regardless if defined traditionally (0.07; calving to first service) or from ultrasound examination [resumed cyclicity at the time of examination (0.07) or early postpartum ovulation (0.10)]. The genetic correlations among the detailed reproductive traits were generally favorable. The exception was the genetic correlation (0.29) between resumed cyclicity and uterine score; superior genetic merit for cyclicity postpartum was associated with inferior uterine score. Superior genetic merit for most traditional reproductive traits was associated with superior genetic merit for resumed cyclicity (genetic correlations ranged from -0.59 to -0.36 and from 0

A Comparative Study of Distribution System Parameter Estimation Methods

Energy Technology Data Exchange (ETDEWEB)

Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup

2016-07-17

In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

Science.gov (United States)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

Multi-Parameter Estimation for Orthorhombic Media

KAUST Repository

Masmoudi, Nabil

2015-08-19

Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.

Multi-Parameter Estimation for Orthorhombic Media

KAUST Repository

Masmoudi, Nabil; Alkhalifah, Tariq Ali

2015-01-01

Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.

Genetic parameters for androstenone and skatole as indicators of boar taint and their relationship to production and litter size traits in Danish Landrace

DEFF Research Database (Denmark)

Strathe, Anders Bjerring; Velander, I. H.; Mark, Thomas

2013-01-01

Boar taint is an offensive odor, which affects the smell and taste of cooked pork, resulting mainly from the accumulation of skatole and androstenone in the back fat of intact males. The aim of the study was to estimate genetic parameters for skatole and androstenone and their genetic relationship...

Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—the Netherlands and United States

Science.gov (United States)

To include feed-intake-related traits in the breeding goal, accurate estimates of genetic parameters of feed intake, and its correlations with other related traits (i.e., production, conformation) are required to compare different options. However, the correlations between feed intake and conformati...

Multiple-trait estimates of genetic parameters for metabolic disease traits, fertility disorders, and their predictors in Canadian Holsteins.

Science.gov (United States)

Jamrozik, J; Koeck, A; Kistemaker, G J; Miglior, F

2016-03-01

Producer-recorded health data for metabolic disease traits and fertility disorders on 35,575 Canadian Holstein cows were jointly analyzed with selected indicator traits. Metabolic diseases included clinical ketosis (KET) and displaced abomasum (DA); fertility disorders were metritis (MET) and retained placenta (RP); and disease indicators were fat-to-protein ratio, milk β-hydroxybutyrate, and body condition score (BCS) in the first lactation. Traits in first and later (up to fifth) lactations were treated as correlated in the multiple-trait (13 traits in total) animal linear model. Bayesian methods with Gibbs sampling were implemented for the analysis. Estimates of heritability for disease incidence were low, up to 0.06 for DA in first lactation. Among disease traits, the environmental herd-year variance constituted 4% of the total variance for KET and less for other traits. First- and later-lactation disease traits were genetically correlated (from 0.66 to 0.72) across all traits, indicating different genetic backgrounds for first and later lactations. Genetic correlations between KET and DA were relatively strong and positive (up to 0.79) in both first- and later-lactation cows. Genetic correlations between fertility disorders were slightly lower. Metritis was strongly genetically correlated with both metabolic disease traits in the first lactation only. All other genetic correlations between metabolic and fertility diseases were statistically nonsignificant. First-lactation KET and MET were strongly positively correlated with later-lactation performance for these traits due to the environmental herd-year effect. Indicator traits were moderately genetically correlated (from 0.30 to 0.63 in absolute values) with both metabolic disease traits in the first lactation. Smaller and mostly nonsignificant genetic correlations were among indicators and metabolic diseases in later lactations. The only significant genetic correlations between indicators and fertility

Parameter and State Estimator for State Space Models

Directory of Open Access Journals (Sweden)

Ruifeng Ding

2014-01-01

Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Parameter Estimation in Stochastic Grey-Box Models

DEFF Research Database (Denmark)

Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay

2004-01-01

An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended...... Kalman filter and features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing observations. The software implementation is compared to an existing software tool...... and proves to have better performance both in terms of quality of estimates for nonlinear systems with significant diffusion and in terms of reproducibility. In particular, the new tool provides more accurate and more consistent estimates of the parameters of the diffusion term....

Kinetic parameter estimation from SPECT cone-beam projection measurements

International Nuclear Information System (INIS)

Huesman, Ronald H.; Reutter, Bryan W.; Zeng, G. Larry; Gullberg, Grant T.

1998-01-01

Kinetic parameters are commonly estimated from dynamically acquired nuclear medicine data by first reconstructing a dynamic sequence of images and subsequently fitting the parameters to time-activity curves generated from regions of interest overlaid upon the image sequence. Biased estimates can result from images reconstructed using inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system. If the SPECT data are acquired using cone-beam collimators wherein the gantry rotates so that the focal point of the collimators always remains in a plane, additional biases can arise from images reconstructed using insufficient, as well as truncated, projection samples. To overcome these problems we have investigated the estimation of kinetic parameters directly from SPECT cone-beam projection data by modelling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated chest image volume, kinetic parameters were estimated for simple one-compartment models for four myocardial regions of interest. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated cone-beam data had biases ranging between 3-26% and 0-28%, respectively. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Statistical uncertainties of parameter estimates for 10 000 000 events ranged between 0.2-9% for the uptake parameters and between 0.3-6% for the washout parameters. (author)

Traveltime approximations and parameter estimation for orthorhombic media

KAUST Repository

Masmoudi, Nabil

2016-05-30

Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.

Estimates of selection parameters in protein mutants of spring barley

International Nuclear Information System (INIS)

Gaul, H.; Walther, H.; Seibold, K.H.; Brunner, H.; Mikaelsen, K.

1976-01-01

Detailed studies have been made with induced protein mutants regarding a possible genetic advance in selection including the estimation of the genetic variation and heritability coefficients. Estimates were obtained for protein content and protein yield. The variation of mutant lines in different environments was found to be many times as large as the variation of the line means. The detection of improved protein mutants seems therefore possible only in trials with more than one environment. The heritability of protein content and protein yield was estimated in different sets of environments and was found to be low. However, higher values were found with an increasing number of environments. At least four environments seem to be necessary to obtain reliable heritability estimates. The geneticall component of the variation between lines was significant for protein content in all environmental combinations. For protein yield some environmental combinations only showed significant differences. The expected genetic advance with one selection step was small for both protein traits. Genetically significant differences between protein micromutants give, however, a first indication that selection among protein mutants with small differences seems also possible. (author)

Parameter Estimation of Nonlinear Models in Forestry.

OpenAIRE

Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.

1999-01-01

Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...

Nonlinear Parameter Estimation in Microbiological Degradation Systems and Statistic Test for Common Estimation

DEFF Research Database (Denmark)

Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik

1995-01-01

Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...... and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the same values....... Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....

Parameter estimation in X-ray astronomy

International Nuclear Information System (INIS)

Lampton, M.; Margon, B.; Bowyer, S.

1976-01-01

The problems of model classification and parameter estimation are examined, with the objective of establishing the statistical reliability of inferences drawn from X-ray observations. For testing the validities of classes of models, the procedure based on minimizing the chi 2 statistic is recommended; it provides a rejection criterion at any desired significance level. Once a class of models has been accepted, a related procedure based on the increase of chi 2 gives a confidence region for the values of the model's adjustable parameters. The procedure allows the confidence level to be chosen exactly, even for highly nonlinear models. Numerical experiments confirm the validity of the prescribed technique.The chi 2 /sub min/+1 error estimation method is evaluated and found unsuitable when several parameter ranges are to be derived, because it substantially underestimates their joint errors. The ratio of variances method, while formally correct, gives parameter confidence regions which are more variable than necessary

A Novel Nonlinear Parameter Estimation Method of Soft Tissues

Directory of Open Access Journals (Sweden)

Qianqian Tong

2017-12-01

Full Text Available The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.

Genetic parameters of infectious bovine keratoconjunctivitis and its relationship with weight and parasite infestations in Australian tropical Bos taurus cattle

Directory of Open Access Journals (Sweden)

Ali Abdirahman A

2012-07-01

Full Text Available Abstract Background Infectious bovine keratoconjunctivitis (IBK or ‘pinkeye’ is an economically important ocular disease that significantly impacts animal performance. Genetic parameters for IBK infection and its genetic and phenotypic correlations with cattle tick counts, number of helminth (unspecified species eggs per gram of faeces and growth traits in Australian tropically adapted Bos taurus cattle were estimated. Methods Animals were clinically examined for the presence of IBK infection before and after weaning when the calves were 3 to 6 months and 15 to 18 months old, respectively and were also recorded for tick counts, helminth eggs counts as an indicator of intestinal parasites and live weights at several ages including 18 months. Results Negative genetic correlations were estimated between IBK incidence and weight traits for animals in pre-weaning and post-weaning datasets. Genetic correlations among weight measurements were positive, with moderate to high values. Genetic correlations of IBK incidence with tick counts were positive for the pre-weaning and negative for the post-weaning datasets but negative with helminth eggs counts for the pre-weaning dataset and slightly positive for the post-weaning dataset. Genetic correlations between tick and helminth eggs counts were moderate and positive for both datasets. Phenotypic correlations of IBK incidence with helminth eggs per gram of faeces were moderate and positive for both datasets, but were close to zero for both datasets with tick counts. Conclusions Our results suggest that genetic selection against IBK incidence in tropical cattle is feasible and that calves genetically prone to acquire IBK infection could also be genetically prone to have a slower growth. The positive genetic correlations among weight traits and between tick and helminth eggs counts suggest that they are controlled by common genes (with pleiotropic effects. Genetic correlations between IBK incidence

Genetic parameters of infectious bovine keratoconjunctivitis and its relationship with weight and parasite infestations in Australian tropical Bos taurus cattle.

Science.gov (United States)

Ali, Abdirahman A; O'Neill, Christopher J; Thomson, Peter C; Kadarmideen, Haja N

2012-07-27

Infectious bovine keratoconjunctivitis (IBK) or 'pinkeye' is an economically important ocular disease that significantly impacts animal performance. Genetic parameters for IBK infection and its genetic and phenotypic correlations with cattle tick counts, number of helminth (unspecified species) eggs per gram of faeces and growth traits in Australian tropically adapted Bos taurus cattle were estimated. Animals were clinically examined for the presence of IBK infection before and after weaning when the calves were 3 to 6 months and 15 to 18 months old, respectively and were also recorded for tick counts, helminth eggs counts as an indicator of intestinal parasites and live weights at several ages including 18 months. Negative genetic correlations were estimated between IBK incidence and weight traits for animals in pre-weaning and post-weaning datasets. Genetic correlations among weight measurements were positive, with moderate to high values. Genetic correlations of IBK incidence with tick counts were positive for the pre-weaning and negative for the post-weaning datasets but negative with helminth eggs counts for the pre-weaning dataset and slightly positive for the post-weaning dataset. Genetic correlations between tick and helminth eggs counts were moderate and positive for both datasets. Phenotypic correlations of IBK incidence with helminth eggs per gram of faeces were moderate and positive for both datasets, but were close to zero for both datasets with tick counts. Our results suggest that genetic selection against IBK incidence in tropical cattle is feasible and that calves genetically prone to acquire IBK infection could also be genetically prone to have a slower growth. The positive genetic correlations among weight traits and between tick and helminth eggs counts suggest that they are controlled by common genes (with pleiotropic effects). Genetic correlations between IBK incidence and tick and helminth egg counts were moderate and opposite between pre

Estimates for the parameters of the heavy quark expansion

Energy Technology Data Exchange (ETDEWEB)

Heinonen, Johannes; Mannel, Thomas [Universitaet Siegen (Germany)

2015-07-01

We give improved estimates for the non-perturbative parameters appearing in the heavy quark expansion for inclusive decays. While the parameters appearing in low orders of this expansion can be extracted from data, the number of parameters in higher orders proliferates strongly, making a determination of these parameters from data impossible. Thus, one has to rely on theoretical estimates which may be obtained from an insertion of intermediate states. We refine this method and attempt to estimate the uncertainties of this approach.

Genetic parameters of body weight and ascites in broilers: effect of different incidence rates of ascites syndrome.

Science.gov (United States)

Ahmadpanah, J; Ghavi Hossein-Zadeh, N; Shadparvar, A A; Pakdel, A

2017-02-01

1. The objectives of the current study were to investigate the effect of incidence rate (5%, 10%, 20%, 30% and 50%) of ascites syndrome on the expression of genetic characteristics for body weight at 5 weeks of age (BW5) and AS and to compare different methods of genetic parameter estimation for these traits. 2. Based on stochastic simulation, a population with discrete generations was created in which random mating was used for 10 generations. Two methods of restricted maximum likelihood and Bayesian approach via Gibbs sampling were used for the estimation of genetic parameters. A bivariate model including maternal effects was used. The root mean square error for direct heritabilities was also calculated. 3. The results showed that when incidence rates of ascites increased from 5% to 30%, the heritability of AS increased from 0.013 and 0.005 to 0.110 and 0.162 for linear and threshold models, respectively. 4. Maternal effects were significant for both BW5 and AS. Genetic correlations were decreased by increasing incidence rates of ascites in the population from 0.678 and 0.587 at 5% level of ascites to 0.393 and -0.260 at 50% occurrence for linear and threshold models, respectively. 5. The RMSE of direct heritability from true values for BW5 was greater based on a linear-threshold model compared with the linear model of analysis (0.0092 vs. 0.0015). The RMSE of direct heritability from true values for AS was greater based on a linear-linear model (1.21 vs. 1.14). 6. In order to rank birds for ascites incidence, it is recommended to use a threshold model because it resulted in higher heritability estimates compared with the linear model and that BW5 could be one of the main components of selection goals.

On robust parameter estimation in brain-computer interfacing

Science.gov (United States)

Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert

2017-12-01

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

Assessment of Genetic Parameters of Agronomic Traits in Bread Wheat using Generation Means Analysis under water-limited Conditions

Directory of Open Access Journals (Sweden)

M Dorrani-Nejad

2017-10-01

Full Text Available Introduction Wheat is the oldest and most important cultivated crop in the world and has fundamental role in human food security. Drought is one of the most common environmental stresses that affect growth and development of plants. Most parts of Iran’s cultivation land are located in arid and semiarid regions and because of water deficiency, plant stress appear and wheat performance reduces severely in these regions. In such circumstances, the production of drought tolerant varieties has special importance. Understand the genetic basis of yield and yield related traits is necessary in breeding programs. One of the best approaches to determine genetic parameters is generation means analysis method, due to it allows breeders to predict epistasis. In order to estimate genetic parameters and evaluation of gene action controlling agronomic traits in bread wheat under moisture stress, F4 families derived from cross between Roushan and Kavir along with F2, F3 and parents, were evaluated under moisture stress. Materials and Methods Field experiment was carried out in research field of Shahid Bahonar University of Kerman, during growing season of year 2013-2014 using Augmented design with 5 known check cultivars (Roushan, Falat, Mahdavi, Karchia and Shahpasand. Stress treatment was cut off irrigation at heading stage. Grain yield and some agronomic traits were measured. Generation means analysis method was used to determine genetic parameters including additive effect (d, dominance effect (h, additive × additive [i], and dominance × dominance effect [l] were evaluated for different traits. Generation means analysis was carried out using equation 1. Y= m+α[d]+β[h]+α2[i]+2αβ[j]+β2[l] (1 Broad and narrow sense heritability of evaluated traits were estimated according to equation 2 and 3. Results and Discussion The study revealed a complex genetic control for studied traits. Genetic variation in F2, F3 and F4 was more than parents. Five-parameter

Optimization of MIS/IL solar cells parameters using genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Ahmed, K.A.; Mohamed, E.A.; Alaa, S.H. [Faculty of Engineering, Alexandria Univ. (Egypt); Motaz, M.S. [Institute of Graduate Studies and Research, Alexandria Univ. (Egypt)

2004-07-01

This paper presents a genetic algorithm optimization for MIS/IL solar cell parameters including doping concentration NA, metal work function {phi}m, oxide thickness d{sub ox}, mobile charge density N{sub m}, fixed oxide charge density N{sub ox} and the external back bias applied to the inversion grid V. The optimization results are compared with theoretical optimization and shows that the genetic algorithm can be used for determining the optimum parameters of the cell. (orig.)

Genetic parameters for reproduction rate in the Tygerhoek Merino ...

African Journals Online (AJOL)

Dolling, 1963; Lewer, Rae & Wickham, 1983). Genetic. Number of lambing opportunities correlations involving EclEm and Ld/Lb, that showed. 2. 3. 4. 5 little genetic variation (Cloete &Heydenrych, 1987)were. Item particularly unstable. Negative between-sire variance. First set of data components prevented the estimation of ...

A simulation of water pollution model parameter estimation

Science.gov (United States)

Kibler, J. F.

1976-01-01

A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data

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Xueqin Zhou

2017-01-01

Full Text Available This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well.

Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice

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Salgado Concepción

2006-09-01

Full Text Available Abstract The aim of this research was to explore the genetic parameters associated with environmental variability for litter size (LS, litter weight (LW and mean individual birth weight (IW in mice before canalisation. The analyses were conducted on an experimental mice population designed to reduce environmental variability for LS. The analysed database included 1976 records for LW and IW and 4129 records for LS. The total number of individuals included in the analysed pedigree was 3997. Heritabilities estimated for the traits under an initial exploratory approach varied from 0.099 to 0.101 for LS, from 0.112 to 0.148 for LW and from 0.028 to 0.033 for IW. The means of the posterior distribution of the heritability under a Bayesian approach were the following: 0.10 (LS, 0.13 (LW and 0.03 (IW. In general, the heritabilities estimated under the initial exploratory approach for the environmental variability of the analysed traits were low. Genetic correlations estimated between the trait and its variability reached values of -0.929 (LS, -0.815 (LW and 0.969 (IW. The results presented here for the first time in mice may suggest a genetic basis for variability of the evaluated traits, thus opening the possibility to be implemented in selection schemes.

Genetic parameters for residual feed intake in a random population of Pekin duck

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Yunsheng Zhang

2017-02-01

Full Text Available Objective The feed intake (FI and feed efficiency are economically important traits in ducks. To obtain insight into this economically important trait, we designed an experiment based on the residual feed intake (RFI and feed conversion ratio (FCR of a random population Pekin duck. Methods Two thousand and twenty pedigreed random population Pekin ducks were established from 90 males mated to 450 females in two hatches. Traits analyzed in the study were body weight at the 42th day (BW42, 15 to 42 days average daily gain (ADG, 15 to 42 days FI, 15 to 42 days FCR, and 15 to 42 days RFI to assess their genetic inter-relationships. The genetic parameters for feed efficiency traits were estimated using restricted maximum likelihood (REML methodology applied to a sire-dam model for all traits using the ASREML software. Results Estimates heritability of BW42, ADG, FI, FCR, and RFI were 0.39, 0.38, 0.33, 0.38, and 0.41, respectively. The genetic correlation was high between RFI and FI (0.77 and moderate between RFI and FCR (0.54. The genetic correlation was high and moderate between FCR and ADG (−0.80, and between FCR and BW42 (−0.64, and between FCR and FI (0.49, respectively. Conclusion Thus, selection on RFI was expected to improve feed efficiency, and reduce FI. Selection on RFI thus improves the feed efficiency of animals without impairing their FI and increase growth rate.

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

Science.gov (United States)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Parameter estimation in stochastic differential equations

CERN Document Server

Bishwal, Jaya P N

2008-01-01

Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.

Estimation of Genetic Parameters for the Protein Profile in Danish Holstein Milk

DEFF Research Database (Denmark)

Buitenhuis, Albert Johannes; Poulsen, Nina Aagaard; Larsen, Lotte Bach

in the univariate and bivariate analysis of the protein traits. Hertitabilities ranged from 0.77 for κ-CN% to 0 for αs 1-CN% (SE: 0.13-0.21). The genetic correlation between β-CN and κ-CN was low (0.01 ± 0.53) whereas the genetic correlation of αs 2-CN with both β-CN and κ-CN was high (0.90). Furthermore α...

How to fool cosmic microwave background parameter estimation

International Nuclear Information System (INIS)

Kinney, William H.

2001-01-01

With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the cosmic microwave background (CMB) have reached unprecedented precision. In this paper I show that it is possible for these estimates to be substantially biased by features in the primordial density power spectrum. I construct primordial power spectra which mimic to within cosmic variance errors the effect of changing parameters such as the baryon density and neutrino mass, meaning that even an ideal measurement would be unable to resolve the degeneracy. Complementary measurements are necessary to resolve this ambiguity in parameter estimation efforts based on CMB temperature fluctuations alone

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

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Jonathan R Karr

2015-05-01

Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Genetic parameters for female fertility, locomotion, body condition score, and linear type traits in Czech Holstein cattle.

Science.gov (United States)

Zink, V; Štípková, M; Lassen, J

2011-10-01

The aim of this study was to estimate genetic parameters for fertility traits and linear type traits in the Czech Holstein dairy cattle population. Phenotypic data regarding 12 linear type traits, measured in first lactation, and 3 fertility traits, measured in each of first and second lactation, were collected from 2005 to 2009 in the progeny testing program of the Czech-Moravian Breeders Corporation. The number of animals for each linear type trait was 59,467, except for locomotion, where 53,436 animals were recorded. The 3-generation pedigree file included 164,125 animals. (Co)variance components were estimated using AI-REML in a series of bivariate analyses, which were implemented via the DMU package. Fertility traits included days from calving to first service (CF1), days open (DO1), and days from first to last service (FL1) in first lactation, and days from calving to first service (CF2), days open (DO2), and days from first to last service (FL2) in second lactation. The number of animals with fertility data varied between traits and ranged from 18,915 to 58,686. All heritability estimates for reproduction traits were low, ranging from 0.02 to 0.04. Heritability estimates for linear type traits ranged from 0.03 for locomotion to 0.39 for stature. Estimated genetic correlations between fertility traits and linear type traits were generally neutral or positive, whereas genetic correlations between body condition score and CF1, DO1, FL1, CF2 and DO2 were mostly negative, with the greatest correlation between BCS and CF2 (-0.51). Genetic correlations with locomotion were greatest for CF1 and CF2 (-0.34 for both). Results of this study show that cows that are genetically extreme for angularity, stature, and body depth tend to perform poorly for fertility traits. At the same time, cows that are genetically predisposed for low body condition score or high locomotion score are generally inferior in fertility. Copyright © 2011 American Dairy Science Association

Uncertanity Analysis in Parameter Estimation of Coupled Bacteria-Sediment Fate and Transport in Streams

Science.gov (United States)

Massoudieh, A.; Le, T.; Pachepsky, Y. A.

2014-12-01

E. coli is widely used as an fecal indicator bacteria in streams. It has been shown that the interaction between sediments and the bacteria is an important factor in determining its fate and transport in water bodies. In this presentation parameter estimation and uncertainty analysis of a mechanistic model of bacteria-sediment interaction respectively using a hybrid genetic algorithm and Makov-Chain Monte Carlo (MCMC) approach will be presented. The physically-based model considers the advective-dispersive transport of sediments as well as both free-floating and sediment-associated bacteria in the water column and also the fate and transport of bacteria in the bed sediments in a small stream. The bed sediments are treated as a distributed system which allows modeling the evolution of the vertical distribution of bacteria as a result of sedimentation and resuspension, diffusion and bioturbation in the sediments. One-dimensional St. Venant's equation is used to model flow in the stream. The model is applied to sediment and E. coli concentration data collected during a high flow event in a small stream historically receiving agricultural runoff. Measured total suspended sediments and total E. coli concentrations in the water column at three sections of the stream are used for the parameter estimation. The data on the initial distribution of E. coli in the sediments was available and was used as the initial conditions. The MCMC method is used to estimate the joint probability distribution of model parameters including sediment deposition and erosion rates, critical shear stress for deposition and erosion, attachment and detachment rate constants of E. coli to/from sediments and also the effective diffusion coefficients of E. coli in the bed sediments. The uncertainties associated with the estimated parameters are quantified via the MCMC approach and the correlation between the posterior distribution of parameters have been used to assess the model adequacy and

Parameter determination for quantitative PIXE analysis using genetic algorithms

International Nuclear Information System (INIS)

Aspiazu, J.; Belmont-Moreno, E.

1996-01-01

For biological and environmental samples, PIXE technique is in particular advantage for elemental analysis, but the quantitative analysis implies accomplishing complex calculations that require the knowledge of more than a dozen parameters. Using a genetic algorithm, the authors give here an account of the procedure to obtain the best values for the parameters necessary to fit the efficiency for a X-ray detector. The values for some variables involved in quantitative PIXE analysis, were manipulated in a similar way as the genetic information is treated in a biological process. The authors carried out the algorithm until they reproduce, within the confidence interval, the elemental concentrations corresponding to a reference material

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

Science.gov (United States)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

Science.gov (United States)

Hoshiba, Yasuhiro; Hirata, Takafumi; Shigemitsu, Masahito; Nakano, Hideyuki; Hashioka, Taketo; Masuda, Yoshio; Yamanaka, Yasuhiro

2018-06-01

Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Genetic variability and heritability estimates of some polygenic traits in upland cotton

International Nuclear Information System (INIS)

Baloch, M.J.

2004-01-01

Plant breeders are more interested in genetic variance rather than phenotypic variance because it is amenable to selection and bring further improvement in the character. Twenty-eight F/sub 2/ progenies were tested in two environments so as to predict genetic variances, heritability estimates and genetic gains. Mean squares for locations were significant for all the five traits suggesting that genotypes performed differently under varying environments. Genetic variances, in most cases, however, were about equal to that of phenotypic variances consequently giving high heritability estimates and significant genetic gains. The broad sense heritability estimates were; 94.2, 92.9, 33.6, 81.9 and 86.9% and genetic gains were; 30.19, 10.55,0.20,0.89 and 1.76 in seed cotton yield, bolls per plant, lint %, fibre length and fibre uniformity ratio, respectively. Substantial genetic variances and high heritability estimates implied that these characters could be improved through selection from segregating populations. (author)

Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

Directory of Open Access Journals (Sweden)

Mahdi Bashiri

2012-02-01

Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

Bayesian and maximum likelihood estimation of genetic maps

DEFF Research Database (Denmark)

York, Thomas L.; Durrett, Richard T.; Tanksley, Steven

2005-01-01

There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods...... of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances....

Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects

Directory of Open Access Journals (Sweden)

Meyer Karin

2001-11-01

Full Text Available Abstract A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.

Genetic parameters for production traits and somatic cell score of the ...

African Journals Online (AJOL)

Paula Bouwer

2013-05-26

May 26, 2013 ... of the other South African dairy breeds, based on the same model. ... Keywords: Genetic evaluation, genetic parameters, milk, protein, butterfat, somatic ... By means of performance measurements, the breeding values (genetic value) ... In comparison with the 63% of dairy cattle that are tested in other ICAR ...

Estimation of light transport parameters in biological media using ...

Indian Academy of Sciences (India)

Estimation of light transport parameters in biological media using coherent backscattering ... backscattered light for estimating the light transport parameters of biological media has been investigated. ... Pramana – Journal of Physics | News.

Statistics of Parameter Estimates: A Concrete Example

KAUST Repository

Aguilar, Oscar; Allmaras, Moritz; Bangerth, Wolfgang; Tenorio, Luis

2015-01-01

© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.

2013-01-01

PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus

Can genetic estimators provide robust estimates of the effective number of breeders in small populations?

Directory of Open Access Journals (Sweden)

Marion Hoehn

Full Text Available The effective population size (N(e is proportional to the loss of genetic diversity and the rate of inbreeding, and its accurate estimation is crucial for the monitoring of small populations. Here, we integrate temporal studies of the gecko Oedura reticulata, to compare genetic and demographic estimators of N(e. Because geckos have overlapping generations, our goal was to demographically estimate N(bI, the inbreeding effective number of breeders and to calculate the N(bI/N(a ratio (N(a =number of adults for four populations. Demographically estimated N(bI ranged from 1 to 65 individuals. The mean reduction in the effective number of breeders relative to census size (N(bI/N(a was 0.1 to 1.1. We identified the variance in reproductive success as the most important variable contributing to reduction of this ratio. We used four methods to estimate the genetic based inbreeding effective number of breeders N(bI(gen and the variance effective populations size N(eV(gen estimates from the genotype data. Two of these methods - a temporal moment-based (MBT and a likelihood-based approach (TM3 require at least two samples in time, while the other two were single-sample estimators - the linkage disequilibrium method with bias correction LDNe and the program ONeSAMP. The genetic based estimates were fairly similar across methods and also similar to the demographic estimates excluding those estimates, in which upper confidence interval boundaries were uninformative. For example, LDNe and ONeSAMP estimates ranged from 14-55 and 24-48 individuals, respectively. However, temporal methods suffered from a large variation in confidence intervals and concerns about the prior information. We conclude that the single-sample estimators are an acceptable short-cut to estimate N(bI for species such as geckos and will be of great importance for the monitoring of species in fragmented landscapes.

Variance Components and Genetic Parameters for Milk Production and Lactation Pattern in an Ethiopian Multibreed Dairy Cattle Population

Directory of Open Access Journals (Sweden)

Gebregziabher Gebreyohannes

2013-09-01

Full Text Available The objective of this study was to estimate variance components and genetic parameters for lactation milk yield (LY, lactation length (LL, average milk yield per day (YD, initial milk yield (IY, peak milk yield (PY, days to peak (DP and parameters (ln(a and c of the modified incomplete gamma function (MIG in an Ethiopian multibreed dairy cattle population. The dataset was composed of 5,507 lactation records collected from 1,639 cows in three locations (Bako, Debre Zeit and Holetta in Ethiopia from 1977 to 2010. Parameters for MIG were obtained from regression analysis of monthly test-day milk data on days in milk. The cows were purebred (Bos indicus Boran (B and Horro (H and their crosses with different fractions of Friesian (F, Jersey (J and Simmental (S. There were 23 breed groups (B, H, and their crossbreds with F, J, and S in the population. Fixed and mixed models were used to analyse the data. The fixed model considered herd-year-season, parity and breed group as fixed effects, and residual as random. The single and two-traits mixed animal repeatability models, considered the fixed effects of herd-year-season and parity subclasses, breed as a function of cow H, F, J, and S breed fractions and general heterosis as a function of heterozygosity, and the random additive animal, permanent environment, and residual effects. For the analysis of LY, LL was added as a fixed covariate to all models. Variance components and genetic parameters were estimated using average information restricted maximum likelihood procedures. The results indicated that all traits were affected (p<0.001 by the considered fixed effects. High grade B×F cows (3/16B 13/16F had the highest least squares means (LSM for LY (2,490±178.9 kg, IY (10.5±0.8 kg, PY (12.7±0.9 kg, YD (7.6±0.55 kg and LL (361.4±31.2 d, while B cows had the lowest LSM values for these traits. The LSM of LY, IY, YD, and PY tended to increase from the first to the fifth parity. Single-trait analyses

Genetic parameters for energy balance, fat /protein ratio, body condition score and disease traits in German Holstein cows.

Science.gov (United States)

Buttchereit, N; Stamer, E; Junge, W; Thaller, G

2012-08-01

Various health problems in dairy cows have been related to the magnitude and duration of the energy deficit post partum. Energy balance indicator traits like fat/protein ratio in milk and body condition score could be used in selection programmes to help predicting breeding values for health traits, but currently there is a lack of appropriate genetic parameters. Therefore, genetic correlations among energy balance, fat/protein ratio, and body condition score, and mastitis, claw and leg diseases, and metabolic disorders were estimated using linear and threshold models on data from 1693 primiparous cows recorded within the first 180 days in milk. Average daily energy balance, milk fat/protein ratio and body condition score were 8 MJ NEL, 1.13 and 2.94, respectively. Disease frequencies (% cows with at least one case) were 24.6% for mastitis, 9.7% for metabolic disorders and 28.2% for claw and leg diseases. Heritability estimates were 0.06, 0.30 and 0.34 for energy balance, fat/protein ratio and body condition score, respectively. For the disease traits, heritabilities ranged between 0.04 and 0.15. The genetic correlations were, in general, associated with large standard errors, but, although not significant, the results suggest that an improvement of overall health can be expected if energy balance traits are included into future breeding programmes. A low fat/protein ratio might serve as an indicator for metabolic stability and health of claw and legs. Between body condition and mastitis, a significant negative correlation of -0.40 was estimated. The study provides a new insight into the role energy balance traits can play as auxiliary traits for robustness of dairy cows. It was concluded that both, fat/protein ratio and body condition score, are potential variables to describe how well cows can adapt to the challenge of early lactation. However, the genetic parameters should be re-estimated on a more comprehensive data set. © 2011 Blackwell Verlag GmbH.

Kalman filter data assimilation: targeting observations and parameter estimation.

Science.gov (United States)

Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

2014-06-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Kalman filter data assimilation: Targeting observations and parameter estimation

International Nuclear Information System (INIS)

Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

2014-01-01

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation

Genetic parameters of infectious bovine keratoconjunctivitis and its relationship with weight and parasite infestations in Australian tropical Bos taurus cattle

DEFF Research Database (Denmark)

Ali, A.; O'Neill, C.J.; Thomson, P.C.

2012-01-01

recorded for tick counts, helminth eggs counts as an indicator of intestinal parasites and live weights at several ages including 18 months. Results: Negative genetic correlations were estimated between IBK incidence and weight traits for animals in pre-weaning and post-weaning datasets. Genetic......Background: Infectious bovine keratoconjunctivitis (IBK) or 'pinkeye' is an economically important ocular disease that significantly impacts animal performance. Genetic parameters for IBK infection and its genetic and phenotypic correlations with cattle tick counts, number of helminth (unspecified...... correlations among weight measurements were positive, with moderate to high values. Genetic correlations of IBK incidence with tick counts were positive for the pre-weaning and negative for the post-weaning datasets but negative with helminth eggs counts for the pre-weaning dataset and slightly positive...

Modeling and Parameter Estimation of a Small Wind Generation System

Directory of Open Access Journals (Sweden)

Carlos A. Ramírez Gómez

2013-11-01

Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.

Data compression can discriminate broilers by selection line, detect haplotypes, and estimate genetic potential for complex phenotypes.

Science.gov (United States)

Hudson, N J; Hawken, R J; Okimoto, R; Sapp, R L; Reverter, A

2017-09-01

Accurately establishing the relationships among individuals lays the foundation for genetic analyses such as genome-wide association studies and identification of selection signatures. Of particular interest to the poultry industry are estimates of genetic merit based on molecular data. These estimates can be commercially exploited in marker-assisted breeding programs to accelerate genetic improvement. Here, we test the utility of a new method we have recently developed to estimate animal relatedness and applied it to genetic parameter estimation in commercial broilers. Our approach is based on the concept of data compression from information theory. Using the real-world compressor gzip to estimate normalized compression distance (NCD) we have built compression-based relationship matrices (CRM) for 988 chickens from 4 commercial broiler lines-2 male and 2 female lines. For all pairs of individuals, we found a strong negative relationship between the commonly used genomic relationship matrix (GRM) and NCD. This reflects the fact that "similarity" is the inverse of "distance." The CRM explained more genetic variation than the corresponding GRM in 2 of 3 phenotypes, with corresponding improvements in accuracy of genomic-enabled predictions of breeding value. A sliding-window version of the analysis highlighted haplotype regions of the genome apparently under selection in a line-specific manner. In the male lines, we retrieved high population-specific scores for IGF-1 and a cognate receptor, INSR. For the female lines, we detected an extreme score for a region containing a reproductive hormone receptor (GNRHR). We conclude that our compression-based method is a valid approach to established relationships and identify regions under selective pressure in commercial lines of broiler chickens. © 2017 Poultry Science Association Inc.

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

Gupta, Ankit

2016-01-07

Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.

Load Estimation from Modal Parameters

DEFF Research Database (Denmark)

Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández

2007-01-01

In Natural Input Modal Analysis the modal parameters are estimated just from the responses while the loading is not recorded. However, engineers are sometimes interested in knowing some features of the loading acting on a structure. In this paper, a procedure to determine the loading from a FRF m...

A variational approach to parameter estimation in ordinary differential equations

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Kaschek Daniel

2012-08-01

Full Text Available Abstract Background Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. Results The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. Conclusions The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.

A variational approach to parameter estimation in ordinary differential equations.

Science.gov (United States)

Kaschek, Daniel; Timmer, Jens

2012-08-14

Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.

Parameter estimation in stochastic rainfall-runoff models

DEFF Research Database (Denmark)

Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

2006-01-01

A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

(Co) variance Components and Genetic Parameter Estimates for Re

African Journals Online (AJOL)

Mapula

The magnitude of heritability estimates obtained in the current study ... traits were recently introduced to supplement progeny testing programmes or for usage as sole source of ..... VCE-5 User's Guide and Reference Manual Version 5.1.

Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits.

Science.gov (United States)

May, Katharina; Brügemann, Kerstin; Yin, Tong; Scheper, Carsten; Strube, Christina; König, Sven

2017-09-01

Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05-0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (-0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk

Approximate effect of parameter pseudonoise intensity on rate of convergence for EKF parameter estimators. [Extended Kalman Filter

Science.gov (United States)

Hill, Bryon K.; Walker, Bruce K.

1991-01-01

When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.

Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability

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Rózsás Árpád

2015-12-01

Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.

Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

Science.gov (United States)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

Inflation and cosmological parameter estimation

Energy Technology Data Exchange (ETDEWEB)

Hamann, J.

2007-05-15

In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)

Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics

Science.gov (United States)

Kukreja, Sunil L.; Boyle, Richard D.

2014-01-01

Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.

A parameter tree approach to estimating system sensitivities to parameter sets

International Nuclear Information System (INIS)

Jarzemba, M.S.; Sagar, B.

2000-01-01

A post-processing technique for determining relative system sensitivity to groups of parameters and system components is presented. It is assumed that an appropriate parametric model is used to simulate system behavior using Monte Carlo techniques and that a set of realizations of system output(s) is available. The objective of our technique is to analyze the input vectors and the corresponding output vectors (that is, post-process the results) to estimate the relative sensitivity of the output to input parameters (taken singly and as a group) and thereby rank them. This technique is different from the design of experimental techniques in that a partitioning of the parameter space is not required before the simulation. A tree structure (which looks similar to an event tree) is developed to better explain the technique. Each limb of the tree represents a particular combination of parameters or a combination of system components. For convenience and to distinguish it from the event tree, we call it the parameter tree. To construct the parameter tree, the samples of input parameter values are treated as either a '+' or a '-' based on whether or not the sampled parameter value is greater than or less than a specified branching criterion (e.g., mean, median, percentile of the population). The corresponding system outputs are also segregated into similar bins. Partitioning the first parameter into a '+' or a '-' bin creates the first level of the tree containing two branches. At the next level, realizations associated with each first-level branch are further partitioned into two bins using the branching criteria on the second parameter and so on until the tree is fully populated. Relative sensitivities are then inferred from the number of samples associated with each branch of the tree. The parameter tree approach is illustrated by applying it to a number of preliminary simulations of the proposed high-level radioactive waste repository at Yucca Mountain, NV. Using a

Online State Space Model Parameter Estimation in Synchronous Machines

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Z. Gallehdari

2014-06-01

The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.

Unifying parameter estimation and the Deutsch-Jozsa algorithm for continuous variables

International Nuclear Information System (INIS)

Zwierz, Marcin; Perez-Delgado, Carlos A.; Kok, Pieter

2010-01-01

We reveal a close relationship between quantum metrology and the Deutsch-Jozsa algorithm on continuous-variable quantum systems. We develop a general procedure, characterized by two parameters, that unifies parameter estimation and the Deutsch-Jozsa algorithm. Depending on which parameter we keep constant, the procedure implements either the parameter-estimation protocol or the Deutsch-Jozsa algorithm. The parameter-estimation part of the procedure attains the Heisenberg limit and is therefore optimal. Due to the use of approximate normalizable continuous-variable eigenstates, the Deutsch-Jozsa algorithm is probabilistic. The procedure estimates a value of an unknown parameter and solves the Deutsch-Jozsa problem without the use of any entanglement.

Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters

Science.gov (United States)

Shi, L.

2015-12-01

This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

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Daigle Bernie J

2012-05-01

Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods

Postprocessing MPEG based on estimated quantization parameters

DEFF Research Database (Denmark)

Forchhammer, Søren

2009-01-01

the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...... in the postprocessing. We focus on deringing and present a scheme which aims at suppressing ringing artifacts, while maintaining the sharpness of the texture. The goal is to improve the visual quality, so perceptual blur and ringing metrics are used in addition to PSNR evaluation. The performance of the new `pure......' postprocessing compares favorable to a reference postprocessing filter which has access to the quantization parameters not only for I-frames but also on P and B-frames....

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

Science.gov (United States)

da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G

2016-07-08

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.

Estimation of transmission parameters of H5N1 avian influenza virus in chickens.

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Annemarie Bouma

2009-01-01

Full Text Available Despite considerable research efforts, little is yet known about key epidemiological parameters of H5N1 highly pathogenic influenza viruses in their avian hosts. Here we show how these parameters can be estimated using a limited number of birds in experimental transmission studies. Our quantitative estimates, based on Bayesian methods of inference, reveal that (i the period of latency of H5N1 influenza virus in unvaccinated chickens is short (mean: 0.24 days; 95% credible interval: 0.099-0.48 days; (ii the infectious period of H5N1 virus in unvaccinated chickens is approximately 2 days (mean: 2.1 days; 95%CI: 1.8-2.3 days; (iii the reproduction number of H5N1 virus in unvaccinated chickens need not be high (mean: 1.6; 95%CI: 0.90-2.5, although the virus is expected to spread rapidly because it has a short generation interval in unvaccinated chickens (mean: 1.3 days; 95%CI: 1.0-1.5 days; and (iv vaccination with genetically and antigenically distant H5N2 vaccines can effectively halt transmission. Simulations based on the estimated parameters indicate that herd immunity may be obtained if at least 80% of chickens in a flock are vaccinated. We discuss the implications for the control of H5N1 avian influenza virus in areas where it is endemic.

Composite likelihood estimation of demographic parameters

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Garrigan Daniel

2009-11-01

Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable

Case Study: On Objective Functions for the Peak Flow Calibration and for the Representative Parameter Estimation of the Basin

Directory of Open Access Journals (Sweden)

Jungwook Kim

2018-05-01

Full Text Available The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall–runoff model. However, it does not focus on peak flow and on representative parameter for various rain storm events of the basin, but it can estimate the optimal parameters by minimizing the overall error of observed and simulated flows. Therefore, the aim of this study is to suggest the objective functions that can fit peak flow in hydrograph and estimate the representative parameter of the basin for the events. The Streamflow Synthesis And Reservoir Regulation (SSARR model was employed to perform flood runoff simulation for the Mihocheon stream basin in Geum River, Korea. Optimization was conducted using three calibration methods: genetic algorithm, pattern search, and the Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA. Two objective functions of the Sum of Squared of Residual (SSR and the Weighted Sum of Squared of Residual (WSSR suggested in this study for peak flow optimization were applied. Since the parameters estimated using a single rain storm event do not represent the parameters for various rain storms in the basin, we used the representative objective function that can minimize the sum of objective functions of the events. Six rain storm events were used for the parameter estimation. Four events were used for the calibration and the other two for validation; then, the results by SSR and WSSR were compared. Flow runoff simulation was carried out based on the proposed objective functions, and the objective function of WSSR was found to be more useful than that of SSR in the simulation of peak flow runoff. Representative parameters that minimize the objective function for each of the four rain storm events were estimated. The calibrated observed and simulated flow runoff hydrographs obtained from applying the estimated representative

Residential-commercial energy input estimation based on genetic algorithm (GA) approaches: an application of Turkey

International Nuclear Information System (INIS)

Ozturk, H.K.; Canyurt, O.E.; Hepbasli, A.; Utlu, Z.

2004-01-01

The main objective of the present study is to develop the energy input estimation equations for the residential-commercial sector (RCS) in order to estimate the future projections based on genetic algorithm (GA) notion and to examine the effect of the design parameters on the energy input of the sector. For this purpose, the Turkish RCS is given as an example. The GA Energy Input Estimation Model (GAEIEM) is used to estimate Turkey's future residential-commercial energy input demand based on gross domestic product (GDP), population, import, export, house production, cement production and basic house appliances consumption figures. It may be concluded that the three various forms of models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies. (author)

Bayesian conjugate analysis using a generalized inverted Wishart distribution accounts for differential uncertainty among the genetic parameters--an application to the maternal animal model.

Science.gov (United States)

Munilla, S; Cantet, R J C

2012-06-01

Consider the estimation of genetic (co)variance components from a maternal animal model (MAM) using a conjugated Bayesian approach. Usually, more uncertainty is expected a priori on the value of the maternal additive variance than on the value of the direct additive variance. However, it is not possible to model such differential uncertainty when assuming an inverted Wishart (IW) distribution for the genetic covariance matrix. Instead, consider the use of a generalized inverted Wishart (GIW) distribution. The GIW is essentially an extension of the IW distribution with a larger set of distinct parameters. In this study, the GIW distribution in its full generality is introduced and theoretical results regarding its use as the prior distribution for the genetic covariance matrix of the MAM are derived. In particular, we prove that the conditional conjugacy property holds so that parameter estimation can be accomplished via the Gibbs sampler. A sampling algorithm is also sketched. Furthermore, we describe how to specify the hyperparameters to account for differential prior opinion on the (co)variance components. A recursive strategy to elicit these parameters is then presented and tested using field records and simulated data. The procedure returned accurate estimates and reduced standard errors when compared with non-informative prior settings while improving the convergence rates. In general, faster convergence was always observed when a stronger weight was placed on the prior distributions. However, analyses based on the IW distribution have also produced biased estimates when the prior means were set to over-dispersed values. © 2011 Blackwell Verlag GmbH.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Science.gov (United States)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Parameter estimation for an expanding universe

Directory of Open Access Journals (Sweden)

Jieci Wang

2015-03-01

Full Text Available We study the parameter estimation for excitations of Dirac fields in the expanding Robertson–Walker universe. We employ quantum metrology techniques to demonstrate the possibility for high precision estimation for the volume rate of the expanding universe. We show that the optimal precision of the estimation depends sensitively on the dimensionless mass m˜ and dimensionless momentum k˜ of the Dirac particles. The optimal precision for the ratio estimation peaks at some finite dimensionless mass m˜ and momentum k˜. We find that the precision of the estimation can be improved by choosing the probe state as an eigenvector of the hamiltonian. This occurs because the largest quantum Fisher information is obtained by performing projective measurements implemented by the projectors onto the eigenvectors of specific probe states.

Genetic parameters for image analysis traits on M. longissimus thoracis and M. trapezius of carcass cross section in Japanese Black steers.

Science.gov (United States)

Osawa, T; Kuchida, K; Hidaka, S; Kato, T

2008-01-01

In Japan, the degree of marbling in ribeye (M. longissimus thoracis) is evaluated in the beef meat grading process. However, other muscles (e.g., M. trapezius) are also important in determining the meat quality and carcass market prices. The purpose of this study was to estimate genetic parameters for M. longissimus thoracis (M-LONG) and M. trapezius (M-TRAP) of carcass cross section of Japanese Black steers by computer image analysis. The number of records of Japanese Black steers and the number of pedigree records were 2,925 and 10,889, respectively. Digital images of the carcass cross section were taken between the sixth and seventh ribs by photographing equipment. Muscle area (MA), fat area ratio (FAR), overall coarseness of marbling particles (OCM), and coarseness of maximum marbling particle (MMC) in M-LONG and M-TRAP were calculated by image analysis. Genetic parameters for these traits were estimated using the AIREMLF90 program with an animal model. Fixed effects that were included in the model were dates of arrival at the carcass market and slaughter age (mo), and random effects of fattening farms, additive genetic effects and residuals were included in the model. For M-LONG, heritability estimates (+/-SE) were 0.46 +/- 0.06, 0.59 +/- 0.06, 0.47 +/- 0.06, and 0.20 +/- 0.05 for MA, FAR, OCM, and MMC, respectively. Heritability estimates (+/-SE) in M-TRAP were 0.47 +/- 0.06, 0.57 +/- 0.07, 0.49 +/- 0.07, and 0.13 +/- 0.04 for the same traits. Genetic correlations between subcutaneous fat thickness and FAR for M-LONG and M-TRAP were negative (-0.21 and -0.19, respectively). Those correlations between M-LONG and M-TRAP were moderate to high for MA, FAR, OCM, and MMC (0.38, 0.52, 0.39, and 0.60, respectively). These results indicate that other muscles including M-LONG should be evaluated for more efficient genetic improvement.

Method for Estimating the Parameters of LFM Radar Signal

Directory of Open Access Journals (Sweden)

Tan Chuan-Zhang

2017-01-01

Full Text Available In order to obtain reliable estimate of parameters, it is very important to protect the integrality of linear frequency modulation (LFM signal. Therefore, in the practical LFM radar signal processing, the length of data frame is often greater than the pulse width (PW of signal. In this condition, estimating the parameters by fractional Fourier transform (FrFT will cause the signal to noise ratio (SNR decrease. Aiming at this problem, we multiply the data frame by a Gaussian window to improve the SNR. Besides, for a further improvement of parameters estimation precision, a novel algorithm is derived via Lagrange interpolation polynomial, and we enhance the algorithm by a logarithmic transformation. Simulation results demonstrate that the derived algorithm significantly reduces the estimation errors of chirp-rate and initial frequency.

Assumptions of the primordial spectrum and cosmological parameter estimation

International Nuclear Information System (INIS)

Shafieloo, Arman; Souradeep, Tarun

2011-01-01

The observables of the perturbed universe, cosmic microwave background (CMB) anisotropy and large structures depend on a set of cosmological parameters, as well as the assumed nature of primordial perturbations. In particular, the shape of the primordial power spectrum (PPS) is, at best, a well-motivated assumption. It is known that the assumed functional form of the PPS in cosmological parameter estimation can affect the best-fit-parameters and their relative confidence limits. In this paper, we demonstrate that a specific assumed form actually drives the best-fit parameters into distinct basins of likelihood in the space of cosmological parameters where the likelihood resists improvement via modifications to the PPS. The regions where considerably better likelihoods are obtained allowing free-form PPS lie outside these basins. In the absence of a preferred model of inflation, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed form of PPS. Our results strongly motivate approaches toward simultaneous estimation of the cosmological parameters and the shape of the primordial spectrum from upcoming cosmological data. It is equally important for theorists to keep an open mind towards early universe scenarios that produce features in the PPS. (paper)

Bayesian estimation of Weibull distribution parameters

International Nuclear Information System (INIS)

Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.

1994-11-01

In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs

Estimation of genetic diversity between three Saudi sheep breeds ...

African Journals Online (AJOL)

Estimation of genetic diversity between three Saudi sheep breeds using DNA markers. AAG Adam, NB Hamza, MAW Salim, KS Khalil. Abstract. The genetic variation of Najdi, Harri and Awassi breeds of Saudi sheep prevailing in Raniah province of Makka district were assessed and compared to Sudanese Desert sheep ...

Restoration of coral populations in light of genetic diversity estimates

Science.gov (United States)

Shearer, T. L.; Porto, I.; Zubillaga, A. L.

2009-09-01

Due to the importance of preserving the genetic integrity of populations, strategies to restore damaged coral reefs should attempt to retain the allelic diversity of the disturbed population; however, genetic diversity estimates are not available for most coral populations. To provide a generalized estimate of genetic diversity (in terms of allelic richness) of scleractinian coral populations, the literature was surveyed for studies describing the genetic structure of coral populations using microsatellites. The mean number of alleles per locus across 72 surveyed scleractinian coral populations was 8.27 (±0.75 SE). In addition, population genetic datasets from four species ( Acropora palmata, Montastraea cavernosa, Montastraea faveolata and Pocillopora damicornis) were analyzed to assess the minimum number of donor colonies required to retain specific proportions of the genetic diversity of the population. Rarefaction analysis of the population genetic datasets indicated that using 10 donor colonies randomly sampled from the original population would retain >50% of the allelic diversity, while 35 colonies would retain >90% of the original diversity. In general, scleractinian coral populations are genetically diverse and restoration methods utilizing few clonal genotypes to re-populate a reef will diminish the genetic integrity of the population. Coral restoration strategies using 10-35 randomly selected local donor colonies will retain at least 50-90% of the genetic diversity of the original population.

A Practical Approach for Parameter Identification with Limited Information

DEFF Research Database (Denmark)

Zeni, Lorenzo; Yang, Guangya; Tarnowski, Germán Claudio

2014-01-01

A practical parameter estimation procedure for a real excitation system is reported in this paper. The core algorithm is based on genetic algorithm (GA) which estimates the parameters of a real AC brushless excitation system with limited information about the system. Practical considerations are ...... parameters. The whole methodology is described and the estimation strategy is presented in this paper....

SCoPE: an efficient method of Cosmological Parameter Estimation

International Nuclear Information System (INIS)

Das, Santanu; Souradeep, Tarun

2014-01-01

Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data

use of genetic variability estimates and interrelationships

African Journals Online (AJOL)

Prof. Adipala Ekwamu

of 11 agronomic and biochemical traits to water stress based on estimation of genetic ... of primary branches and 100 seed weight under W0, and number of primary ... selection of superior drought-tolerant genotype (LR1) with good yield ...

Bayesian parameter estimation in probabilistic risk assessment

International Nuclear Information System (INIS)

Siu, Nathan O.; Kelly, Dana L.

1998-01-01

Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics

MODEL-ASSISTED ESTIMATION OF THE GENETIC VARIABILITY IN PHYSIOLOGICAL PARAMETERS RELATED TO TOMATO FRUIT GROWTH UNDER CONTRASTED WATER CONDITIONS

Directory of Open Access Journals (Sweden)

Dario Constantinescu

2016-12-01

Full Text Available Drought stress is a major abiotic stres threatening plant and crop productivity. In case of fleshy fruits, understanding Drought stress is a major abiotic stress threatening plant and crop productivity. In case of fleshy fruits, understanding mechanisms governing water and carbon accumulations and identifying genes, QTLs and phenotypes, that will enable trade-offs between fruit growth and quality under Water Deficit (WD condition is a crucial challenge for breeders and growers. In the present work, 117 recombinant inbred lines of a population of Solanum lycopersicum were phenotyped under control and WD conditions. Plant water status, fruit growth and composition were measured and data were used to calibrate a process-based model describing water and carbon fluxes in a growing fruit as a function of plant and environment. Eight genotype-dependent model parameters were estimated using a multiobjective evolutionary algorithm in order to minimize the prediction errors of fruit dry and fresh mass throughout fruit development. WD increased the fruit dry matter content (up to 85 % and decreased its fresh weight (up to 60 %, big fruit size genotypes being the most sensitive. The mean normalized root mean squared errors of the predictions ranged between 16-18 % in the population. Variability in model genotypic parameters allowed us to explore diverse genetic strategies in response to WD. An interesting group of genotypes could be discriminated in which i the low loss of fresh mass under WD was associated with high active uptake of sugars and low value of the maximum cell wall extensibility, and ii the high dry matter content in control treatment (C was associated with a slow decrease of mass flow. Using 501 SNP markers genotyped across the genome, a QTL analysis of model parameters allowed to detect three main QTLs related to xylem and phloem conductivities, on chromosomes 2, 4 and 8. The model was then applied to design ideotypes with high dry matter

Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters.

Directory of Open Access Journals (Sweden)

Kaarina Matilainen

Full Text Available Estimation of variance components by Monte Carlo (MC expectation maximization (EM restricted maximum likelihood (REML is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR, where the information matrix was generated via sampling; MC average information(AI, where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

Iterative methods for distributed parameter estimation in parabolic PDE

Energy Technology Data Exchange (ETDEWEB)

Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)

1994-12-31

The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.

A distributed approach for parameters estimation in System Biology models

International Nuclear Information System (INIS)

Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.

2009-01-01

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.

Summary of the BEIR V committee's estimates of genetic risks

International Nuclear Information System (INIS)

Grahn, D.

1990-01-01

The Committee on the Biological Effects of Ionizing Radiations (BEIR V) was constituted in late 1986 to conduct a comprehensive review of the biological effects of ionizing radiations focusing on information reported since the conclusion of the 1980 BEIR study, and to provide new estimates of the risks of genetic and somatic effects in humans due to low-level exposures of ionizing radiation. The Committee preferred the doubling-dose method of genetic risk estimation over the direct method. Data from animal (mouse) studies provide a median value of 100 to 114 cGy for long-term low dose rate exposure doubling doses. These values are lower than the median from human studies. The BEIR Committee believed that a doubling dose of 100 cGy would be a prudent value leading to conservative estimates. The estimated risks themselves are not much different from those generated by previous BEIR committees, UNSCEAR, and other published estimates. The Committee estimates that between 100 and 200 added cases per million live births will be observed at genetic equilibrium if the population is exposed each generation to a dose of 0.01 Sv (1 rem). Nearly half ware attributed to clinically mild dominant defects, and the balance to congenital abnormalities. (L.L.) (2 tabs.)

Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel.

Science.gov (United States)

Dansirikul, Chantaratsamon; Choi, Malcolm; Duffull, Stephen B

2005-06-01

This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel spreadsheet was implemented with the use of Solver and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.

Detrimental effect of selection for milk yield on genetic tolerance to heat stress in purebred Zebu cattle: Genetic parameters and trends.

Science.gov (United States)

Santana, M L; Pereira, R J; Bignardi, A B; Filho, A E Vercesi; Menéndez-Buxadera, A; El Faro, L

2015-12-01

In an attempt to determine the possible detrimental effects of continuous selection for milk yield on the genetic tolerance of Zebu cattle to heat stress, genetic parameters and trends of the response to heat stress for 86,950 test-day (TD) milk yield records from 14,670 first lactations of purebred dairy Gir cows were estimated. A random regression model with regression on days in milk (DIM) and temperature-humidity index (THI) values was applied to the data. The most detrimental effect of THI on milk yield was observed in the stage of lactation with higher milk production, DIM 61 to 120 (-0.099kg/d per THI). Although modest variations were observed for the THI scale, a reduction in additive genetic variance as well as in permanent environmental and residual variance was observed with increasing THI values. The heritability estimates showed a slight increase with increasing THI values for any DIM. The correlations between additive genetic effects across the THI scale showed that, for most of the THI values, genotype by environment interactions due to heat stress were less important for the ranking of bulls. However, for extreme THI values, this type of genotype by environment interaction may lead to an important error in selection. As a result of the selection for milk yield practiced in the dairy Gir population for 3 decades, the genetic trend of cumulative milk yield was significantly positive for production in both high (51.81kg/yr) and low THI values (78.48kg/yr). However, the difference between the breeding values of animals at high and low THI may be considered alarming (355kg in 2011). The genetic trends observed for the regression coefficients related to general production level (intercept of the reaction norm) and specific ability to respond to heat stress (slope of the reaction norm) indicate that the dairy Gir population is heading toward a higher production level at the expense of lower tolerance to heat stress. These trends reflect the genetic

Parameter Estimates in Differential Equation Models for Chemical Kinetics

Science.gov (United States)

Winkel, Brian

2011-01-01

We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…

Genetic parameters for carcass traits and in vovo measured muscle and fat depth in Danish Texel and Shropshire

DEFF Research Database (Denmark)

Maxa, Jan; Norberg, Elise; Berg, Peer

2007-01-01

Genetic parameters for carcass traits and ultrasonic scanning measurements were estimated for Danish Texel and Shropshire, the most common sheep breeds in Denmark. Data used in this study were collected from 1990 to 2005 by the Danish Agricultural Advisory Service. A multivariate animal model.......12 for Shropshire. Carcass conformation was highly heritable, 0.45 for Texel and 0.36 for Shropshire. The heritability for FAT was 0.11 for Texel and 0.19 for Shropshire. Genetic correlations between MD and FORM, and FD and FAT were positive and favourable. It was concluded that ultrasound measures on live animals...... was used for estimation of (co)variance components for muscle depth (MD), fat depth (FD), carcass conformation score (FORM) and carcass fatness (FAT). Heritabilities for MD were 0.29 and 0.28 for Texel and Shropshire, respectively. Diverging heritabilities were found for FD - 0.39 for Texel and 0...

Estimating physiological skin parameters from hyperspectral signatures

Science.gov (United States)

Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe

2013-05-01

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

A software for parameter estimation in dynamic models

Directory of Open Access Journals (Sweden)

M. Yuceer

2008-12-01

Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

Science.gov (United States)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

CTER—Rapid estimation of CTF parameters with error assessment

Energy Technology Data Exchange (ETDEWEB)

Penczek, Pawel A., E-mail: Pawel.A.Penczek@uth.tmc.edu [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Fang, Jia [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Li, Xueming; Cheng, Yifan [The Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158 (United States); Loerke, Justus; Spahn, Christian M.T. [Institut für Medizinische Physik und Biophysik, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin (Germany)

2014-05-01

In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. - Highlights: • We describe methodology for estimation of CTF parameters with error assessment. • Error estimates provide means for automated elimination of inferior micrographs. • High computational efficiency allows real-time monitoring of EM data quality. • Accurate CTF estimation yields structure of the 80S human ribosome at 3.85 Å.

Novel Method for 5G Systems NLOS Channels Parameter Estimation

Directory of Open Access Journals (Sweden)

Vladeta Milenkovic

2017-01-01

Full Text Available For the development of new 5G systems to operate in mm bands, there is a need for accurate radio propagation modelling at these bands. In this paper novel approach for NLOS channels parameter estimation will be presented. Estimation will be performed based on LCR performance measure, which will enable us to estimate propagation parameters in real time and to avoid weaknesses of ML and moment method estimation approaches.

Periodic orbits of hybrid systems and parameter estimation via AD

International Nuclear Information System (INIS)

Guckenheimer, John; Phipps, Eric Todd; Casey, Richard

2004-01-01

between two given periodic orbits which is then minimized using a trust-region minimization algorithm (DS83) to find optimal fits of the model to a reference orbit (Cas04). There are two different yet related goals that motivate the algorithmic choices listed above. The first is to provide a simple yet powerful framework for studying periodic motions in mechanical systems. Formulating mechanically correct equations of motion for systems of interconnected rigid bodies, while straightforward, is a time-consuming error prone process. Much of this difficulty stems from computing the acceleration of each rigid body in an inertial reference frame. The acceleration is computed most easily in a redundant set of coordinates giving the spatial positions of each body: since the acceleration is just the second derivative of these positions. Rather than providing explicit formulas for these derivatives, automatic differentiation can be employed to compute these quantities efficiently during the course of a simulation. The feasibility of these ideas was investigated by applying these techniques to the problem of locating stable walking motions for a disc-foot passive walking machine (CGMR01, Gar99, McG91). The second goal for this work was to investigate the application of smooth optimization methods to periodic orbit parameter estimation problems in neural oscillations. Others (BB93, FUS93, VB99) have favored non-continuous optimization methods such as genetic algorithms, stochastic search methods, simulated annealing and brute-force random searches because of their perceived suitability to the landscape of typical objective functions in parameter space, particularly for multi-compartmental neural models. Here we argue that a carefully formulated optimization problem is amenable to Newton-like methods and has a sufficiently smooth landscape in parameter space that these methods can be an efficient and effective alternative. The plan of this paper is as follows. In Section 1 we provide

Application of isotopic information for estimating parameters in Philip infiltration model

Directory of Open Access Journals (Sweden)

Tao Wang

2016-10-01

Full Text Available Minimizing parameter uncertainty is crucial in the application of hydrologic models. Isotopic information in various hydrologic components of the water cycle can expand our knowledge of the dynamics of water flow in the system, provide additional information for parameter estimation, and improve parameter identifiability. This study combined the Philip infiltration model with an isotopic mixing model using an isotopic mass balance approach for estimating parameters in the Philip infiltration model. Two approaches to parameter estimation were compared: (a using isotopic information to determine the soil water transmission and then hydrologic information to estimate the soil sorptivity, and (b using hydrologic information to determine the soil water transmission and the soil sorptivity. Results of parameter estimation were verified through a rainfall infiltration experiment in a laboratory under rainfall with constant isotopic compositions and uniform initial soil water content conditions. Experimental results showed that approach (a, using isotopic and hydrologic information, estimated the soil water transmission in the Philip infiltration model in a manner that matched measured values well. The results of parameter estimation of approach (a were better than those of approach (b. It was also found that the analytical precision of hydrogen and oxygen stable isotopes had a significant effect on parameter estimation using isotopic information.

A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

Directory of Open Access Journals (Sweden)

Ibrahim M. Safwat

2017-11-01

Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.

On the estimation of water pure compound parameters in association theories

DEFF Research Database (Denmark)

Grenner, Andreas; Kontogeorgis, Georgios; Michelsen, Michael Locht

2007-01-01

Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using t...... different association theories. Their performance for various properties as well as against the results presented earlier is demonstrated.......Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using two...

Parameter Estimation for a Computable General Equilibrium Model

DEFF Research Database (Denmark)

Arndt, Channing; Robinson, Sherman; Tarp, Finn

We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...

Modular Estimation Strategy of Vehicle Dynamic Parameters for Motion Control Applications

Directory of Open Access Journals (Sweden)

Rawash Mustafa

2018-01-01

Full Text Available The presence of motion control or active safety systems in vehicles have become increasingly important for improving vehicle performance and handling and negotiating dangerous driving situations. The performance of such systems would be improved if combined with knowledge of vehicle dynamic parameters. Since some of these parameters are difficult to measure, due to technical or economic reasons, estimation of those parameters might be the only practical alternative. In this paper, an estimation strategy of important vehicle dynamic parameters, pertaining to motion control applications, is presented. The estimation strategy is of a modular structure such that each module is concerned with estimating a single vehicle parameter. Parameters estimated include: longitudinal, lateral, and vertical tire forces – longitudinal velocity – vehicle mass. The advantage of this strategy is its independence of tire parameters or wear, road surface condition, and vehicle mass variation. Also, because of its modular structure, each module could be later updated or exchanged for a more effective one. Results from simulations on a 14-DOF vehicle model are provided here to validate the strategy and show its robustness and accuracy.

Estimation of object motion parameters from noisy images.

Science.gov (United States)

Broida, T J; Chellappa, R

1986-01-01

An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.

State and parameter estimation in biotechnical batch reactors

NARCIS (Netherlands)

Keesman, K.J.

2000-01-01

In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in

Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm

Directory of Open Access Journals (Sweden)

Saad Mohd Sazli

2016-01-01

Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.

Global parameter estimation for thermodynamic models of transcriptional regulation.

Science.gov (United States)

Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N

2013-07-15

Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.

Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

International Nuclear Information System (INIS)

Yamamoto, A.; Yasue, Y.; Endo, T.; Kodama, Y.; Ohoka, Y.; Tatsumi, M.

2012-01-01

An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)

Parameter Estimation for a Computable General Equilibrium Model

DEFF Research Database (Denmark)

Arndt, Channing; Robinson, Sherman; Tarp, Finn

2002-01-01

We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...

Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

Science.gov (United States)

Raykov, Tenko

2005-01-01

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…

State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates

International Nuclear Information System (INIS)

Liang Jinling; Lam, James; Wang Zidong

2009-01-01

This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes.

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

Science.gov (United States)

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

International Nuclear Information System (INIS)

Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.

2016-01-01

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

Energy Technology Data Exchange (ETDEWEB)

Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.

2016-03-11

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

Maximum-likelihood estimation of the hyperbolic parameters from grouped observations

DEFF Research Database (Denmark)

Jensen, Jens Ledet

1988-01-01

a least-squares problem. The second procedure Hypesti first approaches the maximum-likelihood estimate by iterating in the profile-log likelihood function for the scale parameter. Close to the maximum of the likelihood function, the estimation is brought to an end by iteration, using all four parameters...

Genetic Parameters for Reproduction Traits of Prolificacy and Conventional Purebred Sows

Directory of Open Access Journals (Sweden)

Vitomir Vidović

2012-05-01

Full Text Available Research was performed on four farms were included in 1567 a highly fertile females Landrace and Yorkshire, and 24 boars of Danish origin, or 5294 consecutive parities, and in period 2009 - 2011 year. Studies of evaluations genetic parameters conventional breeds Landrace and Yorkshire were included in 2987 female mating with 46 male or 11 674 litters in the same period. Evaluated genetic parameters for litter size traits show the same tendency as the legality of the pure breed sows that produce 11-14 piglets weaned less per sow per year. Environmental factors, HYS, food technology and management showed no significant effect on the traits. Heritability and repeatability of live and still born piglets, litter size and the fifth days after birth and the number of piglets weaned in category of low hereditary traits whose values vary within the limits of 0.08 to 0.11 for the heritability and from 0.14 to 0.18 for the repeatability. There was tendency to lower values of genetic parameters in the conventional compared to highly fertile cows, which is considered the effect of selection on gene frequency for the observed properties.

Dual ant colony operational modal analysis parameter estimation method

Science.gov (United States)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

A genetic meta-algorithm-assisted inversion approach: hydrogeological study for the determination of volumetric rock properties and matrix and fluid parameters in unsaturated formations

Science.gov (United States)

Szabó, Norbert Péter

2018-03-01

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.

Genetic parameters of linear conformation type traits and their relationship with milk yield throughout lactation in mixed-breed dairy goats.

Science.gov (United States)

McLaren, A; Mucha, S; Mrode, R; Coffey, M; Conington, J

2016-07-01

Conformation traits are of interest to many dairy goat breeders not only as descriptive traits in their own right, but also because of their influence on production, longevity, and profitability. If these traits are to be considered for inclusion in future dairy goat breeding programs, relationships between them and production traits such as milk yield must be considered. With the increased use of regression models to estimate genetic parameters, an opportunity now exists to investigate correlations between conformation traits and milk yield throughout lactation in more detail. The aims of this study were therefore to (1) estimate genetic parameters for conformation traits in a population of crossbred dairy goats, (2) estimate correlations between all conformation traits, and (3) assess the relationship between conformation traits and milk yield throughout lactation. No information on milk composition was available. Data were collected from goats based on 2 commercial goat farms during August and September in 2013 and 2014. Ten conformation traits, relating to udder, teat, leg, and feet characteristics, were scored on a linear scale (1-9). The overall data set comprised data available for 4,229 goats, all in their first lactation. The population of goats used in the study was created using random crossings between 3 breeds: British Alpine, Saanen, and Toggenburg. In each generation, the best performing animals were selected for breeding, leading to the formation of a synthetic breed. The pedigree file used in the analyses contained sire and dam information for a total of 30,139 individuals. The models fitted relevant fixed and random effects. Heritability estimates for the conformation traits were low to moderate, ranging from 0.02 to 0.38. A range of positive and negative phenotypic and genetic correlations between the traits were observed, with the highest correlations found between udder depth and udder attachment (0.78), teat angle and teat placement (0

Cosmological parameter estimation using particle swarm optimization

Science.gov (United States)

Prasad, Jayanti; Souradeep, Tarun

2012-06-01

Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.

Estimating RASATI scores using acoustical parameters

International Nuclear Information System (INIS)

Agüero, P D; Tulli, J C; Moscardi, G; Gonzalez, E L; Uriz, A J

2011-01-01

Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.

Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

Directory of Open Access Journals (Sweden)

Kese Pontes Freitas Alberton

2015-01-01

Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

ESTIMATION OF GENETIC PARAMETERS IN TROPICARNE CATTLE WITH RANDOM REGRESSION MODELS USING B-SPLINES

Directory of Open Access Journals (Sweden)

Joel Domínguez Viveros

2015-04-01

Full Text Available The objectives were to estimate variance components, and direct (h2 and maternal (m2 heritability in the growth of Tropicarne cattle based on a random regression model using B-Splines for random effects modeling. Information from 12 890 monthly weightings of 1787 calves, from birth to 24 months old, was analyzed. The pedigree included 2504 animals. The random effects model included genetic and permanent environmental (direct and maternal of cubic order, and residuals. The fixed effects included contemporaneous groups (year – season of weighed, sex and the covariate age of the cow (linear and quadratic. The B-Splines were defined in four knots through the growth period analyzed. Analyses were performed with the software Wombat. The variances (phenotypic and residual presented a similar behavior; of 7 to 12 months of age had a negative trend; from birth to 6 months and 13 to 18 months had positive trend; after 19 months were maintained constant. The m2 were low and near to zero, with an average of 0.06 in an interval of 0.04 to 0.11; the h2 also were close to zero, with an average of 0.10 in an interval of 0.03 to 0.23.

Kalman filter estimation of RLC parameters for UMP transmission line

Directory of Open Access Journals (Sweden)

Mohd Amin Siti Nur Aishah

2018-01-01

Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

Iterative importance sampling algorithms for parameter estimation

OpenAIRE

Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Pau, George Shu Heng; Finsterle, Stefan A.; Bell, John B.

2016-01-01

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is ...

Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly

Science.gov (United States)

Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.

2013-01-01

Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…

Parameter estimation in nonlinear models for pesticide degradation

International Nuclear Information System (INIS)

Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.

1991-01-01

A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)

Genetic parameters of wool colour and skin traits in Corriedale sheep

Directory of Open Access Journals (Sweden)

M.V. Benavides

2003-01-01

Full Text Available Clean wool colour (CWC is an important wool price determinant and has been related to suint characteristics, i.e. sudoriparous and sebaceous gland secretions, such as suint percentage and suint K content. In this work heritability, phenotypic and genetic correlations among wool colour and skin traits were examined. The genetic estimates were assessed by Restricted Maximum Likelihood (REML procedures using average information algorithm (AIREML in a Corriedale flock. The traits analysed were wool colour traits (CWC, yellow predictive colour (YPC, and Visual Score; suint traits such as suint percentage and potassium and sodium concentrations in suint, and physiological traits such as potassium and sodium concentrations in the skin, including plasma and red blood cells. The objectives of this study were to assess phenotypic and genetic correlations between wool colour and skin traits, and to find the suitability of these traits as indirect selection criteria for clean wool colour. Suint traits were highly genetically correlated to YPC. Suint K, but not suint percentage, was found to have a high genetic correlation with CWC. Skin K, Visual Score, YPC and suint K were amongst the best indirect selection criteria for clean wool colour. However, selection using these traits was expected to reduce CWC from 52% to 49% of that estimated under direct selection.

Improved double integral sliding mode MPPT controller based parameter estimation for a stand-alone photovoltaic system

International Nuclear Information System (INIS)

Chatrenour, Nasrin; Razmi, Hadi; Doagou-Mojarrad, Hasan

2017-01-01

Highlights: • IDISMC based MPPT algorithm is introduced. • Hurwitz stability theorem is used for switching surface coefficients computation. • GA approach is presented for parameter estimation of the stand-alone PV system. - Abstract: In this paper, an Improved Double Integral Sliding Mode MPPT Controller (IDISMC) for a stand-alone photovoltaic (PV) system is proposed. Performance of a sliding mode controller (SMC) is greatly influenced by the choice of the sliding surface. Switching surface coefficients were selected by the use of Hurwitz stability theorem. The IDISMC not only is robust against parametric and non-parametric uncertainties, but also has a very small steady-state error, thanks to the use of double integral of tracking voltage error in the definition of its sliding surface. For realistic simulation, Genetic Algorithm (GA) method was used to estimate parameters of solar panels model. The validity of the proposed double integral SMC in maximum power point tracking was approved by comparing the simulation results obtained for a sample PV system with the results of other methods.

Simple method for quick estimation of aquifer hydrogeological parameters

Science.gov (United States)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

Estimate of genetic diversity in cassutinga ( Croton heliotropiifolius ...

African Journals Online (AJOL)

Estimate of genetic diversity in cassutinga (Croton heliotropiifolius) based on molecular markers. Tallita de Oliveira Rocha, Janaina Silva Freitas, Elisa Susilene Lisboa dos Santos, Murilo Marques Scaldaferri, Claudine Gonçalves de Oliveira, Carlos Bernard Moreno Cerqueira-Silva ...

Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

DEFF Research Database (Denmark)

Fyhn, Karsten; Duarte, Marco F.; Jensen, Søren Holdt

2015-01-01

We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non...... to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super...... interpolation increases the estimation precision....

Estimation of Parameters in Mean-Reverting Stochastic Systems

Directory of Open Access Journals (Sweden)

Tianhai Tian

2014-01-01

Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.

Phenotype and genetic parameters for body measurements, reproductive traits and gut lenght of Nile tilapia (Oreochromis niloticus) selected for growth in low-input earthen ponds

NARCIS (Netherlands)

Charo-Karisa, H.; Bovenhuis, H.; Rezk, M.A.; Ponzoni, R.W.; Arendonk, van J.A.M.; Komen, J.

2007-01-01

In this study we present estimates of phenotypic and genetic parameters for body size measurements, reproductive traits, and gut length for Nile tilapia (Oreochromis niloticus) selected for growth in fertilized earthen ponds for two generations. Throughout the experiment, ponds were fertilized daily

Genetic parameters of rabbit semen traits and male fertilising ability.

Science.gov (United States)

Brun, J M; Sanchez, A; Ailloud, E; Saleil, G; Theau-Clément, M

2016-03-01

This study aimed to estimate genetic parameters for rabbit semen production, semen characteristics and fertilising ability following artificial insemination. It involved five successive batches of 30-36 bucks each, 22 weeks of semen collection, and 11 weeks of semen recording per batch. Semen analyses were based on 2312 ejaculates. A total of 2019 inseminations were performed on 674 females with semen from 236 ejaculates from 128 bucks. Heritability estimates of semen traits ranged from 0.05 to 0.18. At approximately 0.05-0.06 for pH, volume and mass motility, they were higher for concentration (0.10) and the total number of sperms per ejaculate (0.12), and even higher for motility traits based on computer-assisted semen analysis. The percentage of motile sperms had the highest heritability (0.18) and appeared to be a good candidate criterion to select for both sperm number and motility. The heritability estimates were close to zero for all three criteria of fertilising ability: fertility (F), prolificacy (live births, LB) and their product (LB per insemination). A permanent environmental effect of the male seemed to be higher for LB (0.04) than for F (0.01). The rabbit does accounted for approximately 10% of the variance of the three criteria. With respect to the female, the male contribution was negligible for fertility and in a ratio of 4-10 for the number of live births. In our experimental conditions, prolificacy would thus be more highly influenced by the buck than fertility. Copyright © 2016 Elsevier B.V. All rights reserved.

Statistical distributions applications and parameter estimates

CERN Document Server

Thomopoulos, Nick T

2017-01-01

This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability.  Understanding statistical distributions is fundamental for researchers in almost all disciplines.  The informed researcher will select the statistical distribution that best fits the data in the study at hand.  Some of the distributions are well known to the general researcher and are in use in a wide variety of ways.  Other useful distributions are less understood and are not in common use.  The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study.  The distributions are for continuous, discrete, and bivariate random variables.  In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values.  In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of ...

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

Directory of Open Access Journals (Sweden)

Guosheng Su

Full Text Available Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1 a simple additive genetic model (MA, 2 a model including both additive and additive by additive epistatic genetic effects (MAE, 3 a model including both additive and dominance genetic effects (MAD, and 4 a full model including all three genetic components (MAED. Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

The Genetics of Pork Quality

NARCIS (Netherlands)

Wijk, van H.J.

2006-01-01

This thesis describes the genetics of carcass composition and pork quality traits. A large population of commercial finishers was extensively phenotyped for growth, carcass composition and meat quality traits. Genetic parameters were estimated based on those measurements. The population was

Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

International Nuclear Information System (INIS)

Yamamoto, Akio; Yasue, Yoshihiro; Endo, Tomohiro; Kodama, Yasuhiro; Ohoka, Yasunori; Tatsumi, Masahiro

2013-01-01

An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. (author)

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

Mechhoud, Sarra

2016-01-01

In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.

Robust estimation of hydrological model parameters

Directory of Open Access Journals (Sweden)

A. Bárdossy

2008-11-01

Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

Targeted estimation of nuisance parameters to obtain valid statistical inference.

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van der Laan, Mark J

2014-01-01

In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special

Genetic parameters for quail body weights using a random ...

African Journals Online (AJOL)

A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option.

minimum variance estimation of yield parameters of rubber tree

African Journals Online (AJOL)

2013-03-01

Mar 1, 2013 ... It is our opinion that Kalman filter is a robust estimator of the ... Kalman filter, parameter estimation, rubber clones, Chow failure test, autocorrelation, STAMP, data ...... Mills, T.C. Modelling Current Temperature Trends.

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

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Le Zuo

2018-04-01

Full Text Available This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D direction of arrival (DOA and signal sorting, with a low-cost circular synthetic array (CSA consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step and the maximization (M-step. In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations.

Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.

Science.gov (United States)

Rigby, Robert A; Stasinopoulos, Dimitrios M

2014-08-01

A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Estimation of G-renewal process parameters as an ill-posed inverse problem

International Nuclear Information System (INIS)

Krivtsov, V.; Yevkin, O.

2013-01-01

Statistical estimation of G-renewal process parameters is an important estimation problem, which has been considered by many authors. We view this problem from the standpoint of a mathematically ill-posed, inverse problem (the solution is not unique and/or is sensitive to statistical error) and propose a regularization approach specifically suited to the G-renewal process. Regardless of the estimation method, the respective objective function usually involves parameters of the underlying life-time distribution and simultaneously the restoration parameter. In this paper, we propose to regularize the problem by decoupling the estimation of the aforementioned parameters. Using a simulation study, we show that the resulting estimation/extrapolation accuracy of the proposed method is considerably higher than that of the existing methods

Joint Multi-Fiber NODDI Parameter Estimation and Tractography using the Unscented Information Filter

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Yogesh eRathi

2016-04-01

Full Text Available Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF. Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters, which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

Estimation of octanol/water partition coefficients using LSER parameters

Science.gov (United States)

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

MCMC for parameters estimation by bayesian approach

International Nuclear Information System (INIS)

Ait Saadi, H.; Ykhlef, F.; Guessoum, A.

2011-01-01

This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

Estimating model parameters in nonautonomous chaotic systems using synchronization

International Nuclear Information System (INIS)

Yang, Xiaoli; Xu, Wei; Sun, Zhongkui

2007-01-01

In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

Mechhoud, Sarra

2016-01-01

First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.

Parameter estimation and determinability analysis applied to Drosophila gap gene circuits

Directory of Open Access Journals (Sweden)

Jaeger Johannes

2008-09-01

Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.

Estimation of delays and other parameters in nonlinear functional differential equations

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Banks, H. T.; Lamm, P. K. D.

1983-01-01

A spline-based approximation scheme for nonlinear nonautonomous delay differential equations is discussed. Convergence results (using dissipative type estimates on the underlying nonlinear operators) are given in the context of parameter estimation problems which include estimation of multiple delays and initial data as well as the usual coefficient-type parameters. A brief summary of some of the related numerical findings is also given.

Nonparametric estimation of location and scale parameters

KAUST Repository

Potgieter, C.J.

2012-12-01

Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.

Bayesian estimation of parameters in a regional hydrological model

Directory of Open Access Journals (Sweden)

K. Engeland

2002-01-01

Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

Estimation of common cause failure parameters with periodic tests

Energy Technology Data Exchange (ETDEWEB)

Barros, Anne [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France)], E-mail: anne.barros@utt.fr; Grall, Antoine [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France); Vasseur, Dominique [Electricite de France, EDF R and D - Industrial Risk Management Department 1, av. du General de Gaulle- 92141 Clamart (France)

2009-04-15

In the specific case of safety systems, CCF parameters estimators for standby components depend on the periodic test schemes. Classically, the testing schemes are either staggered (alternation of tests on redundant components) or non-staggered (all components are tested at the same time). In reality, periodic tests schemes performed on safety components are more complex and combine staggered tests, when the plant is in operation, to non-staggered tests during maintenance and refueling outage periods of the installation. Moreover, the CCF parameters estimators described in the US literature are derived in a consistent way with US Technical Specifications constraints that do not apply on the French Nuclear Power Plants for staggered tests on standby components. Given these issues, the evaluation of CCF parameters from the operating feedback data available within EDF implies the development of methodologies that integrate the testing schemes specificities. This paper aims to formally propose a solution for the estimation of CCF parameters given two distinct difficulties respectively related to a mixed testing scheme and to the consistency with EDF's specific practices inducing systematic non-simultaneity of the observed failures in a staggered testing scheme.

Application of genetic algorithm for extraction of the parameters from powder EPR spectra

International Nuclear Information System (INIS)

Spalek, T.; Pietrzyk, P.; Sojka, Z.

2005-01-01

The application of the stochastic genetic algorithm in tandem with the deterministic Powell method to automated extraction of the magnetic parameters from powder EPR spectra was described. The efficiency and robustness of such hybrid approach were investigated as a function of the uncertainty range of parameters, using simulated data sets. The discussed results demonstrate superior performance of the hybrid genetic algorithm in fitting of complex spectra in comparison to the common Monte Carlo Method joint with the Powell refinement. (author)

Consistent Parameter and Transfer Function Estimation using Context Free Grammars

Science.gov (United States)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

Science.gov (United States)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Parameter and state estimation in nonlinear dynamical systems

Science.gov (United States)

Creveling, Daniel R.

This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling

Parameter estimation for lithium ion batteries

Science.gov (United States)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of

Nonlinear systems time-varying parameter estimation: Application to induction motors

Energy Technology Data Exchange (ETDEWEB)

Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)

2008-11-15

In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)

Genetic parameters for milk yield, age at first calving and interval between first and second calving in milk buffaloes

Directory of Open Access Journals (Sweden)

R.R. Aspilcueta Borquis

2010-02-01

Full Text Available Genetic parameters for the relation between the traits of milk yield (MY, age at first calving (AFC and interval between first and second calving (IBFSC were estimated in milk buffaloes of the Murrah breed. In the study, data of 1578 buffaloes at first lactation, with calvings from 1974 to 2006 were analyzed. The MTDFREML system was used in the analyses with models for the MY, IBFSC traits which included the fixed effects of herd-year-season of calving, linear and quadratic terms of calving age as covariate and the random animal effects and error. The model for AFC consisted of the herd-year-season fixed effects of calving and the random effects of animal and error. Heritability estimates MY, AFC and IBFSC traits were 0.20, 0.07 and 0.14, respectively. Genetic and phenotypic correlations between the traits were: MY and AFC = -0.12 and -0.15, MY and IBFSC = 0.07 and 0.30, AFC and IBFSC = 0.35 and 0.37, respectively. Genetic correlation between MY and AFC traits showed desirable negative association, suggesting that the daughters of the bulls with high breeding value for MY could be physiological maturity to a precocious age. Genetic correlation between MY and IBFSC showed that the selection of the animals that increased milk yield is also those that tend to intervals of bigger calving.

Accuracy and sensitivity analysis on seismic anisotropy parameter estimation

Science.gov (United States)

Yan, Fuyong; Han, De-Hua

2018-04-01

There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.

Estimation of Compaction Parameters Based on Soil Classification

Science.gov (United States)

Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.

2018-02-01

Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.

Response to family selection and genetic parameters in Japanese quail selected for four week breast weight

DEFF Research Database (Denmark)

Khaldari, Majid; Yeganeh, Hassan Mehrabani; Pakdel, Abbas

2011-01-01

An experiment was conducted to investigate the effect of short-term selection for 4 week breast weight (4wk BRW), and to estimate genetic parameters of body weight, and carcass traits. A selection (S) line and control (C) line was randomly selected from a base population. Data were collected over...... was 0.35±0.06. There were a significant difference for BW, and carcass weights but not for carcass percent components between lines (Pcarcass and leg weights were 0.46, 0.41 and 0.47, and 13.2, 16.2, 4.4 %, respectively....... The genetic correlations of BRW with BW, carcass, leg, and back weights were 0.85, 0.88 and 0.72, respectively. Selection for 4 wk BRW improved feed conversion ratio (FCR) about 0.19 units over the selection period. Inbreeding caused an insignificant decline of the mean of some traits. Results from...

How and how much does RAD-seq bias genetic diversity estimates?

Science.gov (United States)

Cariou, Marie; Duret, Laurent; Charlat, Sylvain

2016-11-08

RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to "ideal" empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where

Pose estimation for augmented reality applications using genetic algorithm.

Science.gov (United States)

Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

2005-12-01

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

sGD: software for estimating spatially explicit indices of genetic diversity.

Science.gov (United States)

Shirk, A J; Cushman, S A

2011-09-01

Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans. © 2011 Blackwell Publishing Ltd.

Probabilistic estimation of the constitutive parameters of polymers

Directory of Open Access Journals (Sweden)

Siviour C.R.

2012-08-01

Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.

Short communication: Genetic parameters for post-weaning visual scores and reproductive traits in Suffolk sheep

Directory of Open Access Journals (Sweden)

Juliana V. Portes

2018-04-01

Full Text Available The aim of this study was to estimate the coefficients of heritability and genetic correlations among visual scores (conformation, CPW; precocity, PPW; musculature, MPW and reproductive traits: age at first lambing (AFL and scrotal circumference (SC evaluated at 180 days of age in Suffolk lambs. In the statistical model only the additive genetic effect was considered as random effect. The heritability estimates by univariate analyses for CPW, PPW, MPW, AFL and SC were 0.08, 0.12, 0.09, 0.20 and 0.22, respectively. The genetic correlations among AFL and CPW, PPW, MPW were -0.26, 0.19, and 0.08, respectively. The genetic correlation among SC and CPW, PPW, MPW were, respectively, 0.54, 0.88 and 0.86, and between AFL and SC was 0.26. The direct selection for conformation, precocity and musculature at 180 days of age and age at first lambing will provide slow genetic progress due to low heritability estimates. It is possible to obtain genetic gain in sexual precocity through selection on scrotal circumference in Suffolk rams. The favorable genetic correlation among visual scores and SC and between CPW and AFL, indicated the possibility to gain in genetic progress for reproductive traits through indirect selection of the visual scores in Suffolk sheep.

Estimating 3D Object Parameters from 2D Grey-Level Images

NARCIS (Netherlands)

Houkes, Z.

2000-01-01