Recovery of the complete data set from ultrasound sequences with arbitrary transmit delays

2017 ◽  
Vol 142 (4) ◽  
pp. 2696-2696
Author(s):  
Nick Bottenus
Keyword(s):  
Complete Data ◽  
Data Set

scholarly journals Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 708
Author(s):  
Moran Gershoni ◽  
Joel Ira Weller ◽  
Ephraim Ezra
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Variance Components ◽  
Complete Data ◽  
Variance Component Analysis ◽  
Coefficient Of Determination ◽  
Birth Date ◽  
Analysis Model ◽  
Data Set ◽  
Genetic Evaluations

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight − 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was −0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (pnominal < 1 × 10−8). Considering all SNPs simultaneously, 0.69 of variance among the sires’ transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13–14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

Incomplete Big Data Distributed Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1496-1499
Author(s):  
Yong Lin Leng
Keyword(s):  
Big Data ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Design Method ◽  
Complete Data ◽  
Similarity Metrics ◽  
Distributed Clustering ◽  
Computing Technology ◽  
Data Set ◽  
Affinity Propagation Clustering

Partially missing or blurring attribute values make data become incomplete during collecting data. Generally we use inputation or discarding method to deal with incomplete data before clustering. In this paper we proposed an a new similarity metrics algorithm based on incomplete information system. First algorithm divided the data set into a complete data set and non complete data set, and then the complete data set was clustered using the affinity propagation clustering algorithm, incomplete data according to the design method of the similarity metric is divided into the corresponding cluster. In order to improve the efficiency of the algorithm, designing the distributed clustering algorithm based on cloud computing technology. Experiment demonstrates the proposed algorithm can cluster the incomplete big data directly and improve the accuracy and effectively.

EMPIRICAL FUZZY ESTIMATE OF THE SURVIVAL CURVE

2004 ◽  
Vol 12 (03) ◽  
pp. 347-356
Author(s):  
KIAN POKORNY ◽  
DILEEP SULE
Keyword(s):  
Expert Knowledge ◽  
Survival Curve ◽  
Complete Data ◽  
Point Estimate ◽  
Data Set ◽  
Fuzzy Membership Functions ◽  
Product Limit Estimator ◽  
Product Limit ◽  
Few Data ◽  
Fuzzy Estimate

In this paper, a computational system is developed that estimates a survival curve and a point estimate when very few data are available and a high proportion of the data are censored. Standard statistical methods require a more complete data set. With any less data expert knowledge or heuristic methods are required. The system uses numerical methods to define fuzzy membership functions about each data point that quantify uncertainty due to censoring. The "fuzzy" data is then used to estimate a survival curve and the mean survival time is calculated from the curve. The new estimator converges to the Product-Limit estimator when a complete data set is available. In addition, this method allows for the incorporation of expert knowledge. Finally, simulation results are provided to demonstrate the performance of the new method and its improvement over the Product-Limit estimator.

Hydraulic Model Calibration of a Nuclear Plant Service Water System

2016 ◽  
Author(s):  
Erin A. Onat ◽  
Trey W. Walters ◽  
David M. Mobley ◽  
James J. Mead
Keyword(s):  
Water System ◽  
Complete Data ◽  
Second Phase ◽  
Data Set ◽  
Flow Rates ◽  
Pressure Drops ◽  
Calibration Process ◽  
Partial Data ◽  
Service Water ◽  
Data Calibration

As pipe networks age, build-up [scaling] and corrosion decrease pipe diameter and increase pipe roughness, leading to significant pressure drops and lower flow rates. When modeling the hydraulics of these systems, calibrating the pipes to account for additional scaling and/or fouling can be vital to accurately predicting the hydraulic behavior of the system. An automated, multi-variable goal-seeking software was used to calibrate the raw water system of the Duke McGuire Nuclear Station (MNS). This calibration process involved three phases. The first phase was the testing of the automated, multivariable goal-seeking software on a previously calibrated system. The second phase was the calibration of a partial data set. The third phase was the calibration of a complete data set. The automated goal-seeking software was found to have varying degrees of success in each phase. At the conclusion of the calibration process, the partial data calibration of two parallel systems at MNS yielded average overall calibration accuracies of 2.1% and 1% for flow rates, and 1.2 psig (8.4 kPa-g) and 1.7 psig (11.9 kPa-g) for pressures. The complete data calibration of one of these systems at MNS yielded an average overall calibration accuracy of 2.3% for flow rates, and 1.4 psig (9.5 kPa-g) for pressures.

SCC In-Field Inspection Technology Evaluation and its Application to EMAT Based Pipeline Integrity Management Programs

2014 ◽  
Author(s):  
Samarth Tandon ◽  
Ming Gao ◽  
Ravi Krishnamurthy ◽  
Richard Kania ◽  
Mark Piazza
Keyword(s):  
Failure Analysis ◽  
Extensive Study ◽  
Complete Data ◽  
Technology Evaluation ◽  
Data Set ◽  
Field Inspection ◽  
Fracture Surfaces ◽  
Comprehensive View ◽  
Integrity Management

Most recently, as a complement to the ongoing efforts to monitor and document improvements in EMAT ILI technology, PRCI conducted an extensive study of NDE inspection technologies for characterizing SCC in pipelines using various in-ditch technologies and methods. The test pipes used for the study were cut outs from an operating pipeline where SCC features were identified and sized using EMAT ILI technologies. These are now sized with the NDE study and correlated with EMAT data to support an improvement of EMAT technology in characterizing SCC features. More importantly, the test pipes were burst tested to failure, with post failure analysis completed to fully characterize the crack features, including detailed length and depth measurements. This complete data set provides a comprehensive view of the current capabilities of NDE inspection technologies and EMAT ILI technologies to detect and characterize SCC and crack-like features. In this paper, the approach used for the evaluation of in-ditch NDE and EMAT ILI technologies is presented first. The in-ditch NDE technologies used for evaluation which were commonly used for SCC characterization are then described. SCC characterization results from in-ditch NDE and EMAT ILI are summarized and compared to those directly measured from fracture surfaces exposed by burst tests. The findings and its application to pipeline integrity management programs are discussed.

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