The Experts below are selected from a list of 113082 Experts worldwide ranked by ideXlab platform
Zhang Hongta - One of the best experts on this subject based on the ideXlab platform.
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study on multiple Regression Equation for calculating segmental move inertia of segments of chinese young male bodies
Journal of Medical Biomechanics, 2005Co-Authors: Zhang HongtaAbstract:Objective To set up the multiple Regression Equation that could immediately calculate the segmental move inertia of the Chinese young male bodies by determining the data of move inertia of the segments and the mathematical model. Methods By using the CT-DIP method in this project , to carry out the determination for 50 Chinese young male living subjects (18～23 year-old) on their posture parameters , the scanning , image analysis , calculation , and progressive Regression so that to choose and define the important variable quantities. Results A series of the data on move inertia of the segments were obtained and set up the mathematical model consisted of 15-segments. The multiple Regression Equation was then established which could immediately calculate the move inertia of the segments of the Chinese young male bodies, in which the body weight , the stature , and the segmental length , the wide, and the contour were usedas independent variable quantity. Conclusion This study provided the dependable means to calculate immediately the move inertia of the segments of the Chinese young male bodies .
Heejong Koh - One of the best experts on this subject based on the ideXlab platform.
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influence of multi gene allele combinations on grain size of rice and development of a Regression Equation model to predict grain parameters
Rice, 2015Co-Authors: Chanmi Lee, Jonghwa Park, Backki Kim, Jeonghwan Seo, Gileung Lee, Su Jang, Heejong KohAbstract:Grain size is one of the key factors determining yield and quality in rice. A large number of genes are involved in the regulation of grain size parameters such as grain length and grain width. Different alleles of these genes have different impacts on the grain size traits under their control. However, the combined influence of multiple alleles of different genes on grain size remains to be investigated. Six key genes known to influence grain size were investigated in this study: GS3, GS5, GS6, GW2, qSW5/GW5, and GW8/OsSPL16. Allele and grain measurement data were used to develop a Regression Equation model that can be used for molecular breeding of rice with desired grain characteristics. A total of 215 diverse rice germplasms, which originated from or were developed in 28 rice-consuming countries, were used in this study. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and that these allele combinations were significantly associated with differences in grain size. Furthermore, in several cases, variation at a single gene was sufficient to influence grain size, even when the alleles of other genes remained constant. The data were used to develop a Regression Equation model for prediction of rice grain size, and this was tested using data from a further 34 germplasms. The model was significantly correlated with three of the four grain size-related traits examined in this study. Rice grain size is strongly influenced by specific combinations of alleles from six different genes. A Regression Equation model developed from allele and grain measurement data can be used in rice breeding programs for the development of new rice varieties with desired grain size and shape.
Li Siqi - One of the best experts on this subject based on the ideXlab platform.
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Open Access Research on PDC bit Drilling Rate Equation in Daqing Medium-Deep Well Based on Rock Breaking Experiments by PDC Bit
2016Co-Authors: Li Wei, Xu Xinghua, Yan Tie, Li SiqiAbstract:Abstract: PDC bit drilling rate Equation is one measuring criterion of PDC bit work efficiency. The reasonable PDC bit drilling rate Equation could predict the penetration rate and provide guidance for field operation. This paper studied the in-fluences of the parameters on PDC bit drilling rate, such as rock drillability, cutting teeth diameter, specific weight on bit and rotate speed, and regressed the relation Equations between the above parameters and drilling rate for cement rock, white sandstone, yellow sandstone, red sandstone and granite based on the laboratory rock breaking experiments. The re-sults showed that the Regression Equation between specific weight on bit and drilling rate is quadratic polynomial for the soft and intermediate hardness rock, such as cement rock, yellow sandstone and white sandstone. The Regression Equation is quartic polynomial, the Regression Equation between rotate speed and drilling rate is quadratic polynomial for the inter-mediate hardness rock, such as red sandstone. The field data verification results of Daqing Oilfield medium-deep well showed the fractional error in actual drilling speed and forecast drilling speed between 3.03 % and 9.23 % and the average error is 6.397%. This explained that the modified PDC bit drilling rate Equation could describe the drilling law preferably
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research on pdc bit drilling rate Equation in daqing medium deepwell based on rock breaking experiments by pdc bit
The Open Petroleum Engineering Journal, 2015Co-Authors: Xu Xinghua, Li SiqiAbstract:PDC bit drilling rate Equation is one measuring criterion of PDC bit work efficiency. The reasonable PDC bit drilling rate Equation could predict the penetration rate and provide guidance for field operation. This paper studied the in- fluences of the parameters on PDC bit drilling rate, such as rock drillability, cutting teeth diameter, specific weight on bit and rotate speed, and regressed the relation Equations between the above parameters and drilling rate for cement rock, white sandstone, yellow sandstone, red sandstone and granite based on the laboratory rock breaking experiments. The re- sults showed that the Regression Equation between specific weight on bit and drilling rate is quadratic polynomial for the soft and intermediate hardness rock, such as cement rock, yellow sandstone and white sandstone. The Regression Equation is quartic polynomial, the Regression Equation between rotate speed and drilling rate is quadratic polynomial for the inter- mediate hardness rock, such as red sandstone. The field data verification results of Daqing Oilfield medium-deep well showed the fractional error in actual drilling speed and forecast drilling speed between 3.03% and 9.23% and the average error is 6.397%. This explained that the modified PDC bit drilling rate Equation could describe the drilling law preferably.
Tong Sun - One of the best experts on this subject based on the ideXlab platform.
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effect of multi allele combination on rice grain size based on prediction of Regression Equation model
Molecular Genetics and Genomics, 2020Co-Authors: Hua Zhong, Chang Liu, Weilong Kong, Yue Zhang, Gangqing Zhao, Tong SunAbstract:Rice yield potential is partially affected by grain size and weight, which associates with a great number of genes and QTLs. However, it is still unclear that how multiple alleles in different genes take a combined effect on grain shape/size. Here, we investigated seven core grain size-related functional genes (GL7, GS3, GW8, GS5, TGW6, WTG1, and An-1) and observed a wide phenotypic variation for five agronomic traits (grain length, grain width, grain length–width ratio, grain thickness and thousand-grain weight) in 521 rice germplasm. The correlation analysis showed a strong association among these grain traits which have distinct impacts on determining the final rice grain size. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and these allele combinations were significantly associated with differences in grain size. Furthermore, alleles were regarded as individual variables to develop the multiple Regression Equation. We found that B and C allelic types of GS3 and conventional type of WTG1 played relevant roles in grain size and thousand-grain weight, separately. The models would conduce to devise instructive approaches by selecting appropriate candidate alleles, which could fuel further research for breeding preferred grain shape and high-yielding crop.
Chanmi Lee - One of the best experts on this subject based on the ideXlab platform.
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influence of multi gene allele combinations on grain size of rice and development of a Regression Equation model to predict grain parameters
Rice, 2015Co-Authors: Chanmi Lee, Jonghwa Park, Backki Kim, Jeonghwan Seo, Gileung Lee, Su Jang, Heejong KohAbstract:Grain size is one of the key factors determining yield and quality in rice. A large number of genes are involved in the regulation of grain size parameters such as grain length and grain width. Different alleles of these genes have different impacts on the grain size traits under their control. However, the combined influence of multiple alleles of different genes on grain size remains to be investigated. Six key genes known to influence grain size were investigated in this study: GS3, GS5, GS6, GW2, qSW5/GW5, and GW8/OsSPL16. Allele and grain measurement data were used to develop a Regression Equation model that can be used for molecular breeding of rice with desired grain characteristics. A total of 215 diverse rice germplasms, which originated from or were developed in 28 rice-consuming countries, were used in this study. Genotyping analysis demonstrated that a relatively small number of allele combinations were preserved in the diverse population and that these allele combinations were significantly associated with differences in grain size. Furthermore, in several cases, variation at a single gene was sufficient to influence grain size, even when the alleles of other genes remained constant. The data were used to develop a Regression Equation model for prediction of rice grain size, and this was tested using data from a further 34 germplasms. The model was significantly correlated with three of the four grain size-related traits examined in this study. Rice grain size is strongly influenced by specific combinations of alleles from six different genes. A Regression Equation model developed from allele and grain measurement data can be used in rice breeding programs for the development of new rice varieties with desired grain size and shape.
