Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as th...Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.展开更多
This paper aims at finding a proper way to estimate sloshing severity.First,the concept of sloshing severity RAO(SSR) is introduced,and the wave elevation on the liquid free surface is chosen as an initial index for t...This paper aims at finding a proper way to estimate sloshing severity.First,the concept of sloshing severity RAO(SSR) is introduced,and the wave elevation on the liquid free surface is chosen as an initial index for the rough prediction of sloshing severity.Then,compared with experimental data from a 3 D regular model test,this index is adjusted and a new index is generated.One step further,sloshing severity under irregular sea states can be achieved by nonlinear combinations of the new index.For validation,the same model tank is tested under a set of irregular sea conditions,and peak pressures and impulse areas are taken as comparison standards.It is found that both numerical and experimental results show a similar tendency of sloshing severity.As a real ship application on the new index,the sloshing severity of a liquefied natural gas floating production storage and offloading(LNG-FPSO) is predicted under a low filling condition.Besides,the ship motion responses with and without sloshing effects are considered for the calculation of severity.From the present observation,this proposed methodology and generated new index is expected to be applicable to the selection of severe sea states for sloshing loads analysis.展开更多
In materials science,data-driven methods accelerate material discovery and optimization while reducing costs and improving success rates.Symbolic regression is a key to extracting material descriptors from large datas...In materials science,data-driven methods accelerate material discovery and optimization while reducing costs and improving success rates.Symbolic regression is a key to extracting material descriptors from large datasets,in particular the Sure Independence Screening and Sparsifying Operator(SISSO)method.While SISSO needs to store the entire expression space to impose heavy memory demands,it limits the performance in complex problems.To address this issue,we propose a RF-SISSO algorithm by combining Random Forests(RF)with SISSO.In this algorithm,the Random Forests algorithm is used for prescreening,capturing non-linear relationships and improving feature selection,which may enhance the quality of the input data and boost the accuracy and efficiency on regression and classification tasks.For a testing on the SISSO’s verification problem for 299 materials,RF-SISSO demonstrates its robust performance and high accuracy.RF-SISSO can maintain the testing accuracy above 0.9 across all four training sample sizes and significantly enhancing regression efficiency,especially in training subsets with smaller sample sizes.For the training subset with 45 samples,the efficiency of RF-SISSO was 265 times higher than that of original SISSO.As collecting large datasets would be both costly and time-consuming in the practical experiments,it is thus believed that RF-SISSO may benefit scientific researches by offering a high predicting accuracy with limited data efficiently.展开更多
CO2 geological sequestration in a depleted shale gas reservoir is a promising method to address the global energy crisis as well as to reduce greenhouse gas emissions. Though improvements have been achieved by many re...CO2 geological sequestration in a depleted shale gas reservoir is a promising method to address the global energy crisis as well as to reduce greenhouse gas emissions. Though improvements have been achieved by many researchers, the carbon sequestration and enhanced gas recovery(CS-EGR) in shale formations is still in a preliminary stage. The current research status of CO2 sequestration in shale gas reservoirs with potential EGR is systematically and critically addressed in the paper. In addition, some original findings are also presented in this paper. This paper will shed light on the technology development that addresses the dual problem of energy crisis and environmental degradation.展开更多
基金This research was supported by the National Natural Science Foundations of China(31872975)the Science and Technology Project of Inner Mongolia Autonomous Region,China(2020GG0210)the Program of National Beef Cattle and Yak Industrial Technology System,China(CARS-37).
文摘Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.
文摘This paper aims at finding a proper way to estimate sloshing severity.First,the concept of sloshing severity RAO(SSR) is introduced,and the wave elevation on the liquid free surface is chosen as an initial index for the rough prediction of sloshing severity.Then,compared with experimental data from a 3 D regular model test,this index is adjusted and a new index is generated.One step further,sloshing severity under irregular sea states can be achieved by nonlinear combinations of the new index.For validation,the same model tank is tested under a set of irregular sea conditions,and peak pressures and impulse areas are taken as comparison standards.It is found that both numerical and experimental results show a similar tendency of sloshing severity.As a real ship application on the new index,the sloshing severity of a liquefied natural gas floating production storage and offloading(LNG-FPSO) is predicted under a low filling condition.Besides,the ship motion responses with and without sloshing effects are considered for the calculation of severity.From the present observation,this proposed methodology and generated new index is expected to be applicable to the selection of severe sea states for sloshing loads analysis.
基金supported by the National Natural Science Foundation of China(Nos.21933006 and 21773124)the Fundamental Research Funds for the Central Universities of Nankai University(Nos.63243091 and 63233001)the Supercomputing Center of Nankai University(NKSC).
文摘In materials science,data-driven methods accelerate material discovery and optimization while reducing costs and improving success rates.Symbolic regression is a key to extracting material descriptors from large datasets,in particular the Sure Independence Screening and Sparsifying Operator(SISSO)method.While SISSO needs to store the entire expression space to impose heavy memory demands,it limits the performance in complex problems.To address this issue,we propose a RF-SISSO algorithm by combining Random Forests(RF)with SISSO.In this algorithm,the Random Forests algorithm is used for prescreening,capturing non-linear relationships and improving feature selection,which may enhance the quality of the input data and boost the accuracy and efficiency on regression and classification tasks.For a testing on the SISSO’s verification problem for 299 materials,RF-SISSO demonstrates its robust performance and high accuracy.RF-SISSO can maintain the testing accuracy above 0.9 across all four training sample sizes and significantly enhancing regression efficiency,especially in training subsets with smaller sample sizes.For the training subset with 45 samples,the efficiency of RF-SISSO was 265 times higher than that of original SISSO.As collecting large datasets would be both costly and time-consuming in the practical experiments,it is thus believed that RF-SISSO may benefit scientific researches by offering a high predicting accuracy with limited data efficiently.
基金supported by the General Project of National Natural Science Foundation of China (Grant Nos. 51974253 and 51974247)the Youth Project of National Natural Science Foundation of China (Grant No.41502311)+1 种基金the Natural Science Foundation of Shaanxi Province (Grant No.2019JQ-525)the Natural Science Basic Research Program of Shaanxi Province (Grant No. 2020JQ-781)。
文摘CO2 geological sequestration in a depleted shale gas reservoir is a promising method to address the global energy crisis as well as to reduce greenhouse gas emissions. Though improvements have been achieved by many researchers, the carbon sequestration and enhanced gas recovery(CS-EGR) in shale formations is still in a preliminary stage. The current research status of CO2 sequestration in shale gas reservoirs with potential EGR is systematically and critically addressed in the paper. In addition, some original findings are also presented in this paper. This paper will shed light on the technology development that addresses the dual problem of energy crisis and environmental degradation.
