Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective ...Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.展开更多
A problem in chemical analysis in connection with measurements of a substance normally occurring in a sample, or identification of a substance which should not exist in a sample, is insufficient selectivity. In this a...A problem in chemical analysis in connection with measurements of a substance normally occurring in a sample, or identification of a substance which should not exist in a sample, is insufficient selectivity. In this article, we analyze this problem and propose remedies. We use a real doping case to illustrate how chemical noise causes a serious selectivity problem, probably causing a false positive outcome.展开更多
Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance pr...Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.展开更多
Sensitive and fast detection of ibuprofen( Ibu) in an aquatic environment often requires costly, time-consuming and sophisticated techniques. To tackle those limitations,a novel android smartphone application was deve...Sensitive and fast detection of ibuprofen( Ibu) in an aquatic environment often requires costly, time-consuming and sophisticated techniques. To tackle those limitations,a novel android smartphone application was developed based on a colorimetric analysis method using unmodified gold nanoparticles( AuNPs)aptamer probes to quantitatively detect Ibu. Under optimal conditions,it could detect Ibu as low as 0. 25 ng/mL with high selectivity. The determination of Ibu in real water samples was also carried out to confirm the practicability of the application.展开更多
文摘Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.
文摘A problem in chemical analysis in connection with measurements of a substance normally occurring in a sample, or identification of a substance which should not exist in a sample, is insufficient selectivity. In this article, we analyze this problem and propose remedies. We use a real doping case to illustrate how chemical noise causes a serious selectivity problem, probably causing a false positive outcome.
文摘Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus（QTL） mapping, and marker-assisted selection（MAS） are evolving into more efficient concepts of linkage disequilibrium（LD） also called association mapping and genomic selection（GS）, respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice.
基金National Natural Science Foundation of China(No.21377023)
文摘Sensitive and fast detection of ibuprofen( Ibu) in an aquatic environment often requires costly, time-consuming and sophisticated techniques. To tackle those limitations,a novel android smartphone application was developed based on a colorimetric analysis method using unmodified gold nanoparticles( AuNPs)aptamer probes to quantitatively detect Ibu. Under optimal conditions,it could detect Ibu as low as 0. 25 ng/mL with high selectivity. The determination of Ibu in real water samples was also carried out to confirm the practicability of the application.
