K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics.We can use k-mer vector as a representation method of the k-mer ...K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics.We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence.Problems,such as similarity calculations or sequence assembly,can be described in the k-mer vector space.It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory.In this study,we defined the k-mer vector space for the generalized biological sequences.The meaning of corresponding vector operations is explained in the biological context.We presented the vector/matrix form of several widely seen sequence-based problems,including read quantification,sequence assembly,and pattern detection problem.Its advantages and disadvantages are discussed.Also,we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods.It shows the practicability and convenience of this algorithm design strategy.展开更多
The inter-sequence analysis method is a popular decision making method considering subjective information of the decision maker.The validity of this method is sensitive to the subjective information provided by the de...The inter-sequence analysis method is a popular decision making method considering subjective information of the decision maker.The validity of this method is sensitive to the subjective information provided by the decision maker significantly.Thus,a new weighting method based on the inter-sequence analysis method is proposed.This new method can appropriately reduce the participation of the subjective judgment of the decision maker,and then accordingly decrease the influence of the subjective judgment to the results.It only needs the decision maker to give the inter sequence of the criteria.The importance comparison of two criteria is objectively determined by considering the ratio of a criterion being superior to another one.Some numerical examples are given to illustrate the calculation process of the new method.展开更多
The study on ^(13)C-NMR spectra of aliphatic carbon region of emuision-processed and solution-processed (by lithium catalyst) SBR was carried out. The assignments for more than thirty odd peaks observed experimentally...The study on ^(13)C-NMR spectra of aliphatic carbon region of emuision-processed and solution-processed (by lithium catalyst) SBR was carried out. The assignments for more than thirty odd peaks observed experimentally were made by using 'corresponding analysis' method, combined with the empirical parameters reported in literature. The peak intensifies were calculated based on BemouUian statistic assumption.展开更多
目的通过三种假丝酵母菌鉴定方法的比较,为临床选择假丝酵母菌的鉴定方法提供参考。方法用科玛嘉显色培养基、API 20C AUX生化系统和rRNA-ITS序列测定分析鉴定住院患者口腔培养出的198株假丝酵母菌,比较3种鉴定方法的结果。结果 API 20C...目的通过三种假丝酵母菌鉴定方法的比较,为临床选择假丝酵母菌的鉴定方法提供参考。方法用科玛嘉显色培养基、API 20C AUX生化系统和rRNA-ITS序列测定分析鉴定住院患者口腔培养出的198株假丝酵母菌,比较3种鉴定方法的结果。结果 API 20C AUX生化系统与rDNA-ITS序列测定分析鉴定结果一致,科玛嘉显色培养基与API 20C AUX生化系统和rDNA-ITS序列测定分析鉴定的符合率白假丝酵母菌为97.84%,热带假丝酵母菌的符合率为93.33%,光滑假丝酵母菌和克柔假丝酵母菌的符合率分别为90.91%、88.89%,其他假丝酵母菌科玛嘉不能鉴定。结论 API 20C AUX生化系统与rDNA-ITS序列测定分析鉴定假丝酵母菌的结果有较好的一致性,rDNA-ITS序列测定分析鉴定假丝酵母菌有望成为假丝酵母菌鉴定较好的方法。科玛嘉显色培养基方法可以简便有效的鉴定最常见的4种假丝酵母菌。展开更多
Results from optical CCD photometric observations of 13 pre-main-sequence stars collected during the period from February 2007 to November 2020 are presented.These stars are located in the association Cepheus OB3,in t...Results from optical CCD photometric observations of 13 pre-main-sequence stars collected during the period from February 2007 to November 2020 are presented.These stars are located in the association Cepheus OB3,in the field of the young star V733 Cephei.Photometric observations,especially concerning the long-term variability of the stars,are missing in the literature.We present the first longterm V(RI)c monitoring for them,that cover 13 years.Results from our study indicate that all of the investigated stars manifest strong photometric variability.The presented paper is a part of our program for the photometric study of pre-main-sequence stars located in active star-forming regions.展开更多
The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However...The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.展开更多
基金the National Natural Science Foundation of China(11771393,11632015)the Natural Sci-ence Foundation of Zhejiang Province,China(LZ14A010002).
文摘K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics.We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence.Problems,such as similarity calculations or sequence assembly,can be described in the k-mer vector space.It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory.In this study,we defined the k-mer vector space for the generalized biological sequences.The meaning of corresponding vector operations is explained in the biological context.We presented the vector/matrix form of several widely seen sequence-based problems,including read quantification,sequence assembly,and pattern detection problem.Its advantages and disadvantages are discussed.Also,we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods.It shows the practicability and convenience of this algorithm design strategy.
基金Supported by the National Natural Science Foundation of China(71961018,71671031,71901079,71473033,41661116)the Ministry of Education Humanities and Social Sciences Research Youth Fund Project(17YJC630067)+1 种基金the Youth Foundation Project for Humanities and Social Sciences Research in Colleges and Universities of Jiangxi Province(GL18225)General Project of Social Science Planning in Jiangxi Province(19GL06).
文摘The inter-sequence analysis method is a popular decision making method considering subjective information of the decision maker.The validity of this method is sensitive to the subjective information provided by the decision maker significantly.Thus,a new weighting method based on the inter-sequence analysis method is proposed.This new method can appropriately reduce the participation of the subjective judgment of the decision maker,and then accordingly decrease the influence of the subjective judgment to the results.It only needs the decision maker to give the inter sequence of the criteria.The importance comparison of two criteria is objectively determined by considering the ratio of a criterion being superior to another one.Some numerical examples are given to illustrate the calculation process of the new method.
文摘The study on ^(13)C-NMR spectra of aliphatic carbon region of emuision-processed and solution-processed (by lithium catalyst) SBR was carried out. The assignments for more than thirty odd peaks observed experimentally were made by using 'corresponding analysis' method, combined with the empirical parameters reported in literature. The peak intensifies were calculated based on BemouUian statistic assumption.
基金partly supported by the National Science Fund of the Ministry of Education and Science of Bulgaria under grant DN 18-10/2017funds of the project RD-08-125/2021 of the University of Shumen。
文摘Results from optical CCD photometric observations of 13 pre-main-sequence stars collected during the period from February 2007 to November 2020 are presented.These stars are located in the association Cepheus OB3,in the field of the young star V733 Cephei.Photometric observations,especially concerning the long-term variability of the stars,are missing in the literature.We present the first longterm V(RI)c monitoring for them,that cover 13 years.Results from our study indicate that all of the investigated stars manifest strong photometric variability.The presented paper is a part of our program for the photometric study of pre-main-sequence stars located in active star-forming regions.
基金supported by the National Natural Science Foundation of China(Grant No.51176030)Jiangsu Science and Technology Department(Grant No.BY2015070-17)
文摘The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.
