Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the effici...Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.展开更多
This paper begins with the overthrow of the concept of combining ability in crossbreeding by the concept of heritability.The reason is that general combining ability changes with the number and kind of pure strains in...This paper begins with the overthrow of the concept of combining ability in crossbreeding by the concept of heritability.The reason is that general combining ability changes with the number and kind of pure strains in the foundation stock and hence special combining ability changes also,so that work with different kinds of pure strains in the foundation stock cannot be compared.Hence combining ability is useless as a parameter to predict the amount of heterosis expected in the next generation.On the other hand,since each cross has a separate heritability,it can be applied to a cross population just as successfully as in purebreeding.Since the same concept holds in both cases,resort to any other concept would be superfluous.That's why combining ability must be rejected.Another reason(not given in the full text)is,an infinite number of pure strains would be required in the foundation stock for its results to be comparable with those of the heritability theory,which disposes of its utility altogether.The main content of the thesis is then the centennial enigma of heterosis can be resolved by Descarte's theoretic method of deduction.Accordingly we start from the definition of heterosis.H=F¡-MP,where H is heterosis,F,is the first generation offspring,MP is the mean of the parents or midparent,and from the use of a binomial random variable and its extention to the multinomial case derive the basic relations of heterosis with its components.Starting with second degree statistics,we obtain Vn=Vr,-2cov(F,,MP)+Vup,where V and cov stand for variance and covariance.The equations of heterosis are v„=(1/2)Na²+(1/4)Nd’+Vr(F,)=additive dominance F,epistasis Vup=(1/2)Na’+(1/2)V1,additive parental epistasis V„=(1/4)Nd’+V(F)+(1/2)V1,dominance F,epistasis parental epistasis.where N is number of genes controlling a trait,a=(P1-P,)12,d is deviation from midparent,while the variance components are all indicated by their names under the repective terms.It turns out that all these can be easily computed from the data so that the problem becomes a simple one which any college student may solve.In other words,the right answers are found when the right questions are asked.Who had ever shown that the heritability principle is inapplicable in crossbreeding,e.g.,in a crossing of two pure strains?From this cue arose the realization that the F,of a cross of two pure strains must also be a Mendelian population,with p and q both equal to 1/2 which simplifies the algebra outright.This Heritability Theory of Heterosis,or HTH in capital letters,re-sts on 2 initial anguments:1)Since 0.5+0.5=1,crossing two pure strains gives a population which is only a special case of pure-breeding,thereforea heritability coefficient must exist for the F1;2)Our problem reduces to that of finding that coefficient;the an-swer is given by the additive component divided by Ve.,i.e.,(1/2)No'1 Vp..which is readily found from the solution of the het-erosis equations.Thus the elemnal enigma of heterosis is resolved!This happened at the end of the 20th century.We now come to the second point of the discovery,the new genetic parameter crossheritability which will rise in size with the increase of the number of times it's used and form the link between breeding and evolution.The advent of the Age of Evolution Engineering in the 21st century marks a totally new era,showing that artificial will ultimately supercede natural selection,with the long span of time element eliminated.For agriculture at least,it means there is no limit to the increase of food supply by the new method,with the concentra-tion of desirable genes by hybridization in place of the old theory of their fixation.Genetic gain is achieved through artificial selec-tion,with an 80%saving of time,labor and cost by adoption of the new method.Applied to a further increase in all kinds of agri-cultural products including hybrd rice,it means that a huge eacalation,in fact a New Green Revolution,on a much langer scale than that of any such before,is in view,provided it is adopted in our research and educational institutions as early as possible,ere its spread elsewhere.The possibilities from the evolution point of view can only be pictured by science fiction.展开更多
This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-...This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-stage simulation flow to study the deviceto-circuit performance of advanced memory technologies in presence of statistical and process variability.We present a DRAM example to highlight the DTCO enablement for both memory and periphery.Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.展开更多
Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural l...Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural locations in Laborecká vrchovina (Slovakia). Samples were collected by destructive methods in all three locations. Silicon content was determined in dry biomass by AAS. Silicon content in plants ranged from 21.11 ± 3.24 g·kg-1 to 32.80 ± 8.03 g·kg-1. The highest content of silicon exhibited samples of the September collection. We found that the location and the year in terms of silicon content were not statistically significant. The main sources for statistical variability in the accumulation of silicon were during the collections.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.11390371,11303036,11390374,11233004 and 61202315)The Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences+6 种基金Funding for the project has been provided by the National Development and Reform CommissionFunding for SDSS-Ⅲ has been provided by the Alfred P.Sloan Foundationthe Participating Institutionsthe National Science Foundationthe U.S.Department of Energy Office of Sciencefunded by NASANSF
文摘Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.
文摘This paper begins with the overthrow of the concept of combining ability in crossbreeding by the concept of heritability.The reason is that general combining ability changes with the number and kind of pure strains in the foundation stock and hence special combining ability changes also,so that work with different kinds of pure strains in the foundation stock cannot be compared.Hence combining ability is useless as a parameter to predict the amount of heterosis expected in the next generation.On the other hand,since each cross has a separate heritability,it can be applied to a cross population just as successfully as in purebreeding.Since the same concept holds in both cases,resort to any other concept would be superfluous.That's why combining ability must be rejected.Another reason(not given in the full text)is,an infinite number of pure strains would be required in the foundation stock for its results to be comparable with those of the heritability theory,which disposes of its utility altogether.The main content of the thesis is then the centennial enigma of heterosis can be resolved by Descarte's theoretic method of deduction.Accordingly we start from the definition of heterosis.H=F¡-MP,where H is heterosis,F,is the first generation offspring,MP is the mean of the parents or midparent,and from the use of a binomial random variable and its extention to the multinomial case derive the basic relations of heterosis with its components.Starting with second degree statistics,we obtain Vn=Vr,-2cov(F,,MP)+Vup,where V and cov stand for variance and covariance.The equations of heterosis are v„=(1/2)Na²+(1/4)Nd’+Vr(F,)=additive dominance F,epistasis Vup=(1/2)Na’+(1/2)V1,additive parental epistasis V„=(1/4)Nd’+V(F)+(1/2)V1,dominance F,epistasis parental epistasis.where N is number of genes controlling a trait,a=(P1-P,)12,d is deviation from midparent,while the variance components are all indicated by their names under the repective terms.It turns out that all these can be easily computed from the data so that the problem becomes a simple one which any college student may solve.In other words,the right answers are found when the right questions are asked.Who had ever shown that the heritability principle is inapplicable in crossbreeding,e.g.,in a crossing of two pure strains?From this cue arose the realization that the F,of a cross of two pure strains must also be a Mendelian population,with p and q both equal to 1/2 which simplifies the algebra outright.This Heritability Theory of Heterosis,or HTH in capital letters,re-sts on 2 initial anguments:1)Since 0.5+0.5=1,crossing two pure strains gives a population which is only a special case of pure-breeding,thereforea heritability coefficient must exist for the F1;2)Our problem reduces to that of finding that coefficient;the an-swer is given by the additive component divided by Ve.,i.e.,(1/2)No'1 Vp..which is readily found from the solution of the het-erosis equations.Thus the elemnal enigma of heterosis is resolved!This happened at the end of the 20th century.We now come to the second point of the discovery,the new genetic parameter crossheritability which will rise in size with the increase of the number of times it's used and form the link between breeding and evolution.The advent of the Age of Evolution Engineering in the 21st century marks a totally new era,showing that artificial will ultimately supercede natural selection,with the long span of time element eliminated.For agriculture at least,it means there is no limit to the increase of food supply by the new method,with the concentra-tion of desirable genes by hybridization in place of the old theory of their fixation.Genetic gain is achieved through artificial selec-tion,with an 80%saving of time,labor and cost by adoption of the new method.Applied to a further increase in all kinds of agri-cultural products including hybrd rice,it means that a huge eacalation,in fact a New Green Revolution,on a much langer scale than that of any such before,is in view,provided it is adopted in our research and educational institutions as early as possible,ere its spread elsewhere.The possibilities from the evolution point of view can only be pictured by science fiction.
文摘This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-stage simulation flow to study the deviceto-circuit performance of advanced memory technologies in presence of statistical and process variability.We present a DRAM example to highlight the DTCO enablement for both memory and periphery.Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.
基金The work was supported by the Agency of Ministry of Education,Science,Research and Sport of the Slovak Republic,the project:00162-0001(MS SR-3634/2010-11).
文摘Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural locations in Laborecká vrchovina (Slovakia). Samples were collected by destructive methods in all three locations. Silicon content was determined in dry biomass by AAS. Silicon content in plants ranged from 21.11 ± 3.24 g·kg-1 to 32.80 ± 8.03 g·kg-1. The highest content of silicon exhibited samples of the September collection. We found that the location and the year in terms of silicon content were not statistically significant. The main sources for statistical variability in the accumulation of silicon were during the collections.
