Description of a New Variable-Length Key, 64-Bit Block Cipher (Blowfish)
和rews包：多维数据可视化的不同Andrews曲线说明书
Package‘andrews’October23,2023Type PackageTitle Various Andrews CurvesVersion1.1.2Maintainer Sigbert Klinke<********************>Depends R(>=2.10)Description Visualisation of multidimensional data through different Andrews curves: Andrews,D.F.(1972)Plots of High-Dimensional Data.Biometrics,28(1),125-136.<doi:10.2307/2528964>.License GPL-3URL https:///sigbertklinke/andrews(development version)Encoding UTF-8LazyData trueImports grDevices,graphics,gmpSuggests knitr,rmarkdown,robustbase,mclustVignetteBuilder knitrRoxygenNote7.2.3NeedsCompilation noAuthor Jaroslav Myslivec[aut],Sigbert Klinke[cre,ctb]Repository CRANDate/Publication2023-10-2310:20:05UTCR topics documented:andrews (2)andrews0 (4)banknote (5)deftype (6)generate_n_primes (7)normalize (7)1numarray ..........................................8outlyingness .........................................9selectand ..........................................10zzz ..............................................11Index12andrews Andrews curvesDescriptionAndrews curves for visualization of multidimensional data.For colouring the curves see the details.For differences between andrews and andrews0see the vignette("andrews").With the same parameters called both functions should create the same plot.type==5is a modification of type==3and type==6is a modification of type==ageandrews(df,type =1,clr =NULL,step =100,ymax =10,alpha =NULL,palcol =NULL,lwd =1,lty ="solid",...)Argumentsdf data frame or an R object that can be converted into a data frame with as.data.frame typetype of curve •1:f (t )=x 1/√2+x 2sin(t )+x 3cos(t )+x 4sin(2t )+x 5cos(2t )+...•2:f (t )=x 1sin(t )+x 2cos(t )+x 3sin(2t )+x 4cos(2t )+...•3:f (t )=x 1cos(t )+x 2cos(√2t )+x 3cos(√3t )+...•4:f (t )=0.5p/2x 1+0.5(p −1)/2x 2(sin(t )+cos(t ))+0.5(p −2)/2x 3(sin(t )−cos(t ))+0.5(p −3)/2x 4(sin(2t )+cos(2t ))+0.5(p −4)/2x 5(sin(2t )−cos(2t ))+...)with p the number of variables•5:f (t )=x 1cos(√p 0t )+x 2cos(√p 1t )+x 3cos(√p 2t )+...with p 0=1and p i the i-th prime number•6:f (t )=1/√2(x 1+x 2(sin(t )+cos(t ))+x 3(sin(t )−cos(t ))+x 4(sin(2t )+cos(2t ))+x 5(sin(2t )−cos(2t ))+...)clr number/name of column in the data frame for color of curvesstep smoothness of curvesymax maximum of y coordinatealpha semi-transparent color(0<alpha<1)which are supported only on somedevicespalcol a function which generates a set of colors,see detailslwd line width,a positive number,defaulting to1.lty line type,can either be specified as an integer(0=blank,1=solid(default),2=dashed, 3=dotted,4=dotdash,5=longdash,6=twodash)or as one of the character strings"blank","solid","dashed","dotted","dotdash","longdash",or"twodash",where"blank"uses‘invisible lines’(i.e.,does not draw them)....further named parameters given to graphics::plot.default()except x,y,and type.DetailsIf clr has length one then it is used as column number or column name for coloring the curves:•If df[,clr]is numeric then palcol must be function which returns colors for values in therange\[0,1\]using normalized variable.The default is function function(v){hsv(0,1,v)}.•Otherwise df[,clr]is converted to a factor and palcol must be a function which returnsfor each level a color.The parameter for palcol is the numbe of levels and the default isgrDevices::rainbow().If the length of clr is the number of rows of df then clr is inter-preted as colors.Andrews curves transform multidimensional data into curves.This package presents four types of curves.ValuenothingAuthor(s)Sigbert Klinke********************,Jaroslav Myslivec*************************References•Andrews,D.F.(1972)Plots of High-Dimensional Data.Biometrics,vol.28,no.1,pp.125-136.•Khattree,R.,Naik,D.N.(2002)Andrews Plots for Multivariate Data:Some New Suggestionsand Applications.Journal of Statistical Planning and Inference,vol.100,no.2,pp.411-425.Examplesdata(iris)op <-par(mfrow=c(1,2))andrews0(iris,clr=5,ymax=3)andrews(iris,clr=5,ymax=3)par(op)andrews(iris,type=4,clr=5,ymax=NA)andrews0Andrews curvesDescriptionAndrews curves for visualization of multidimensional data.For differences between andrews and andrews2see the ‘vignette("andrews").For colouring the curves see the ageandrews0(df,type =1,clr =NULL,step =100,ymax =10,main =NULL,sub =NULL )Argumentsdf data frame typetype of curve•1:f (t )=x 1/√2+x 2sin(t )+x 3cos(t )+x 4sin(2t )+x 5cos(2t )+...•2:f (t )=x 1sin(t )+x 2cos(t )+x 3sin(2t )+x 4cos(2t )+...•3:f (t )=0.5p/2x 1+0.5(p −1)/2x 2(sin(t )+cos(t ))+0.5(p −2)/2x 3(sin(t )−cos(t ))+0.5(p −3)/2x 4(sin(2t )+cos(2t ))+0.5(p −6)/2x 5(sin(2t )−cos(2t ))+...)with $p$the number of variables•4:f (t )=1/√2(x 1+x 2(sin(t )+cos(t ))+x 3(sin(t )−cos(t ))+x 4(sin(2t )+cos(2t ))+x 5(sin(2t )−cos(2t ))+...)clr number/name of column in the date frame for color of curves step smoothness of curves ymax maximum of y coordinate.main main title for the plot subsub title for the plotbanknote5DetailsAndrews curves transform multidimensional data into curves.This package presents four types of curvesIf df[,clr]is numeric then hsv(1,1,v)with the normalized values(on\[0,1\])of df[,clr]is used.Otherwise the number of unique values in nuv<-unique(df[,clr])is used in connection with rainbow(nuv).ValuenothingAuthor(s)Jaroslav Myslivec*************************References•Andrews,D.F.(1972)Plots of High-Dimensional Data.Biometrics,vol.28,no.1,pp.125-136.•Khattree,R.,Naik,D.N.(2002)Andrews Plots for Multivariate Data:Some New Suggestions and Applications.Journal of Statistical Planning and Inference,vol.100,no.2,pp.411-425. Examplesdata(iris)andrews0(iris,clr=5,ymax=3)andrews0(iris,type=4,clr=5,ymax=2)banknote Swiss banknotes dataDescriptionThe data set contains six measurements made on100genuine and100counterfeit old-Swiss1000-franc bank notes.The data frame and the documentation is a copy of mclust::banknote.UsagebanknoteFormatA data frame with200rows and7columns:Status the status of the banknote:genuine or counterfeitLength Length of bill(mm)Left Width of left edge(mm)6deftypeRight Width of right edge(mm)Bottom Bottom margin width(mm)Top Top margin width(mm)Diagonal Length of diagonal(mm)SourceFlury,B.and Riedwyl,H.(1988).Multivariate Statistics:A practical approach.London:Chapman &Hall,Tables1.1and1.2,pp.5-8.deftype deftypeDescriptionDefines a function which can be used as basis for Andrews curves f t(t)= pj=1x ij f i(t).Usagedeftype(index=NULL,FUN=NULL,xlim=c(-pi,pi))Argumentsindex index/name of the functionFUN function of the form function(n,t){...}xlim default range for displaying curves(default:c(-pi,pi))Valueeither a list of all functions or a single functionExamples#define a new andrews curve,just with sine curvesdeftype("sine",function(n,t){n<-as.integer(if(n<1)1else n)m<-matrix(NA,nrow=length(t),ncol=n)for(i in1:n)m[,i]<-sin(i*t)m})andrews(iris,"sine")#querydeftype()deftype("sine")generate_n_primes7 generate_n_primes Generate a Sequence of Prime NumbersDescriptionGenerates a vector of thefirst n primes using gmp::nextprime().Usagegenerate_n_primes(n,one=FALSE)Argumentsn the number of primes to generate.one should1included in the sequence(default:FALSE)Valuean integer vector of prime numbersExamplesgenerate_n_primes(5)generate_n_primes(5,TRUE)normalize NormalizationDescriptionNormalization of a variable:•type==1:ar normalized into[0,1],•type==2:ar is standardized,•otherwise no normalization is done.Usagenormalize(ar,type=1)Argumentsar numeric variable.type integer:type of normalization(default:1)8numarrayDetailsNormalization of variable:ar<-(ar-min(ar))/(max(ar)-min(ar))ValueReturns normalized variable.Author(s)Jaroslav Myslivec*************************,Sigbert Klinke********************Examplesnormalize(iris[,1])numarray Numeric arrayDescriptionExtracts numeric array from data frame.Usagenumarray(df)Argumentsdf data frame.DetailsExtracts numeric array from data frame.ValueReturns numeric array.Author(s)Jaroslav Myslivec*************************,Sigbert Klinke********************Examplesnumarray(iris)outlyingness9 outlyingness outlyingnessDescriptionComputes the Stahel-Donoho outlyingness.If type is any of the available types by andrews()then the projection vectors are generated along the andrews curves.Otherwise step random directions will be used.Note that the projection vectors are always normalized to length one.Usageoutlyingness(x,type=1,step=100,xlim=NULL,normalize=1)Argumentsx data frametype type of curve,see andrews()step step smoothness of curvesxlim the x limits(x1,x2)normalize type of normalization,see normalize()Valuethe Stahel-Donoho outlyingnessReferences•Stahel,W.(1981),Robuste Schätzungen:infinitesimale Optimalität und Schätzungen von Kovarianzmatrizen,PhD thesis,ETH Z¨urich.•Donoho,D.(1982),Breakdown properties of multivariate location estimators,Ph.D.Qualify-ing paper,Dept.Statistics,Harvard University,Boston.Examples#use projection vectors from the Andrews curvesdo<-outlyingness(iris)col<-gray(1-sdo/max(sdo))andrews(iris,clr=col,ymax=NA)#use1000random projection vectorssdo<-outlyingness(iris,type=0,step=1000)col<-gray(1-sdo/max(sdo))andrews(iris,clr=col,ymax=NA)#use1000random projection vectors with adjusted outlyingnesslibrary("robustbase")x<-numarray(iris)x<-scale(x,center=apply(x,2,min),scale=apply(x,2,max)-apply(x,2,min))sdo<-adjOutlyingness(x,ndir=1000,only.outlyingness=TRUE)10selectand col<-gray(1-sdo/max(sdo))andrews(as.data.frame(x),clr=col,ymax=NA)selectand Selecting in Andrews curvesDescriptionSelecting object utility in Andrews curvesUsageselectand(df,type=1,step=100,ncol=0,from=0,to=1,col=2)Argumentsdf data frame.type type of curve.step smoothness of curves.ncol number of column in data frame for selection.from from value.to to value.col color of selected objects.DetailsDefine which objects will be selected(colored)in Andrews curves.ValueNothingAuthor(s)Jaroslav Myslivec*************************Examplesdata(iris)andrews(iris,clr=5,ymax=3)selectand(iris,ncol=1,from=5,to=5.5,col=1)zzz11 zzz ComparisonDescriptionCreates and displays a temporary PDFfile with different diagrams comparing andrews and andrews0 plots.Usagezzz()ValuenothingExamplesif(interactive())zzz()Index∗datasetsbanknote,5∗hplotandrews,2andrews0,4selectand,10andrews,2andrews(),9andrews0,4banknote,5deftype,6generate_n_primes,7gmp::nextprime(),7graphics::plot.default(),3grDevices::rainbow(),3mclust::banknote,5normalize,7normalize(),9numarray,8outlyingness,9selectand,10zzz,1112。
Stata数据集描述文档说明书
Titlecodebook —Describe data contentsSyntaxMenuDescription Options Remarks and examplesStored resultsReferencesAlso seeSyntaxcodebookvarlistifin,optionsoptionsDescriptionOptionsall print complete report without missing values header print dataset name and last saved date notes print any notes attached to variables mvreport pattern of missing valuestabulate(#)set tables/summary statistics threshold;default is tabulate(9)problems report potential problems in datasetdetail display detailed report on the variables;only with problems compact display compact report on the variablesdotsdisplay a dot for each variable processed;only with compact Languageslanguages (namelist )use with multilingual datasets;see [D ]label language for detailsMenuData>Describe data>Describe data contents (codebook)Descriptioncodebook examines the variable names,labels,and data to produce a codebook describing the dataset.Options££Optionsall is equivalent to specifying the header and notes options.It provides a complete report,which excludes only performing mv .header adds to the top of the output a header that lists the dataset name,the date that the dataset was last saved,etc.notes lists any notes attached to the variables;see [D ]notes .mv specifies that codebook search the data to determine the pattern of missing values.This is a CPU -intensive task.12codebook—Describe data contentstabulate(#)specifies the number of unique values of the variables to use to determine whether a variable is categorical or continuous.Missing values are not included in this count.The default is 9;when there are more than nine unique values,the variable is classified as continuous.Extended missing values will be included in the tabulation.problems specifies that a summary report is produced describing potential problems that have been diagnosed:•Variables that are labeled with an undefined value label•Incompletely value-labeled variables•Variables that are constant,including always missing•Leading,trailing,and embedded spaces in string variables•Embedded binary0(\0)in string variables•Noninteger-valued date variablesSee the discussion of these problems and advice on overcoming them following example5. detail may be specified only with the problems option.It specifies that the detailed report on the variables not be suppressed.compact specifies that a compact report on the variables be pact may not be specified with any options other than dots.dots specifies that a dot be displayed for every variable processed.dots may be specified only with compact.££Languages languages(namelist)is for use with multilingual datasets;see[D]label language.It indicates that the codebook pertains to the languages in namelist or to all defined languages if no such list is specified as an argument to languages().The output of codebook lists the data label and variable labels in these languages and which value labels are attached to variables in these languages.Problems are diagnosed in all these languages,as well.The problem report does not provide details in which language problems occur.We advise you to rerun codebook for problematic variables;specify detail to produce the problem report again.If you have a multilingual dataset but do not specify languages(),all output,including the problem report,is shown in the“active”language.Remarks and examples codebook,without arguments,is most usefully combined with log to produce a printed listing for enclosure in a notebook documenting the data;see[U]15Saving and printing output—logfiles.codebook is,however,also useful interactively,because you can specify one or a few variables. Example1codebook examines the data in producing its results.For variables that codebook thinks are continuous,it presents the mean;the standard deviation;and the10th,25th,50th,75th,and90th percentiles.For variables that it thinks are categorical,it presents a tabulation.In part,codebook makes this determination by counting the number of unique values of the variable.If the number is nine or fewer,codebook reports a tabulation;otherwise,it reports summary statistics.codebook—Describe data contents3 codebook distinguishes the standard missing values(.)and the extended missing values(.a through.z,denoted by.*).If extended missing values are found,codebook reports the number of distinct missing value codes that occurred in that variable.Missing values are ignored with the tabulate option when determining whether a variable is treated as continuous or categorical..use /data/r13/educ3(ccdb46,52-54).codebook fips division,allDataset:/data/r13/educ3.dtaLast saved:6Mar201322:20Label:ccdb46,52-54Number of variables:42Number of observations:956Size:145,312bytes ignoring labels,etc._dta:1.confirmed data with steve on7/22fips state/place codetype:numeric(long)range:[10060,560050]units:1unique values:956missing.:0/956mean:256495std.dev:156998percentiles:10%25%50%75%90%61462120426252848391360482530 division Census Divisiontype:numeric(int)label:divisionrange:[1,9]units:1unique values:9missing.:4/956unique mv codes:2missing.*:2/956tabulation:Freq.Numeric Label691N.Eng.972Mid Atl2023 E.N.C.784W.N.C.1155S.Atl.466 E.S.C.897W.S.C.598Mountain1959Pacific4.2.aBecause division has nine unique nonmissing values,codebook reported a tabulation.If divi-sion had contained one more unique nonmissing value,codebook would have switched to reporting summary statistics,unless we had included the tabulate(#)option.4codebook—Describe data contentsExample2The mv option is useful.It instructs codebook to search the data to determine patterns of missing values.Different kinds of missing values are not distinguished in the patterns..use /data/r13/citytemp(City Temperature Data).codebook cooldd heatdd tempjan tempjuly,mvcooldd Cooling degree daystype:numeric(int)range:[0,4389]units:1unique values:438missing.:3/956mean:1240.41std.dev:937.668percentiles:10%25%50%75%90%41161594015662761missing values:heatdd==mv<->cooldd==mvtempjan==mv-->cooldd==mvtempjuly==mv-->cooldd==mvheatdd Heating degree daystype:numeric(int)range:[0,10816]units:1unique values:471missing.:3/956mean:4425.53std.dev:2199.6percentiles:10%25%50%75%90%15102460495062326919missing values:cooldd==mv<->heatdd==mvtempjan==mv-->heatdd==mvtempjuly==mv-->heatdd==mvtempjan Average January temperaturetype:numeric(float)range:[2.2,72.6]units:.1unique values:310missing.:2/956mean:35.749std.dev:14.1881percentiles:10%25%50%75%90%20.225.131.347.855.1missing values:tempjuly==mv<->tempjan==mvcodebook—Describe data contents5tempjuly Average July temperaturetype:numeric(float)range:[58.1,93.6]units:.1unique values:196missing.:2/956mean:75.0538std.dev: 5.49504percentiles:10%25%50%75%90%68.871.874.2578.782.3missing values:tempjan==mv<->tempjuly==mvcodebook reports that if tempjan is missing,tempjuly is also missing,and vice versa.In the output for the cooldd variable,codebook also reports that the pattern of missing values is the same for cooldd and heatdd.In both cases,the correspondence is indicated with“<->”.For cooldd,codebook also states that“tempjan==mv-->cooldd==mv”.The one-way arrow means that a missing tempjan value implies a missing cooldd value but that a missing cooldd value does not necessarily imply a missing tempjan value.Another feature of codebook—this one for numeric variables—is that it can determine the units of the variable.For instance,in the example above,tempjan and tempjuly both have units of0.1, meaning that temperature is recorded to tenths of a degree.codebook handles precision considerations in making this determination(tempjan and tempjuly are float s;see[U]13.11Precision and problems therein).If we had a variable in our dataset recorded in100s(for example,21,500or 36,800),codebook would have reported the units as100.If we had a variable that took on only values divisible by5(5,10,15,etc.),codebook would have reported the units as5.Example3We can use the label language command(see[D]label language)and the label command(see [D]label)to create German value labels for our auto dataset.These labels are reported by codebook:.use /data/r13/auto(1978Automobile Data).label language en,rename(language default renamed en).label language de,new(language de now current language).label data"1978Automobile Daten".label variable foreign"Art Auto".label values foreign origin_de.label define origin_de0"Innen"1"Ausl¨a ndish"6codebook—Describe data contents.codebook foreignforeign Art Autotype:numeric(byte)label:origin_derange:[0,1]units:1unique values:2missing.:0/74tabulation:Freq.Numeric Label520Innen221Ausl¨a ndish.codebook foreign,languages(en de)foreign in en:Car typein de:Art Autotype:numeric(byte)label in en:originlabel in de:origin_derange:[0,1]units:1unique values:2missing.:0/74tabulation:Freq.Numeric origin origin_de520Domestic Innen221Foreign Ausl¨a ndishWith the languages()option,the value labels are shown in the specified active and available languages.Example4codebook,compact summarizes the variables in your dataset,including variable labels.It is an alternative to the summarize command..use /data/r13/auto(1978Automobile Data).codebook,compactVariable Obs Unique Mean Min Max Labelmake7474...Make and Modelprice74746165.257329115906Pricempg742121.29731241Mileage(mpg)rep78695 3.40579715Repair Record1978headroom748 2.993243 1.55Headroom(in.)trunk741813.75676523Trunk space(cu.ft.)weight74643019.45917604840Weight(lbs.)length7447187.9324142233Length(in.)turn741839.648653151Turn Circle(ft.)displacement7431197.297379425Displacement(cu.in.)gear_ratio7436 3.014865 2.19 3.89Gear Ratioforeign742.297297301Car typecodebook—Describe data contents7.summarizeVariable Obs Mean Std.Dev.Min Maxmake0price746165.2572949.496329115906mpg7421.2973 5.7855031241rep7869 3.405797.989932315headroom74 2.993243.8459948 1.55trunk7413.75676 4.277404523weight743019.459777.193617604840length74187.932422.26634142233turn7439.64865 4.3993543151displacement74197.297391.8372279425gear_ratio74 3.014865.4562871 2.19 3.89foreign74.2972973.460188501Example5When codebook determines that neither a tabulation nor a listing of summary statistics is appropriate, for instance,for a string variable or for a numeric variable taking on many labeled values,it reports a few examples instead..use /data/r13/funnyvar.codebook namename(unlabeled)type:string(str5),but longest is str3unique values:10missing"":0/10examples:"10""3""5""7"warning:variable has embedded blankscodebook is also on the lookout for common problems that might cause you to make errors when dealing with the data.For string variables,this includes leading,embedded,and trailing blanks and embedded binary0(\0).In the output above,codebook informed us that name includes embedded blanks.If name had leading or trailing blanks,it would have mentioned that,too.When variables are value labeled,codebook performs two checks.First,if a value label labname is associated with a variable,codebook checks whether labname is actually defined.Second,it checks whether all values are value labeled.Partial labeling of a variable may mean that the label was defined incorrectly(for instance,the variable has values0and1,but the value label maps1to“male”and2 to“female”)or that the variable was defined incorrectly(for example,a variable gender with three values).codebook checks whether date variables are integer valued.If the problems option is specified,codebook does not provide detailed descriptions of each variable but reports only the potential problems in the data.8codebook—Describe data contents.codebook,problemsPotential problems in dataset /data/r13/funnyvar.dtapotential problem variablesconstant(or all missing)vars human planetvars with nonexisting label educincompletely labeled vars genderstr#vars that may be compressed name address city country planetstring vars with leading blanks city countrystring vars with trailing blanks planetstring vars with embedded blanks name addressstring vars with embedded\0mugshotnoninteger-valued date vars birthdateIn the example above,codebook,problems reported various potential problems with the dataset. These problems include•Constant variables,including variables that are always missingVariables that are constant,taking the same value in all observations,or that are alwaysmissing,are often superfluous.Such variables,however,may also indicate problems.For instance,variables that are always missing may occur when importing data withan incorrect input specification.Such variables may also occur if you generate a newvariable for a subset of the data,selected with an expression that is false for allobservations.Advice:Carefully check the origin of constant variables.If you are saving a constantvariable,be sure to compress the variable to use minimal storage.•Variables with nonexisting value labelsStata treats value labels as separate objects that can be attached to one or more variables.A problem may arise if variables are linked to value labels that are not yet defined orif an incorrect value label name was used.Advice:Attach the correct value label,or label define the value label.See[D]label.•Incompletely labeled variablesA variable is called“incompletely value labeled”if the variable is value labeled but nomapping is provided for some values of the variable.An example is a variable withvalues0,1,and2and value labels for1,2,and3.This situation usually indicates anerror,either in the data or in the value label.Advice:Change either the data or the value label.•String variables that may be compressedThe storage space used by a string variable is determined by its data type;see[D]datatypes.For instance,the storage type str20indicates that20bytes are used perobservation.If the declared storage type exceeds your requirements,memory and diskspace is wasted.Advice:Use compress to store the data as compactly as possible.•String variables with leading or trailing blanksIn most applications,leading and trailing spaces do not affect the meaning of variablesbut are probably side effects from importing the data or from data manipulation.Spuriouscodebook—Describe data contents9 leading and trailing spaces force Stata to use more memory than required.In addition,manipulating strings with leading and trailing spaces is harder.Advice:Remove leading and trailing blanks from a string variable s by typing replace s=trim(s)See[D]functions.•String variables with embedded blanksString variables with embedded blanks are often appropriate;however,sometimes theyindicate problems importing the data.Advice:Verify that blanks are meaningful in the variables.•String variables with embedded binary0(\0)String variables with embedded binary0(\0)are allowed;however,caution should beused when working with them as some commands and functions may only work withthe plain-text portion of a binary string,ignoring anything after thefirst binary0.Advice:Be aware of binary strings in your data and whether you are manipulating themin a way that is only appropriate with plain-text values.•Noninteger-valued date variablesStata’s date and time formats were designed for use with integer values but will workwith noninteger values.Advice:Carefully inspect the nature of the noninteger values.If noninteger values in avariable are the consequence of roundoff error,you may want to round the variable tothe nearest integer.replace time=round(time)Of course,more problems not reported by codebook are possible.These might include •Numerical data stored as stringsAfter importing data into Stata,you may discover that some string variables can actuallybe interpreted as numbers.Stata can do much more with numerical data than withstring data.Moreover,string representation usually makes less efficient use of computerresources.destring will convert string variables to numeric.A string variable may contain a“field”with numeric information.An example is anaddress variable that contains the street name followed by the house number.The Statastring functions can extract the relevant substring.•Categorical variables stored as stringsMost statistical commands do not allow string variables.Moreover,string variables thattake only a limited number of distinct values are an inefficient storage evalue-labeled numeric values instead.These are easily created with encode.•Duplicate observationsSee[D]duplicates.10codebook—Describe data contents•Observations that are always missingDrop observations that are missing for all variables in varlist using the rownonmiss()egen function:egen nobs=rownonmiss(varlist)drop if nobs==0Specify all for varlist if only observations that are always missing should be dropped.Stored resultscodebook stores the following lists of variables with potential problems in r():Macrosr(cons)constant(or missing)r(labelnotfound)undefined value labeledr(notlabeled)value labeled but with unlabeled categoriesr(str type)compressibler(str leading)leading blanksr(str trailing)trailing blanksr(str embedded)embedded blanksr(str embedded0)embedded binary0(\0)r(realdate)noninteger datesReferencesCox,N.J.2008.Speaking Stata:Distinct observations.Stata Journal8:557–568..2012.Software Updates:Speaking Stata:Distinct observations.Stata Journal12:352.Long,J.S.2009.The Workflow of Data Analysis Using Stata.College Station,TX:Stata Press.Also see[D]describe—Describe data in memory or infile[D]ds—List variables matching name patterns or other characteristics[D]inspect—Display simple summary of data’s attributes[D]labelbook—Label utilities[D]notes—Place notes in data[D]split—Split string variables into parts[U]15Saving and printing output—logfiles。
Native Instruments MASCHINE MK3 用户手册说明书
The information in this document is subject to change without notice and does not represent a commitment on the part of Native Instruments GmbH. The software described by this docu-ment is subject to a License Agreement and may not be copied to other media. No part of this publication may be copied, reproduced or otherwise transmitted or recorded, for any purpose, without prior written permission by Native Instruments GmbH, hereinafter referred to as Native Instruments.“Native Instruments”, “NI” and associated logos are (registered) trademarks of Native Instru-ments GmbH.ASIO, VST, HALion and Cubase are registered trademarks of Steinberg Media Technologies GmbH.All other product and company names are trademarks™ or registered® trademarks of their re-spective holders. Use of them does not imply any affiliation with or endorsement by them.Document authored by: David Gover and Nico Sidi.Software version: 2.8 (02/2019)Hardware version: MASCHINE MK3Special thanks to the Beta Test Team, who were invaluable not just in tracking down bugs, but in making this a better product.NATIVE INSTRUMENTS GmbH Schlesische Str. 29-30D-10997 Berlin Germanywww.native-instruments.de NATIVE INSTRUMENTS North America, Inc. 6725 Sunset Boulevard5th FloorLos Angeles, CA 90028USANATIVE INSTRUMENTS K.K.YO Building 3FJingumae 6-7-15, Shibuya-ku, Tokyo 150-0001Japanwww.native-instruments.co.jp NATIVE INSTRUMENTS UK Limited 18 Phipp StreetLondon EC2A 4NUUKNATIVE INSTRUMENTS FRANCE SARL 113 Rue Saint-Maur75011 ParisFrance SHENZHEN NATIVE INSTRUMENTS COMPANY Limited 5F, Shenzhen Zimao Center111 Taizi Road, Nanshan District, Shenzhen, GuangdongChina© NATIVE INSTRUMENTS GmbH, 2019. All rights reserved.Table of Contents1Welcome to MASCHINE (25)1.1MASCHINE Documentation (26)1.2Document Conventions (27)1.3New Features in MASCHINE 2.8 (29)1.4New Features in MASCHINE 2.7.10 (31)1.5New Features in MASCHINE 2.7.8 (31)1.6New Features in MASCHINE 2.7.7 (32)1.7New Features in MASCHINE 2.7.4 (33)1.8New Features in MASCHINE 2.7.3 (36)2Quick Reference (38)2.1Using Your Controller (38)2.1.1Controller Modes and Mode Pinning (38)2.1.2Controlling the Software Views from Your Controller (40)2.2MASCHINE Project Overview (43)2.2.1Sound Content (44)2.2.2Arrangement (45)2.3MASCHINE Hardware Overview (48)2.3.1MASCHINE Hardware Overview (48)2.3.1.1Control Section (50)2.3.1.2Edit Section (53)2.3.1.3Performance Section (54)2.3.1.4Group Section (56)2.3.1.5Transport Section (56)2.3.1.6Pad Section (58)2.3.1.7Rear Panel (63)2.4MASCHINE Software Overview (65)2.4.1Header (66)2.4.2Browser (68)2.4.3Arranger (70)2.4.4Control Area (73)2.4.5Pattern Editor (74)3Basic Concepts (76)3.1Important Names and Concepts (76)3.2Adjusting the MASCHINE User Interface (79)3.2.1Adjusting the Size of the Interface (79)3.2.2Switching between Ideas View and Song View (80)3.2.3Showing/Hiding the Browser (81)3.2.4Showing/Hiding the Control Lane (81)3.3Common Operations (82)3.3.1Using the 4-Directional Push Encoder (82)3.3.2Pinning a Mode on the Controller (83)3.3.3Adjusting Volume, Swing, and Tempo (84)3.3.4Undo/Redo (87)3.3.5List Overlay for Selectors (89)3.3.6Zoom and Scroll Overlays (90)3.3.7Focusing on a Group or a Sound (91)3.3.8Switching Between the Master, Group, and Sound Level (96)3.3.9Navigating Channel Properties, Plug-ins, and Parameter Pages in the Control Area.973.3.9.1Extended Navigate Mode on Your Controller (102)3.3.10Navigating the Software Using the Controller (105)3.3.11Using Two or More Hardware Controllers (106)3.3.12Touch Auto-Write Option (108)3.4Native Kontrol Standard (110)3.5Stand-Alone and Plug-in Mode (111)3.5.1Differences between Stand-Alone and Plug-in Mode (112)3.5.2Switching Instances (113)3.5.3Controlling Various Instances with Different Controllers (114)3.6Host Integration (114)3.6.1Setting up Host Integration (115)3.6.1.1Setting up Ableton Live (macOS) (115)3.6.1.2Setting up Ableton Live (Windows) (116)3.6.1.3Setting up Apple Logic Pro X (116)3.6.2Integration with Ableton Live (117)3.6.3Integration with Apple Logic Pro X (119)3.7Preferences (120)3.7.1Preferences – General Page (121)3.7.2Preferences – Audio Page (126)3.7.3Preferences – MIDI Page (130)3.7.4Preferences – Default Page (133)3.7.5Preferences – Library Page (137)3.7.6Preferences – Plug-ins Page (145)3.7.7Preferences – Hardware Page (150)3.7.8Preferences – Colors Page (154)3.8Integrating MASCHINE into a MIDI Setup (156)3.8.1Connecting External MIDI Equipment (156)3.8.2Sync to External MIDI Clock (157)3.8.3Send MIDI Clock (158)3.9Syncing MASCHINE using Ableton Link (159)3.9.1Connecting to a Network (159)3.9.2Joining and Leaving a Link Session (159)3.10Using a Pedal with the MASCHINE Controller (160)3.11File Management on the MASCHINE Controller (161)4Browser (163)4.1Browser Basics (163)4.1.1The MASCHINE Library (163)4.1.2Browsing the Library vs. Browsing Your Hard Disks (164)4.2Searching and Loading Files from the Library (165)4.2.1Overview of the Library Pane (165)4.2.2Selecting or Loading a Product and Selecting a Bank from the Browser (170)4.2.2.1[MK3] Browsing by Product Category Using the Controller (174)4.2.2.2[MK3] Browsing by Product Vendor Using the Controller (174)4.2.3Selecting a Product Category, a Product, a Bank, and a Sub-Bank (175)4.2.3.1Selecting a Product Category, a Product, a Bank, and a Sub-Bank on theController (179)4.2.4Selecting a File Type (180)4.2.5Choosing Between Factory and User Content (181)4.2.6Selecting Type and Character Tags (182)4.2.7List and Tag Overlays in the Browser (186)4.2.8Performing a Text Search (188)4.2.9Loading a File from the Result List (188)4.3Additional Browsing Tools (193)4.3.1Loading the Selected Files Automatically (193)4.3.2Auditioning Instrument Presets (195)4.3.3Auditioning Samples (196)4.3.4Loading Groups with Patterns (197)4.3.5Loading Groups with Routing (198)4.3.6Displaying File Information (198)4.4Using Favorites in the Browser (199)4.5Editing the Files’ Tags and Properties (203)4.5.1Attribute Editor Basics (203)4.5.2The Bank Page (205)4.5.3The Types and Characters Pages (205)4.5.4The Properties Page (208)4.6Loading and Importing Files from Your File System (209)4.6.1Overview of the FILES Pane (209)4.6.2Using Favorites (211)4.6.3Using the Location Bar (212)4.6.4Navigating to Recent Locations (213)4.6.5Using the Result List (214)4.6.6Importing Files to the MASCHINE Library (217)4.7Locating Missing Samples (219)4.8Using Quick Browse (221)5Managing Sounds, Groups, and Your Project (225)5.1Overview of the Sounds, Groups, and Master (225)5.1.1The Sound, Group, and Master Channels (226)5.1.2Similarities and Differences in Handling Sounds and Groups (227)5.1.3Selecting Multiple Sounds or Groups (228)5.2Managing Sounds (233)5.2.1Loading Sounds (235)5.2.2Pre-listening to Sounds (236)5.2.3Renaming Sound Slots (237)5.2.4Changing the Sound’s Color (237)5.2.5Saving Sounds (239)5.2.6Copying and Pasting Sounds (241)5.2.7Moving Sounds (244)5.2.8Resetting Sound Slots (245)5.3Managing Groups (247)5.3.1Creating Groups (248)5.3.2Loading Groups (249)5.3.3Renaming Groups (251)5.3.4Changing the Group’s Color (251)5.3.5Saving Groups (253)5.3.6Copying and Pasting Groups (255)5.3.7Reordering Groups (258)5.3.8Deleting Groups (259)5.4Exporting MASCHINE Objects and Audio (260)5.4.1Saving a Group with its Samples (261)5.4.2Saving a Project with its Samples (262)5.4.3Exporting Audio (264)5.5Importing Third-Party File Formats (270)5.5.1Loading REX Files into Sound Slots (270)5.5.2Importing MPC Programs to Groups (271)6Playing on the Controller (275)6.1Adjusting the Pads (275)6.1.1The Pad View in the Software (275)6.1.2Choosing a Pad Input Mode (277)6.1.3Adjusting the Base Key (280)6.1.4Using Choke Groups (282)6.1.5Using Link Groups (284)6.2Adjusting the Key, Choke, and Link Parameters for Multiple Sounds (286)6.3Playing Tools (287)6.3.1Mute and Solo (288)6.3.2Choke All Notes (292)6.3.3Groove (293)6.3.4Level, Tempo, Tune, and Groove Shortcuts on Your Controller (295)6.3.5Tap Tempo (299)6.4Performance Features (300)6.4.1Overview of the Perform Features (300)6.4.2Selecting a Scale and Creating Chords (303)6.4.3Scale and Chord Parameters (303)6.4.4Creating Arpeggios and Repeated Notes (316)6.4.5Swing on Note Repeat / Arp Output (321)6.5Using Lock Snapshots (322)6.5.1Creating a Lock Snapshot (322)6.5.2Using Extended Lock (323)6.5.3Updating a Lock Snapshot (323)6.5.4Recalling a Lock Snapshot (324)6.5.5Morphing Between Lock Snapshots (324)6.5.6Deleting a Lock Snapshot (325)6.5.7Triggering Lock Snapshots via MIDI (326)6.6Using the Smart Strip (327)6.6.1Pitch Mode (328)6.6.2Modulation Mode (328)6.6.3Perform Mode (328)6.6.4Notes Mode (329)7Working with Plug-ins (330)7.1Plug-in Overview (330)7.1.1Plug-in Basics (330)7.1.2First Plug-in Slot of Sounds: Choosing the Sound’s Role (334)7.1.3Loading, Removing, and Replacing a Plug-in (335)7.1.3.1Browser Plug-in Slot Selection (341)7.1.4Adjusting the Plug-in Parameters (344)7.1.5Bypassing Plug-in Slots (344)7.1.6Using Side-Chain (346)7.1.7Moving Plug-ins (346)7.1.8Alternative: the Plug-in Strip (348)7.1.9Saving and Recalling Plug-in Presets (348)7.1.9.1Saving Plug-in Presets (349)7.1.9.2Recalling Plug-in Presets (350)7.1.9.3Removing a Default Plug-in Preset (351)7.2The Sampler Plug-in (352)7.2.1Page 1: Voice Settings / Engine (354)7.2.2Page 2: Pitch / Envelope (356)7.2.3Page 3: FX / Filter (359)7.2.4Page 4: Modulation (361)7.2.5Page 5: LFO (363)7.2.6Page 6: Velocity / Modwheel (365)7.3Using Native Instruments and External Plug-ins (367)7.3.1Opening/Closing Plug-in Windows (367)7.3.2Using the VST/AU Plug-in Parameters (370)7.3.3Setting Up Your Own Parameter Pages (371)7.3.4Using VST/AU Plug-in Presets (376)7.3.5Multiple-Output Plug-ins and Multitimbral Plug-ins (378)8Using the Audio Plug-in (380)8.1Loading a Loop into the Audio Plug-in (384)8.2Editing Audio in the Audio Plug-in (385)8.3Using Loop Mode (386)8.4Using Gate Mode (388)9Using the Drumsynths (390)9.1Drumsynths – General Handling (391)9.1.1Engines: Many Different Drums per Drumsynth (391)9.1.2Common Parameter Organization (391)9.1.3Shared Parameters (394)9.1.4Various Velocity Responses (394)9.1.5Pitch Range, Tuning, and MIDI Notes (394)9.2The Kicks (395)9.2.1Kick – Sub (397)9.2.2Kick – Tronic (399)9.2.3Kick – Dusty (402)9.2.4Kick – Grit (403)9.2.5Kick – Rasper (406)9.2.6Kick – Snappy (407)9.2.7Kick – Bold (409)9.2.8Kick – Maple (411)9.2.9Kick – Push (412)9.3The Snares (414)9.3.1Snare – Volt (416)9.3.2Snare – Bit (418)9.3.3Snare – Pow (420)9.3.4Snare – Sharp (421)9.3.5Snare – Airy (423)9.3.6Snare – Vintage (425)9.3.7Snare – Chrome (427)9.3.8Snare – Iron (429)9.3.9Snare – Clap (431)9.3.10Snare – Breaker (433)9.4The Hi-hats (435)9.4.1Hi-hat – Silver (436)9.4.2Hi-hat – Circuit (438)9.4.3Hi-hat – Memory (440)9.4.4Hi-hat – Hybrid (442)9.4.5Creating a Pattern with Closed and Open Hi-hats (444)9.5The Toms (445)9.5.1Tom – Tronic (447)9.5.2Tom – Fractal (449)9.5.3Tom – Floor (453)9.5.4Tom – High (455)9.6The Percussions (456)9.6.1Percussion – Fractal (458)9.6.2Percussion – Kettle (461)9.6.3Percussion – Shaker (463)9.7The Cymbals (467)9.7.1Cymbal – Crash (469)9.7.2Cymbal – Ride (471)10Using the Bass Synth (474)10.1Bass Synth – General Handling (475)10.1.1Parameter Organization (475)10.1.2Bass Synth Parameters (477)11Working with Patterns (479)11.1Pattern Basics (479)11.1.1Pattern Editor Overview (480)11.1.2Navigating the Event Area (486)11.1.3Following the Playback Position in the Pattern (488)11.1.4Jumping to Another Playback Position in the Pattern (489)11.1.5Group View and Keyboard View (491)11.1.6Adjusting the Arrange Grid and the Pattern Length (493)11.1.7Adjusting the Step Grid and the Nudge Grid (497)11.2Recording Patterns in Real Time (501)11.2.1Recording Your Patterns Live (501)11.2.2The Record Prepare Mode (504)11.2.3Using the Metronome (505)11.2.4Recording with Count-in (506)11.2.5Quantizing while Recording (508)11.3Recording Patterns with the Step Sequencer (508)11.3.1Step Mode Basics (508)11.3.2Editing Events in Step Mode (511)11.3.3Recording Modulation in Step Mode (513)11.4Editing Events (514)11.4.1Editing Events with the Mouse: an Overview (514)11.4.2Creating Events/Notes (517)11.4.3Selecting Events/Notes (518)11.4.4Editing Selected Events/Notes (526)11.4.5Deleting Events/Notes (532)11.4.6Cut, Copy, and Paste Events/Notes (535)11.4.7Quantizing Events/Notes (538)11.4.8Quantization While Playing (540)11.4.9Doubling a Pattern (541)11.4.10Adding Variation to Patterns (541)11.5Recording and Editing Modulation (546)11.5.1Which Parameters Are Modulatable? (547)11.5.2Recording Modulation (548)11.5.3Creating and Editing Modulation in the Control Lane (550)11.6Creating MIDI Tracks from Scratch in MASCHINE (555)11.7Managing Patterns (557)11.7.1The Pattern Manager and Pattern Mode (558)11.7.2Selecting Patterns and Pattern Banks (560)11.7.3Creating Patterns (563)11.7.4Deleting Patterns (565)11.7.5Creating and Deleting Pattern Banks (566)11.7.6Naming Patterns (568)11.7.7Changing the Pattern’s Color (570)11.7.8Duplicating, Copying, and Pasting Patterns (571)11.7.9Moving Patterns (574)11.7.10Adjusting Pattern Length in Fine Increments (575)11.8Importing/Exporting Audio and MIDI to/from Patterns (576)11.8.1Exporting Audio from Patterns (576)11.8.2Exporting MIDI from Patterns (577)11.8.3Importing MIDI to Patterns (580)12Audio Routing, Remote Control, and Macro Controls (589)12.1Audio Routing in MASCHINE (590)12.1.1Sending External Audio to Sounds (591)12.1.2Configuring the Main Output of Sounds and Groups (596)12.1.3Setting Up Auxiliary Outputs for Sounds and Groups (601)12.1.4Configuring the Master and Cue Outputs of MASCHINE (605)12.1.5Mono Audio Inputs (610)12.1.5.1Configuring External Inputs for Sounds in Mix View (611)12.2Using MIDI Control and Host Automation (614)12.2.1Triggering Sounds via MIDI Notes (615)12.2.2Triggering Scenes via MIDI (622)12.2.3Controlling Parameters via MIDI and Host Automation (623)12.2.4Selecting VST/AU Plug-in Presets via MIDI Program Change (631)12.2.5Sending MIDI from Sounds (632)12.3Creating Custom Sets of Parameters with the Macro Controls (636)12.3.1Macro Control Overview (637)12.3.2Assigning Macro Controls Using the Software (638)12.3.3Assigning Macro Controls Using the Controller (644)13Controlling Your Mix (646)13.1Mix View Basics (646)13.1.1Switching between Arrange View and Mix View (646)13.1.2Mix View Elements (647)13.2The Mixer (649)13.2.1Displaying Groups vs. Displaying Sounds (650)13.2.2Adjusting the Mixer Layout (652)13.2.3Selecting Channel Strips (653)13.2.4Managing Your Channels in the Mixer (654)13.2.5Adjusting Settings in the Channel Strips (656)13.2.6Using the Cue Bus (660)13.3The Plug-in Chain (662)13.4The Plug-in Strip (663)13.4.1The Plug-in Header (665)13.4.2Panels for Drumsynths and Internal Effects (667)13.4.3Panel for the Sampler (668)13.4.4Custom Panels for Native Instruments Plug-ins (671)13.4.5Undocking a Plug-in Panel (Native Instruments and External Plug-ins Only) (675)13.5Controlling Your Mix from the Controller (677)13.5.1Navigating Your Channels in Mix Mode (678)13.5.2Adjusting the Level and Pan in Mix Mode (679)13.5.3Mute and Solo in Mix Mode (680)13.5.4Plug-in Icons in Mix Mode (680)14Using Effects (681)14.1Applying Effects to a Sound, a Group or the Master (681)14.1.1Adding an Effect (681)14.1.2Other Operations on Effects (690)14.1.3Using the Side-Chain Input (692)14.2Applying Effects to External Audio (695)14.2.1Step 1: Configure MASCHINE Audio Inputs (695)14.2.2Step 2: Set up a Sound to Receive the External Input (698)14.2.3Step 3: Load an Effect to Process an Input (700)14.3Creating a Send Effect (701)14.3.1Step 1: Set Up a Sound or Group as Send Effect (702)14.3.2Step 2: Route Audio to the Send Effect (706)14.3.3 A Few Notes on Send Effects (708)14.4Creating Multi-Effects (709)15Effect Reference (712)15.1Dynamics (713)15.1.1Compressor (713)15.1.2Gate (717)15.1.3Transient Master (721)15.1.4Limiter (723)15.1.5Maximizer (727)15.2Filtering Effects (730)15.2.1EQ (730)15.2.2Filter (733)15.2.3Cabinet (737)15.3Modulation Effects (738)15.3.1Chorus (738)15.3.2Flanger (740)15.3.3FM (742)15.3.4Freq Shifter (743)15.3.5Phaser (745)15.4Spatial and Reverb Effects (747)15.4.1Ice (747)15.4.2Metaverb (749)15.4.3Reflex (750)15.4.4Reverb (Legacy) (752)15.4.5Reverb (754)15.4.5.1Reverb Room (754)15.4.5.2Reverb Hall (757)15.4.5.3Plate Reverb (760)15.5Delays (762)15.5.1Beat Delay (762)15.5.2Grain Delay (765)15.5.3Grain Stretch (767)15.5.4Resochord (769)15.6Distortion Effects (771)15.6.1Distortion (771)15.6.2Lofi (774)15.6.3Saturator (775)15.7Perform FX (779)15.7.1Filter (780)15.7.2Flanger (782)15.7.3Burst Echo (785)15.7.4Reso Echo (787)15.7.5Ring (790)15.7.6Stutter (792)15.7.7Tremolo (795)15.7.8Scratcher (798)16Working with the Arranger (801)16.1Arranger Basics (801)16.1.1Navigating Song View (804)16.1.2Following the Playback Position in Your Project (806)16.1.3Performing with Scenes and Sections using the Pads (807)16.2Using Ideas View (811)16.2.1Scene Overview (811)16.2.2Creating Scenes (813)16.2.3Assigning and Removing Patterns (813)16.2.4Selecting Scenes (817)16.2.5Deleting Scenes (818)16.2.6Creating and Deleting Scene Banks (820)16.2.7Clearing Scenes (820)16.2.8Duplicating Scenes (821)16.2.9Reordering Scenes (822)16.2.10Making Scenes Unique (824)16.2.11Appending Scenes to Arrangement (825)16.2.12Naming Scenes (826)16.2.13Changing the Color of a Scene (827)16.3Using Song View (828)16.3.1Section Management Overview (828)16.3.2Creating Sections (833)16.3.3Assigning a Scene to a Section (834)16.3.4Selecting Sections and Section Banks (835)16.3.5Reorganizing Sections (839)16.3.6Adjusting the Length of a Section (840)16.3.6.1Adjusting the Length of a Section Using the Software (841)16.3.6.2Adjusting the Length of a Section Using the Controller (843)16.3.7Clearing a Pattern in Song View (843)16.3.8Duplicating Sections (844)16.3.8.1Making Sections Unique (845)16.3.9Removing Sections (846)16.3.10Renaming Scenes (848)16.3.11Clearing Sections (849)16.3.12Creating and Deleting Section Banks (850)16.3.13Working with Patterns in Song view (850)16.3.13.1Creating a Pattern in Song View (850)16.3.13.2Selecting a Pattern in Song View (850)16.3.13.3Clearing a Pattern in Song View (851)16.3.13.4Renaming a Pattern in Song View (851)16.3.13.5Coloring a Pattern in Song View (851)16.3.13.6Removing a Pattern in Song View (852)16.3.13.7Duplicating a Pattern in Song View (852)16.3.14Enabling Auto Length (852)16.3.15Looping (853)16.3.15.1Setting the Loop Range in the Software (854)16.4Playing with Sections (855)16.4.1Jumping to another Playback Position in Your Project (855)16.5Triggering Sections or Scenes via MIDI (856)16.6The Arrange Grid (858)16.7Quick Grid (860)17Sampling and Sample Mapping (862)17.1Opening the Sample Editor (862)17.2Recording Audio (863)17.2.1Opening the Record Page (863)17.2.2Selecting the Source and the Recording Mode (865)17.2.3Arming, Starting, and Stopping the Recording (868)17.2.5Using the Footswitch for Recording Audio (871)17.2.6Checking Your Recordings (872)17.2.7Location and Name of Your Recorded Samples (876)17.3Editing a Sample (876)17.3.1Using the Edit Page (877)17.3.2Audio Editing Functions (882)17.4Slicing a Sample (890)17.4.1Opening the Slice Page (891)17.4.2Adjusting the Slicing Settings (893)17.4.3Live Slicing (898)17.4.3.1Live Slicing Using the Controller (898)17.4.3.2Delete All Slices (899)17.4.4Manually Adjusting Your Slices (899)17.4.5Applying the Slicing (906)17.5Mapping Samples to Zones (912)17.5.1Opening the Zone Page (912)17.5.2Zone Page Overview (913)17.5.3Selecting and Managing Zones in the Zone List (915)17.5.4Selecting and Editing Zones in the Map View (920)17.5.5Editing Zones in the Sample View (924)17.5.6Adjusting the Zone Settings (927)17.5.7Adding Samples to the Sample Map (934)18Appendix: Tips for Playing Live (937)18.1Preparations (937)18.1.1Focus on the Hardware (937)18.1.2Customize the Pads of the Hardware (937)18.1.3Check Your CPU Power Before Playing (937)18.1.4Name and Color Your Groups, Patterns, Sounds and Scenes (938)18.1.5Consider Using a Limiter on Your Master (938)18.1.6Hook Up Your Other Gear and Sync It with MIDI Clock (938)18.1.7Improvise (938)18.2Basic Techniques (938)18.2.1Use Mute and Solo (938)18.2.2Use Scene Mode and Tweak the Loop Range (939)18.2.3Create Variations of Your Drum Patterns in the Step Sequencer (939)18.2.4Use Note Repeat (939)18.2.5Set Up Your Own Multi-effect Groups and Automate Them (939)18.3Special Tricks (940)18.3.1Changing Pattern Length for Variation (940)18.3.2Using Loops to Cycle Through Samples (940)18.3.3Using Loops to Cycle Through Samples (940)18.3.4Load Long Audio Files and Play with the Start Point (940)19Troubleshooting (941)19.1Knowledge Base (941)19.2Technical Support (941)19.3Registration Support (942)19.4User Forum (942)20Glossary (943)Index (951)1Welcome to MASCHINEThank you for buying MASCHINE!MASCHINE is a groove production studio that implements the familiar working style of classi-cal groove boxes along with the advantages of a computer based system. MASCHINE is ideal for making music live, as well as in the studio. It’s the hands-on aspect of a dedicated instru-ment, the MASCHINE hardware controller, united with the advanced editing features of the MASCHINE software.Creating beats is often not very intuitive with a computer, but using the MASCHINE hardware controller to do it makes it easy and fun. You can tap in freely with the pads or use Note Re-peat to jam along. Alternatively, build your beats using the step sequencer just as in classic drum machines.Patterns can be intuitively combined and rearranged on the fly to form larger ideas. You can try out several different versions of a song without ever having to stop the music.Since you can integrate it into any sequencer that supports VST, AU, or AAX plug-ins, you can reap the benefits in almost any software setup, or use it as a stand-alone application. You can sample your own material, slice loops and rearrange them easily.However, MASCHINE is a lot more than an ordinary groovebox or sampler: it comes with an inspiring 7-gigabyte library, and a sophisticated, yet easy to use tag-based Browser to give you instant access to the sounds you are looking for.What’s more, MASCHINE provides lots of options for manipulating your sounds via internal ef-fects and other sound-shaping possibilities. You can also control external MIDI hardware and 3rd-party software with the MASCHINE hardware controller, while customizing the functions of the pads, knobs and buttons according to your needs utilizing the included Controller Editor application. We hope you enjoy this fantastic instrument as much as we do. Now let’s get go-ing!—The MASCHINE team at Native Instruments.MASCHINE Documentation1.1MASCHINE DocumentationNative Instruments provide many information sources regarding MASCHINE. The main docu-ments should be read in the following sequence:1.MASCHINE Getting Started: This document provides a practical approach to MASCHINE viaa set of tutorials covering easy and more advanced tasks in order to help you familiarizeyourself with MASCHINE.2.MASCHINE Manual (this document): The MASCHINE Manual provides you with a compre-hensive description of all MASCHINE software and hardware features.Additional documentation sources provide you with details on more specific topics:▪Controller Editor Manual: Besides using your MASCHINE hardware controller together withits dedicated MASCHINE software, you can also use it as a powerful and highly versatileMIDI controller to pilot any other MIDI-capable application or device. This is made possibleby the Controller Editor software, an application that allows you to precisely define all MIDIassignments for your MASCHINE controller. The Controller Editor was installed during theMASCHINE installation procedure. For more information on this, please refer to the Con-troller Editor Manual available as a PDF file via the Help menu of Controller Editor.▪Online Support Videos: You can find a number of support videos on The Official Native In-struments Support Channel under the following URL: https:///NIsupport-EN. We recommend that you follow along with these instructions while the respective ap-plication is running on your computer.Other Online Resources:If you are experiencing problems related to your Native Instruments product that the supplied documentation does not cover, there are several ways of getting help:▪Knowledge Base▪User Forum▪Technical Support▪Registration SupportYou will find more information on these subjects in the chapter Troubleshooting.1.2Document ConventionsThis section introduces you to the signage and text highlighting used in this manual. This man-ual uses particular formatting to point out special facts and to warn you of potential issues. The icons introducing these notes let you see what kind of information is to be expected:This document uses particular formatting to point out special facts and to warn you of poten-tial issues. The icons introducing the following notes let you see what kind of information can be expected:Furthermore, the following formatting is used:▪Text appearing in (drop-down) menus (such as Open…, Save as… etc.) in the software and paths to locations on your hard disk or other storage devices is printed in italics.▪Text appearing elsewhere (labels of buttons, controls, text next to checkboxes etc.) in the software is printed in blue. Whenever you see this formatting applied, you will find the same text appearing somewhere on the screen.▪Text appearing on the displays of the controller is printed in light grey. Whenever you see this formatting applied, you will find the same text on a controller display.▪Text appearing on labels of the hardware controller is printed in orange. Whenever you see this formatting applied, you will find the same text on the controller.▪Important names and concepts are printed in bold.▪References to keys on your computer’s keyboard you’ll find put in square brackets (e.g.,“Press [Shift] + [Enter]”).►Single instructions are introduced by this play button type arrow.→Results of actions are introduced by this smaller arrow.Naming ConventionThroughout the documentation we will refer to MASCHINE controller (or just controller) as the hardware controller and MASCHINE software as the software installed on your computer.The term “effect” will sometimes be abbreviated as “FX” when referring to elements in the MA-SCHINE software and hardware. These terms have the same meaning.Button Combinations and Shortcuts on Your ControllerMost instructions will use the “+” sign to indicate buttons (or buttons and pads) that must be pressed simultaneously, starting with the button indicated first. E.g., an instruction such as:“Press SHIFT + PLAY”means:1.Press and hold SHIFT.2.While holding SHIFT, press PLAY and release it.3.Release SHIFT.Unlabeled Buttons on the ControllerThe buttons and knobs above and below the displays on your MASCHINE controller do not have labels.。
Stata 统计软件：biprobit 双因变量回归分析的 postestimation 工具说明书
Title biprobit postestimation—Postestimation tools for biprobitPostestimation commands predict margins Also seePostestimation commandsThe following postestimation commands are available after biprobit:Command Descriptioncontrast contrasts and ANOV A-style joint tests of estimatesestat ic Akaike’s,consistent Akaike’s,corrected Akaike’s,and Schwarz’s Bayesian in-formation criteria(AIC,CAIC,AIC c,and BIC)estat summarize summary statistics for the estimation sampleestat vce variance–covariance matrix of the estimators(VCE)estat(svy)postestimation statistics for survey dataestimates cataloging estimation resultsetable table of estimation results∗hausman Hausman’s specification testlincom point estimates,standard errors,testing,and inference for linear combinations ofcoefficients∗lrtest likelihood-ratio testmargins marginal means,predictive margins,marginal effects,and average marginal effects marginsplot graph the results from margins(profile plots,interaction plots,etc.)nlcom point estimates,standard errors,testing,and inference for nonlinear combinationsof coefficientspredict probabilities for joint,marginal,and conditional outcomespredictnl point estimates,standard errors,testing,and inference for generalized predictions pwcompare pairwise comparisons of estimatessuest seemingly unrelated estimationtest Wald tests of simple and composite linear hypothesestestnl Wald tests of nonlinear hypotheses∗hausman and lrtest are not appropriate with svy estimation results.12biprobit postestimation—Postestimation tools for biprobitpredictDescription for predictpredict creates a new variable containing predictions such as probabilities,linear predictions, and standard errors.Menu for predictStatistics>PostestimationSyntax for predictpredicttypenewvarifin,statistic nooffsetpredicttypestub*ifin,scoresstatistic DescriptionMainp11Φ2(x j b,z j g,ρ),predicted probability Pr(y1j=1,y2j=1);the defaultp10Φ2(x j b,−z j g,−ρ),predicted probability Pr(y1j=1,y2j=0)p01Φ2(−x j b,z j g,−ρ),predicted probability Pr(y1j=0,y2j=1)p00Φ2(−x j b,−z j g,ρ),predicted probability Pr(y1j=0,y2j=0)pmarg1Φ(x j b),marginal success probability for equation1pmarg2Φ(z j g),marginal success probability for equation2pcond1Φ2(x j b,z j g,ρ)/Φ(z j g),conditional probability of success for equation1pcond2Φ2(x j b,z j g,ρ)/Φ(x j b),conditional probability of success for equation2 xb1x j b,linear prediction for equation1xb2z j g,linear prediction for equation2stdp1standard error of the linear prediction for equation1stdp2standard error of the linear prediction for equation2whereΦ(·)is the standard normal-distribution function andΦ2(·)is the bivariate standardnormal-distribution function.These statistics are available both in and out of sample;type predict...if e(sample)...if wanted only for the estimation sample.Options for predict££Main p11,the default,calculates the bivariate predicted probability Pr(y1j=1,y2j=1).p10calculates the bivariate predicted probability Pr(y1j=1,y2j=0).p01calculates the bivariate predicted probability Pr(y1j=0,y2j=1).biprobit postestimation—Postestimation tools for biprobit3p00calculates the bivariate predicted probability Pr(y1j=0,y2j=0).pmarg1calculates the univariate(marginal)predicted probability of success Pr(y1j=1).pmarg2calculates the univariate(marginal)predicted probability of success Pr(y2j=1).pcond1calculates the conditional(on success in equation2)predicted probability of success Pr(y1j=1,y2j=1)/Pr(y2j=1).pcond2calculates the conditional(on success in equation1)predicted probability of success Pr(y1j=1,y2j=1)/Pr(y1j=1).xb1calculates the probit linear prediction x j b.xb2calculates the probit linear prediction z j g.stdp1calculates the standard error of the linear prediction for equation1.stdp2calculates the standard error of the linear prediction for equation2.nooffset is relevant only if you specified offset1(varname)or offset2(varname)for biprobit.It modifies the calculations made by predict so that they ignore the offset variables;the linear predictions are treated as x j b rather than as x j b+offset1j and z jγrather than as z jγ+offset2j. scores calculates equation-level score variables.Thefirst new variable will contain∂ln L/∂(x jβ).The second new variable will contain∂ln L/∂(z jγ).The third new variable will contain∂ln L/∂(atanhρ).4biprobit postestimation—Postestimation tools for biprobitmarginsDescription for marginsmargins estimates margins of response for probabilities and linear predictions.Menu for marginsStatistics>PostestimationSyntax for marginsmarginsmarginlist,optionsmarginsmarginlist,predict(statistic...)predict(statistic...)...optionsstatistic Descriptionp11Φ2(x j b,z j g,ρ),predicted probability Pr(y1j=1,y2j=1);the default p10Φ2(x j b,−z j g,−ρ),predicted probability Pr(y1j=1,y2j=0)p01Φ2(−x j b,z j g,−ρ),predicted probability Pr(y1j=0,y2j=1)p00Φ2(−x j b,−z j g,ρ),predicted probability Pr(y1j=0,y2j=0)pmarg1Φ(x j b),marginal success probability for equation1pmarg2Φ(z j g),marginal success probability for equation2pcond1Φ2(x j b,z j g,ρ)/Φ(z j g),conditional probability of success for equation1 pcond2Φ2(x j b,z j g,ρ)/Φ(x j b),conditional probability of success for equation2 xb1x j b,linear prediction for equation1xb2z j g,linear prediction for equation2stdp1not allowed with marginsstdp2not allowed with marginsStatistics not allowed with margins are functions of stochastic quantities other than e(b).For the full syntax,see[R]margins.Also see[R]biprobit—Bivariate probit regression[U]20Estimation and postestimation commandsStata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organizationof the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.®。
美瑞 М1500数字压力传感器手册说明书
DigitalPressure TransmitterM1500 + Meriam = Performance you can rely on!Meriam’s compact, M1500 Analog or Digital Pressure Transmitters are ideal for pressure measurement needs from 10” H 20 to 3000 PSI Full Scale. Output options include digital (RS-232, RS-485 and USB) or analog (mA or V DC). Choose from differential (dry/dry or wet/wet), gauge, compound or absolute pressure types – see the table below for complete details. Typical NIST traceable digital accu-racy is ±0.025% of F.S. including all effects of linearity, repeatability, hyster-esis and temperature from -20º to +50º C (-4º to +122º F).Digital output options for the M1500 deliver accurate pressure measure-ment to compatible receiving devices. Choose RS-232 single point or RS-485 for networks up to 255 devices. M1500 connections are made via terminal block or DB-9 serial port. The USB digital communication option also pow-ers the M1500 via high power USB ports or powered USB hubs.Software is included for initial setup and support. Meriam Serial Protocol (MSP) or Meriam DLL can be used to send and receive information. Implementation Guides are available at (see Resources / Application Notes).Analog output models are also avail-able. Output accuracy is typically ±0.035% of F.S. The user may config-ure the M1500 for 0 – 5 VDC or 4 – 20mA output. Two SPST opto switches are included. Analog units are config-ured using a USB Mini A Connector and the included Meriam Setup Utility. The utility supports initial configura-tion, zeroing, recalibration and other functions.For wider pressure range require-ments, model M1502 with digital out-put incorporates two pressure sen-sors. Most combinations of AI or CI type pressure sensors are supported. See the table below for available rang-es. M1502 is available with RS-232, RS-485 or USB digital outputs - not available with analog output. A single software command returns both pres-sure readings. The M1502 reduces purchase price and installation costs when multiple pressure measurements are needed.(Orifice plate, Venturi, Accutube, Wedge)• Clean room pressure monitoringStandard AccessoriesP/N Z9A000003PN06 Support Disk including: - Meriam Setup Utility- Meriam Serial Protocol Implementation Guide - USB Device Drivers & Installation Instructions - LabVIEW® VI’s- Meriam DLLOptional AccessoriesP/N Z9P273Analog Starter Kit, 6 ft. USB cablefor configuration through PCsoftwareP/N ZA900447-00052 DB-9 connector cable, 6ft., female by maleP/N Z7621RS-485 to RS-232 interfaceadapter, DIN rail mountedP/N Z7621-1 RS-485 to RS-232 interfaceadapter, PC port mounted andpoweredLabVIEW® is a registered trademark of National Instruments.Viton® is a registered trademark of DuPont.SPECIFICATIONS: BEST IN CLASS ACCURACY EasyDIN Rail MountingWith 100 years of pressure measurementexperience Meriam is the clear choice. We set the standard for high accuracy over the largest operating temperature range.One or Two Pressure Sensors in Each UnitM1500-“Sensor Code with Range” shown below -or-mattersM1500 CONNECTION OPTIONSRS-232 / RS-485 communication connectorsUSB type B connectorOutput OptionsDigital (use Meriam Serial Protocol or LabView ®)RS-232: 19200 baud (adjustable), 8 data bits, 1 stop bit, no parity RS-485: half duplex, 3-wire TR-1, TR-0, signal ground, 19200 baud (adjustable), 1 start bit, 1 stop bit, no parity. Multi-dropaddressing for up to 255 devices (MSP).USB: USB 2.0Analog4 – 20 mA: 2-wire loop powered, 4-wire systems. 0 –5 VDC Analog: 4-wire, 1 – 5 VDC is user configurable Two SPST opto switches, 80V DC, 100 mA maximum, 4-wiresystems onlyConnectionsPressure: 1/8” NPT (female)Electrical / Communications / Output:Power: 7 position terminal block; 1.3 mm diameter holes for 16 – 25gauge solid or stranded wireRS-232: DB-9 (female) Serial Connector or 7 position terminal block RS-485: 7 position terminal block USB: USB type B female connectorAnalog: Terminal blocks with USB Mini -A Connector for configuration and recalibrationPower RequirementsRS-232, RS-485: 8 – 36 VDC, 20 mA minimumUSB: high power (500 mA) USB port or USB hub (PC USB ports andUSB hubs with power adapters are typically high power)mA, 2-wire: 20 – 36 VDC, 50 mA minimum mA, 4-wire: 8 – 36 VDC, 50 mA minimum V, 4-wire: 8 – 36 VDC, 50 mA minimumPower ConsumptionRS-232: 110 mW (maximum)RS-485: 110 mW (maximum)EPI / THE EMBEDDED PRESSURE INSTRUMENTThe EPI™ is a fully compensated instrument capable of measuring applied pressures and providing an accurate and fully characterized digital output. The Meriam M1500 Pressure Transmitter, as well as other pressure products from Meriam, are powered by the EPI™. This ensures you are getting a highly-accurate device capable of ±0.025% FS including all effects of linearity, hysteresis, repeatability and temperature over specified operating temperature range. NIST traceable certification is standard. The EPI™ provides true 14 samples per second data rate of accurate pressure readings.po w e r e d byEPIUSB type B connectorEasy rear panel accessExamples: M1500-DN0415 = M1500, Differential Non-isolated, 0 - 415” H2O full scale measurement M1500-G I 1000 = M1500, Gauge I solated, 0 - 1000 PSIG full scale measurementM1500-C I 0100 = M1500, Compound I solated, -14.7 to +100 PSIG full scale measurement M1502-A I 0017-CI0100, Dual Pressure ModelConvenient Panel MountingAnalog connectorEnclosure - Analog Out: 4.725” L x 2.125” W x 1.25” H Digital Out: 4.625 ”L x 2.125” W x 1.25” HIP40, aluminum case, 316LSS pressure manifold Weight: 10.5 oz for DN, G I , C I or A I pressure types, 16 oz for D Itype, analog output adds 1 oz.Mounting: Panel and DIN rail mounting hardware are standard Temperature Limits Operating: -4 to 122°F (-20 to 50°C) Storage: -40 to 185°F (-40 to 85°C)Humidity Limits: 5 – 95% RHShock, Operating: 30 g, halve-sine, 11mSec pulse (tested in accordance with EC-60068-2-27)Vibration Sinusoidal, Operating: 2g peak acceleration at 5-500 Hz (tested in accordance with IEC-60068-2-6)Vibration Random, Operating: 6g rms acceleration at 5 to 2000 Hz (tested in accordance with IEC-60068-2-64) Certification:Firmware FeaturesProgramming features supported through Meriam Serial Protocol (MSP): pressure zero, reset factory zero, sensor damping, pressure units select, analog output span set (Upper Sensor Value, Lower Sensor Value), tare on / off, field recalibration, PROD (precision right of decimal), AROD (accuracy right of decimal), Get/Set baud rate.The following information commands are available through Meriam Serial Protocol (MSP ): serial number, pressure module class and type, firmware version, LSL (lower sensor limit), USL (upper sensor limit), instrument temperature, primary and secondary variable value, primary and secondary min/max.The following programming features are available via LabVIEW ® VI’s: pressure zero, sensor damping, pressure units select, PROD (precision right of decimal), AROD (accuracy right of decimal), Get / Set baud rate.The following information commands are supported via LabVIEW ® VI’s: model, s/n, description, tag no., asset no., firmware version, primary(and secondary if applicable) variable value(s), primary (and secondary if applicable) min/max value(s), instrument temperature.Field Recalibration: All M1500 transmitters can be recalibrated in the field using suitable reference standards and the utility sent with the product.Since 1911。
mt4中文帮助命令中文手册
MQL4 Reference MQL4命令手册（本手册采用Office2007编写）2010年2月目录MQL4 Reference (1)MQL4命令手册 (1)Basics基础 (12)Syntax语法 (12)Comments注释 (12)Identifiers标识符 (12)Reserved words保留字 (13)Data types数据类型 (13)Type casting类型转换 (14)Integer constants整数常量 (14)Literal constants字面常量 (14)Boolean constants布尔常量 (15)Floating-point number constants (double)浮点数常量（双精度） (15)String constants字符串常量 (15)Color constants颜色常数 (16)Datetime constants日期时间常数 (16)Operations & Expressions操作表达式 (17)Expressions表达式 (17)Arithmetical operations算术运算 (17)Assignment operation赋值操作 (17)Operations of relation操作关系 (18)Boolean operations布尔运算 (18)Bitwise operations位运算 (19)Other operations其他运算 (19)Precedence rules优先规则 (20)Operators操作符 (21)Compound operator复合操作符 (21)Expression operator表达式操作符 (21)Break operator终止操作符 (21)Continue operator继续操作符 (22)Return operator返回操作符 (22)Conditional operator if-else条件操作符 (23)Switch operator跳转操作符 (23)Cycle operator while循环操作符while (24)Cycle operator for循环操作符for (24)Functions函数 (25)Function call函数调用 (26)Special functions特殊函数 (27)Variables变量 (27)Local variables局部变量 (28)Formal parameters形式变量 (28)Static variables静态变量 (29)Global variables全局变量 (29)Defining extern variables外部定义变量 (30)Initialization of variables初始化变量 (30)External functions definition外部函数的定义 (30)Preprocessor预处理 (31)Constant declaration常量声明 (31)Controlling compilation编译控制 (32)Including of files包含文件 (32)Importing of functions导入功能 (33)Standard constants标准常数 (35)Series arrays系列数组 (35)Timeframes图表周期时间 (35)Trade operations交易操作 (36)Price constants价格常数 (36)MarketInfo市场信息识别符 (36)Drawing styles画线风格 (37)Arrow codes预定义箭头 (38)Wingdings宋体 (39)Web colors颜色常数 (39)Indicator lines指标线 (40)Ichimoku Kinko Hyo (41)Moving Average methods移动平均方法 (41)MessageBox信息箱 (41)Object types对象类型 (43)Object properties对象属性 (44)Object visibility (45)Uninitialize reason codes撤销初始化原因代码 (45)Special constants特别常数 (46)Error codes错误代码 (46)Predefined variables预定义变量 (50)Ask最新卖价 (50)Bars柱数 (50)Bid最新买价 (50)Close[]收盘价 (51)Digits汇率小数位 (51)High[]最高价 (51)Low[]最低价 (52)Open[]开盘价 (53)Point点值 (53)Time[]开盘时间 (53)Volume[]成交量 (54)Program Run程序运行 (56)Program Run程序运行 (56)Imported functions call输入函数调用 (57)Runtime errors运行错误 (57)Account information账户信息 (68)AccountBalance( )账户余额 (68)AccountCredit( )账户信用点数 (68)AccountCompany( )账户公司名 (68)AccountCurrency( )基本货币 (68)AccountEquity( )账户资产净值 (68)AccountFreeMargin( )账户免费保证金 (69)AccountFreeMarginCheck()账户当前价格自由保证金 (69)AccountFreeMarginMode( )账户免费保证金模式 (69)AccountLeverage( )账户杠杆 (69)AccountMargin( )账户保证金 (69)AccountName( )账户名称 (70)AccountNumber( )账户数字 (70)AccountProfit( )账户利润 (70)AccountServer( )账户连接服务器 (70)AccountStopoutLevel( )账户停止水平值 (70)AccountStopoutMode( )账户停止返回模式 (71)Array functions数组函数 (72)ArrayBsearch()数组搜索 (72)ArrayCopy()数组复制 (72)ArrayCopyRates()数组复制走势 (73)ArrayCopySeries()数组复制系列走势 (74)ArrayDimension()返回数组维数 (75)ArrayGetAsSeries()返回数组序列 (75)ArrayInitialize()数组初始化 (75)ArrayIsSeries()判断数组连续 (75)ArrayMaximum()数组最大值定位 (76)ArrayMinimum()数组最小值定位 (76)ArrayRange()返回数组指定维数数量 (76)ArrayResize()改变数组维数 (77)ArraySetAsSeries()设定系列数组 (77)ArraySize()返回数组项目数 (78)ArraySort()数组排序 (78)Checkup检查 (79)GetLastError( )返回最后错误 (79)IsConnected( )返回联机状态 (79)IsDemo( )返回模拟账户 (79)IsDllsAllowed( )返回dll允许调用 (80)IsExpertEnabled( )返回智能交易开启状态 (80)IsLibrariesAllowed( )返回数据库函数调用 (80)IsOptimization( )返回策略测试中优化模式 (81)IsStopped( )返回终止业务 (81)IsTesting( )返回测试模式状态 (81)IsTradeAllowed( )返回允许智能交易 (81)IsTradeContextBusy( )返回其他智能交易忙 (82)IsVisualMode( )返回智能交易“图片模式” (82)UninitializeReason( )返回智能交易初始化原因 (82)Client terminal客户端信息 (83)TerminalCompany( )返回客户端所属公司 (83)TerminalName( )返回客户端名称 (83)TerminalPath( )返回客户端文件路径 (83)Common functions常规命令函数 (84)Alert弹出警告窗口 (84)Comment显示信息在走势图左上角 (84)GetTickCount获取时间标记 (84)MarketInfo在市场观察窗口返回不同数据保证金列表 (85)MessageBox创建信息窗口 (85)PlaySound播放声音 (86)Print窗口中显示文本 (86)SendFTP设置FTP (86)SendMail设置Email (87)Sleep指定的时间间隔内暂停交易业务 (87)Conversion functions格式转换函数 (88)CharToStr字符转换成字符串 (88)DoubleToStr双精度浮点转换成字符串 (88)NormalizeDouble给出环绕浮点值的精确度 (88)StrToDouble字符串型转换成双精度浮点型 (89)StrToInteger字符串型转换成整型 (89)StrToTime字符串型转换成时间型 (89)TimeToStr时间类型转换为"yyyy.mm.dd hh:mi"格式 (89)Custom indicators自定义指标 (91)IndicatorBuffers (91)IndicatorCounted (92)IndicatorDigits (92)IndicatorShortName (93)SetIndexArrow (94)SetIndexBuffer (94)SetIndexDrawBegin (95)SetIndexEmptyValue (95)SetIndexLabel (96)SetIndexShift (97)SetIndexStyle (98)SetLevelStyle (98)SetLevelValue (99)Date & Time functions日期时间函数 (100)Day (100)DayOfWeek (100)Hour (100)Minute (101)Month (101)Seconds (101)TimeCurrent (101)TimeDay (102)TimeDayOfWeek (102)TimeDayOfYear (102)TimeHour (102)TimeLocal (102)TimeMinute (103)TimeMonth (103)TimeSeconds (103)TimeYear (103)Year (104)File functions文件函数 (105)FileClose关闭文件 (105)FileDelete删除文件 (105)FileFlush将缓存中的数据刷新到磁盘上去 (106)FileIsEnding文件结尾 (106)FileIsLineEnding (107)FileOpen打开文件 (107)FileOpenHistory历史目录中打开文件 (108)FileReadArray将二进制文件读取到数组中 (108)FileReadDouble从文件中读取浮点型数据 (109)FileReadInteger从当前二进制文件读取整形型数据 (109)FileReadNumber (109)FileReadString从当前文件位置读取字串符 (110)FileSeek文件指针移动 (110)FileSize文件大小 (111)FileTell文件指针的当前位置 (111)FileWrite写入文件 (112)FileWriteArray一个二进制文件写入数组 (112)FileWriteDouble一个二进制文件以浮动小数点写入双重值 (113)FileWriteInteger一个二进制文件写入整数值 (113)FileWriteString当前文件位置函数写入一个二进制文件字串符 (114)Global variables全局变量 (115)GlobalVariableCheck (115)GlobalVariableDel (115)GlobalVariableGet (115)GlobalVariableName (116)GlobalVariableSet (116)GlobalVariableSetOnCondition (116)GlobalVariablesTotal (117)Math & Trig数学和三角函数 (119)MathAbs (119)MathArccos (119)MathArcsin (119)MathArctan (120)MathCeil (120)MathCos (120)MathExp (121)MathFloor (121)MathLog (122)MathMax (122)MathMin (122)MathMod (122)MathPow (123)MathRand (123)MathRound (123)MathSin (124)MathSqrt (124)MathSrand (124)MathTan (125)Object functions目标函数 (126)ObjectCreate建立目标 (126)ObjectDelete删除目标 (127)ObjectDescription目标描述 (127)ObjectFind查找目标 (127)ObjectGet目标属性 (128)ObjectGetFiboDescription斐波纳契描述 (128)ObjectGetShiftByValue (128)ObjectGetValueByShift (129)ObjectMove移动目标 (129)ObjectName目标名 (129)ObjectsDeleteAll删除所有目标 (130)ObjectSet改变目标属性 (130)ObjectSetFiboDescription改变目标斐波纳契指标 (131)ObjectSetText改变目标说明 (131)ObjectsTotal返回目标总量 (131)ObjectType返回目标类型 (132)String functions字符串函数 (133)StringConcatenate字符串连接 (133)StringFind字符串搜索 (133)StringGetChar字符串指定位置代码 (133)StringLen字符串长度 (134)StringSubstr提取子字符串 (134)StringTrimLeft (135)StringTrimRight (135)Technical indicators技术指标 (136)iAC比尔.威廉斯的加速器或减速箱振荡器 (136)iAD离散指标 (136)iAlligator比尔・威廉斯的鳄鱼指标 (136)iADX移动定向索引 (137)iATR平均真实范围 (137)iAO比尔.威廉斯的振荡器 (138)iBearsPower熊功率指标 (138)iBands保力加通道技术指标 (138)iBandsOnArray保力加通道指标 (139)iBullsPower牛市指标 (139)iCCI商品通道索引指标 (139)iCCIOnArray商品通道索引指标 (140)iCustom指定的客户指标 (140)iDeMarker (140)iEnvelopes包络指标 (141)iEnvelopesOnArray包络指标 (141)iForce强力索引指标 (142)iFractals分形索引指标 (142)iGator随机震荡指标 (142)iIchimoku (143)iBWMFI比尔.威廉斯市场斐波纳契指标 (143)iMomentum动量索引指标 (143)iMomentumOnArray (144)iMFI资金流量索引指标 (144)iMA移动平均指标 (144)iMAOnArray (145)iOsMA移动振动平均震荡器指标 (145)iMACD移动平均数汇总/分离指标 (146)iOBV能量潮指标 (146)iSAR抛物线状止损和反转指标 (146)iRSI相对强弱索引指标 (147)iRSIOnArray (147)iRVI相对活力索引指标 (147)iStdDev标准偏差指标 (148)iStdDevOnArray (148)iStochastic随机震荡指标 (148)iWPR威廉指标 (149)Timeseries access时间序列图表数据 (150)iBars柱的数量 (150)iClose (150)iHigh (151)iHighest (151)iLow (152)iLowest (152)iOpen (152)iTime (153)iVolume (153)Trading functions交易函数 (155)Execution errors (155)OrderClose (157)OrderCloseBy (158)OrderClosePrice (158)OrderCloseTime (158)OrderComment (159)OrderCommission (159)OrderDelete (159)OrderExpiration (160)OrderLots (160)OrderMagicNumber (160)OrderModify (160)OrderOpenPrice (161)OrderOpenTime (161)OrderPrint (162)OrderProfit (162)OrderSelect (162)OrderSend (163)OrdersHistoryTotal (164)OrderStopLoss (164)OrdersTotal (164)OrderSwap (165)OrderSymbol (165)OrderTakeProfit (165)OrderTicket (166)OrderType (166)Window functions窗口函数 (167)HideTestIndicators隐藏指标 (167)Period使用周期 (167)RefreshRates刷新预定义变量和系列数组的数据 (167)Symbol当前货币对 (168)WindowBarsPerChart可见柱总数 (168)WindowExpertName智能交易系统名称 (169)WindowFind返回名称 (169)WindowFirstVisibleBar第一个可见柱 (169)WindowHandle (169)WindowIsVisible图表在子窗口中可见 (170)WindowOnDropped (170)WindowPriceMax (170)WindowPriceMin (171)WindowPriceOnDropped (171)WindowRedraw (172)WindowScreenShot (172)WindowTimeOnDropped (173)WindowsTotal指标窗口数 (173)WindowXOnDropped (173)WindowYOnDropped (174)Obsolete functions过时的函数 (175)MetaQuotes Language 4 (MQL4) 是一种新的内置型程序用来编写交易策略。
高中数学课程描述(英文)
Mathematics Course DescriptionMathematics course in middle school has two parts: compulsory courses and optional courses. Compulsory courses content lots of modern mathematical knowledge and conceptions, such as calculus, statistics, analytic geometry, algorithm and vector. Optional courses are chosen by students which is according their interests.Compulsory Courses:Set TheoryCourse content:This course introduces a new vocabulary and set of rules that is foundational to the mathematical discussions. Learning the basics of this all-important branch of mathematics so that students are prepared to tackle and understand the concept of mathematical functions. Students learn about how entities are grouped into sets and how to conduct various operations of sets such as unions and intersections (i.e. the algebra of sets). We conclude with a brief introduction to the relationship between functions and sets to set the stage for the next stepKey Topics:➢The language of set theory➢Set membership➢Subsets, supersets, and equality➢Set theory and functionsFunctionsCourse content:This lesson begins with talking about the role of functions and look at the concept of mapping values between domain and range. From there student spend a good deal of time looking at how to visualize various kinds of functions using graphs. This course will begin with the absolute value function and then move on to discuss both exponential and logarithmic functions. Students get an opportunity to see how these functions can be used to model various kinds of phenomena. Key Topics:➢Single-variable functions➢Two –variable functions➢Exponential function➢ Logarithmic function➢Power- functionCalculusCourse content:In the first step, the course introduces the conception of limit, derivative and differential. Then students can fully understand what is limit of number sequence and what is limit of function through some specific practices. Moreover, the method to calculate derivative is also introduced to students.Key Topics:➢Limit theory➢Derivative➢DifferentialAlgorithmCourse content:Introduce the conception of algorithm and the method to design algorithm. Then the figures of flow charts and the conception of logical structure, like sequential structure, contracture of condition and cycle structure are introduced to students. Next step students can use theknowledge of algorithm to make simple programming language, during this procedure, student also approach to grammatical rules and statements which is as similar as BASIC language.Key Topics:➢Algorithm➢Logical structure of flow chart and algorithm➢Output statement➢Input statement➢Assignment statementStatisticsCourse content:The course starts with basic knowledge of statistics, such as systematic sampling and group sampling. During the lesson students acquire the knowledge like how to estimate collectivity distribution according frequency distribution of samples, and how to compute numerical characteristics of collectivity by looking at numerical characteristics of samples. Finally, the relationship and the interdependency of two variables is introduced to make sure that students mastered in how to make scatterplot, how to calculate regression line, and what is Method of Square.Key Topics:➢Systematic sampling➢Group sampling➢Relationship between two variables➢Interdependency of two variablesBasic Trigonometry ICourse content:This course talks about the properties of triangles and looks at the relationship that exists between their internal angles and lengths of their sides. This leads to discussion of the most commonly used trigonometric functions that relate triangle properties to unit circles. This includes the sine, cosine and tangent functions. Students can use these properties and functions to solve a number of issues.Key Topics:➢Common Angles➢The polar coordinate system➢Triangles properties➢Right triangles➢The trigonometric functions➢Applications of basic trigonometryBasic Trigonometry IICourse content:This course will look at the very important inverse trig functions such as arcsin, arcos, and arctan, and see how they can be used to determine angle values. Students also learn core trig identities such as the reduction and double angle identities and use them as a means for deriving proofs. Key Topics:➢Derivative trigonometric functions➢Inverse trig functions➢Identities●Pythagorean identities●Reduction identities●Angle sum/Difference identities●Double-angle identitiesAnalytic Geometry ICourse content:This course introduces analytic geometry as the means for using functions and polynomials to mathematically represent points, lines, planes and ellipses. All of these concepts are vital in student’s mathematical development since they are used in rendering and optimization, collision detection, response and other critical areas. Students look at intersection formulas and distance formulas with respect to lines, points, planes and also briefly talk about ellipsoidal intersections. Key Topics:➢Parametric representation➢Parallel and perpendicular lines➢Intersection of two lines➢Distance from a point to a line➢Angles between linesAnalytic Geometry IICourse content:Students look at how analytic geometry plays an important role in a number of different areas of class design. Students continue intersection discussion by looking at a way to detect collision between two convex polygons. Then students can wrap things up with a look at the Lambertian Diffuse Lighting model to see how vector dot products can be used to determine the lighting and shading of points across a surface.Key Topics:➢Reflections➢Polygon/polygon intersection➢LightingSequence of NumberCourse content:This course begin with introducing several conceptions of sequence of number, such as, term, finite sequence of number, infinite sequence of number, formula of general term and recurrence formula. Then, the conception of geometric sequence and arithmetic sequence is introduced to students. Through practices and mathematical games, students gradually understand and utilize the knowledge of sequence of number, eventually students are able to solve mathematical questions.Key Topics:➢Sequence of number➢Geometric sequence➢Arithmetic sequenceInequalityThis course introduces conception of inequality as well as its properties. In the following lessons students learn the solutions and arithmetic of one-variable quadratic inequality, two variables inequality, fundamental inequality as well how to solve simple linear programming problems.Key Topics:➢Unequal relationship and Inequality➢One-variable quadratic inequality and its solution➢Two-variable inequality and linear programming➢Fundamental inequalityVector MathematicsCourse content:After an introduction to the concept of vectors, students look at how to perform various important mathematical operations on them. This includes addition and subtraction, scalar multiplication, and the all-important dot and cross products. After laying this computational foundation, students engage in games and talk about their relationship with planes and the plane representation, revisit distance calculations using vectors and see how to rotate and scale geometry using vector representations of mesh vertices.Key Topics:➢Linear combinations➢Vector representations➢Addition/ subtraction➢Scalar multiplication/ division➢The dot product➢Vector projection➢The cross productOptional CoursesMatrix ICourse content:In this course, students are introduced to the concept of a matrix like vectors, matrices and so on. In the first two lessons, student look at matrices from a purely mathematical perspective. The course talks about what matrices are and what problems they are intended to solve and then looks at various operations that can be performed using them. This includes topics like matrix addition and subtraction and multiplication by scalars or by other matrices. At the end, students can conclude this course with an overview of the concept of using matrices to solve system of linear equations.Key Topics:➢Matrix relations➢Matrix operations●Addition/subtraction●Scalar multiplication●Matrix Multiplication●Transpose●Determinant●InversePolynomialsCourse content:This course begins with an examination of the algebra of polynomials and then move on to look at the graphs for various kinds of polynomial functions. The course starts with linear interpolation using polynomials that is commonly used to draw polygons on display. From there students are asked to look at how to take complex functions that would be too costly to compute in a relatively relaxed studying environment and use polynomials to approximate the behavior of the function to produce similar results. Students can wrap things up by looking at how polynomials can be used as means for predicting the future values of variables.Key Topics:➢Polynomial algebra ( single variable)●addition/subtraction●multiplication/division➢Quadratic equations➢Graphing polynomialsLogical Terms in MathematicsCourse content:This course introduces the relationships of four kinds of statements, necessary and sufficient conditions, basic logical conjunctions, existing quantifier and universal quantifier. By learning mathematical logic terms, students can be mastered in the usage of common logical terms and can self-correct logical mistakes. At the end of this course, students can deeply understand the mathematical expression is not only accurate but also concise.Key Topics:➢Statement and its relationship➢Necessary and sufficient conditions➢Basic logical conjunctions➢Existing quantifier and universal quantifierConic Sections and EquationCourse content:By using the knowledge of coordinate method which have been taught in the lesson of linear and circle, in this lesson students learn how to set an equation according the character of conic sections. Students is able to find out the property of conic sections during establishing equations. The aim of this course is to make students understand the idea of combination of number and shape by using the method of coordinate to solve simple geometrical problems which are related to conic sections.Key Topics:➢Curve and equation➢Oval➢Hyperbola➢Parabola。
CASTEP关键词手册
tion of the second derivatives
of the GGA functions.
Pseudopotentials
S pspot_nonlocal_type This defines the representa-
tion (real or reciprocal space) used for application of the
length unit mass unit time unit
charge unit energy unit force unit velocity unit pressure unit inv length unit frequency unit force constant unit volume unit
energy_unit
The unit of energy for output.
force_unit velocity_unit
The unit of force for output. The unit of velocity for output.
pressure_unit
The unit of pressure for output.
volume_unit
The unit of volume for output.
Table 1.1: Parameter file keywords controlling general parameters. Argument types are represented by, I for a integer, R for a real number, P for a physical value, L for a logical value, D for a keyword that may simply be defined (present) or not, and S for a text string.
索莱宝 repliQa HiFi ToughMix 使用说明书
repliQa HiFi ToughMix ®DescriptionThe repliQa HiFi ToughMix is a unique, next generation 2x master mix that has 90x higher fidelity compared to Taq. The ToughMix has extreme speed, with extension times as fast as 1-10 kb/sec depending on target length. Additionally, the ToughMix has long range amplification properties as it can amplify hgDNA targets up to 24 kb and lambda targets up to 40kb.The ToughMix is formulated with a genetically modified DNA polymerase coupled with hot start antibodies. It has 5’ → 3’ polymerase activity, 3’ → 5’ exonuclease activity, and generates blunt-ended products while providing the ability to amplify through uracils and primers containing inosines/uracils. It is tough tested, and is tolerant to multiple PCR inhibitors.ComponentsrepliQa HiFi ToughMix2x reaction buffer containing optimized concentrations of MgCl 2, dNTP ’s and proprietarily formulated HiFi polymerase, hot start antibodies and ToughMix chemistryStorage and StabilityStore kit components in a constant temperature freezer at -25°C to -15°C protected from light upon receipt. For lot specific expiry date, refer to package label, Certificate of Analysis or Product Specification Form.Guidelines for PCR▪ The design of highly specific primers is a critical parameter for successful PCR. The use of computer aided primer design programs is encouraged in order to minimize the potential for internal secondary structure. For best results, primer size should be limited to 22 - 35 bp with a melting point of at least 63°C. Ideal GC-content of the primers is 45-60%. A final concentration of 300 nM each primer is effective for most applications. Primers with inosine (dI) and uracil (dU) are acceptable.▪ Preparation of a reaction cocktail is recommended to reduce pipetting errors and maximize assay precision. Assemble the reaction cocktail with all required components except sample template and dispense equal aliquots into each reaction tube. Add the DNA template to each reaction as the final step. Addition of samples as 2 to 5 L volumes will improve assay precision.▪ Suggested input quantities of template are: genomic DNA ≤ 200 ng; plasmid DNA ≤ 50 ng ; cDNA ≤ 750 ng.▪ After sealing each reaction, vortex gently to mix contents. Centrifuge briefly to collect components at the bottom of the reaction tube.▪ Longer targets may require a higher primer melting temperature of at least 65°C, and a lower primer concentration of 150 nM.Reaction AssemblyCat No.95200-025 95200-100 95200-500Size: 25 x 25 µL reactions (1 x 0.625 mL)100 x 25 µL reactions (1 x 2.50 mL) 500 x 25 µL reactions (1 x 12.50 mL)Store at -25°C to - 15°CPCR Cycling ProtocolPCR cycling (25 - 45 cycles):*We recommend using 2-step cyclingfirst. If you are have having difficulty,we suggest trying 3-step cycling.Quality ControlKit components are free of contaminating DNase and RNase. 2x repliQa HiFi ToughMix is functionally tested for amplification of a 4-kb fragment from a single-copy gene in a human genomic DNA.Nuclease Assay:DNase: DNase activity must be below the detectable limits of 100 pg DNase I equivalent as assayed using a fluorogenic substrate following a 1 hour incubation at 37°C with each kit component at 1X concentration.RNase: RNase activity must be below the detectable limits of 1 pg RNase A equivalent as assayed using a fluorogenic substrate following a 1 hour incubation at 37°C with each kit component at 1X concentration.4.1 kb PCR Functional Assay: Negative control must be free of visible product with a single band at ~4.1 kb visible from 35 cycles of PCR using 20 ng human genomic DNA.Limited Label LicensesUse of this product signifies the agreement of any purchaser or user of the product to the following terms:1.The product may be used solely in accordance with the protocols provided with the product and this manual and for usewith components contained in the kit only. QIAGEN Beverly, Inc. grants no license under any of its intellectual property to use or incorporate the enclosed components of this kit with any components not included within this kit except as described in the protocols provided with the product, this manual, and additional protocols available at . Some of these additional protocols have been provided by Quantabio product users. These protocols have not been thoroughly tested or optimized by QIAGEN Beverly, Inc.. QIAGEN Beverly, Inc. neither guarantees them nor warrants that they do not infringe the rights of third-parties.2.Other than expressly stated licenses, QIAGEN Beverly, Inc. makes no warranty that this kit and/or its use(s) do not infringethe rights of third-parties.3.This kit and its components are licensed for one-time use and may not be reused, refurbished, or resold.4.QIAGEN Beverly, Inc. specifically disclaims any other licenses, expressed or implied other than those expressly stated.5.The purchaser and user of the kit agree not to take or permit anyone else to take any steps that could lead to or facilitateany acts prohibited above. QIAGEN Beverly, Inc. may enforce the prohibitions of this Limited License Agreement in any Court, and shall recover all its investigative and Court costs, including attorney fees, in any action to enforce this Limited License Agreement or any of its intellectual property rights relating to the kit and/or its components.©2020 QIAGEN Beverly Inc. 100 Cummings Center Suite 407J Beverly, MA 01915Quantabio brand products are manufactured by QIAGEN, Beverly Inc.Intended for molecular biology applications. This product is not intended for the diagnosis, prevention or treatment of a disease.TrademarksToughMix is a registered trademark of QIAGEN Beverly, Inc.FEATURES AND BENEFITS:Fidelity of >90x wild type Taq2–3x faster PCR results with extension rates as fast as 1 kb/sec* Tough Tested − tolerant to a wide range of PCR inhibitors Superior yield and sensitivityAmplification of +24 kb gDNA and +40 kb λDNArepliQa HiFi ToughMixSuperior speed and inhibitor tolerance for DNA amplification requiring high fidelity* For fragments less than 1 kb in size.Speed (min)20H S Q P6040Speed (min)DESCRIPTION:The repliQa HiFi ToughMix is a 2x, ready-to-use solution that contains all the components for high fidelity PCR amplification, including a genetically modified DNA polymerase coupled with hot start antibodies.This unique, next generation master mix provides >90x higher fidelity compared to Taq, while reducing time to PCR results by 2–3x. The extreme speed is enabled by extension times as fast as 1–10 kb/sec depending on target length. The enzyme is coupled with the industry leading ToughMix which is tolerant to a wide variety of inhibitors making it suitable for routine PCR, cloning, amplicon sequencing and site directed mutagenesis.Extreme Speed: 2−3x faster resultsrepliQa HiFi ToughMix has very fast extension times, ranging from 1–10 kb/sec depending on the fragment size, which can significantly shorten the time to result.Tough Tested: Tolerant to a wide range of PCR inhibitorsrepliQa HiFi ToughMix is able to tolerate a wide range of common PCR inhibitors, allowing for amplification of crude or difficult PCR sample types.Superior Yield and SensitivityrepliQa HiFi ToughMix provides higher yield and sensitivity, highlighting the enzyme efficiency. Coupled with extreme amplification speed allows PCR products to be amplified earlier and detected at lower levels.H S Q P10510101010Ethanol (%)H S Q P1.00.5 1.0Guanidine (%)H S Q P534215Hematin (μM)H S Q P2.01.02222Hemoglobin (mg/ml)H S Q P15050100Heparin (ng/μl)H S Q P500300400200100Humic acid (pg/μl)H S Q P0.010.005SDS (%)H S Q P010005002507501000Xylan (ng/μl)Figure 2Strong Inhibitor Resistance. A 2 kb λ DNA template was amplified using each manufacturers recommended cycling conditions with different amountsof inhibitors. The experiment was run in duplicate.Figure 3Comparison of yield. A gDNA template was amplified with varyingGC-content and length targets using each manufacturers recommended cycling conditions. 8 different targets were used. The experiment was run in duplicate.Average yield (ng)0200400600800HPSQHPSQrepliQa HiFi ToughMix has the ability to amplify long fragments +24 kb gDNA and +40 kb λ DNA, further proving the versatility of this enzyme.Figure 4Comparison of efficiency. A gDNA template was amplified withvarying GC-content and length targets using each manufacturers recom-mended cycling conditions. 8 different targets were used. Ran in duplicate.Figure 5Long Range capabilities (gDNA). A range of 3.6 kb, 8.5 kb, 17.5 kb, and 24 kb gDNA templates were amplified with varying GC-content and lengthsusing each manufacturers recommended cycling conditions. The experiment was run in duplicate.H H Q Q P P 3.6 kbS SH H Q Q PP 8.5 kbS SH H Q Q P P 17.5 kbS SH H Q Q P P 24 kbS SHHH24 kbEf ciency (%)020604080100HPSQHPSQHHQQPP10 kbSSHHQQPP20 kb SSHHQQPP30 kbSSHHQQPP40 kb SSQuantabio products are intended for molecular biology applications. The products are not intended for the diagnosis, prevention or treatment of a disease.MK-SF-0034 REV 01 repliQa HiFi TM 0220duplicate.Consistent GC TolerancerepliQa HiFi ToughMix is able to amplify varying levels of GC-content targets (32%–70% GC-rich), further enabling superiorPCR performance.HHQQPP32% GC (900 bp)SSHHQQPP34% GC (800 bp)SSHHQQPP38% GC (900 bp)SSHHQQPP40% GC (1000 bp)SS。
heteroskedastic-robust standard errors
heteroskedastic-robust standard errorsHeteroskedastic-robust standard errors are a type of standard error that accounts for the heteroskedasticity of the data, meaning that the variance of the errors is not constant across the range of the data. This is a problem in many statistical models, particularly in regression analysis, where the variance of the errors can be systematically related to the level of the predictor variables.In such cases, the heteroskedastic-robust standard errors provide a more accurate estimate of the true standard error of the regression coefficient estimates. These standard errors are usually larger than the regular standard errors, which is due to the fact that they take into account the heteroskedasticity of the data.There are several ways to estimate heteroskedastic-robust standard errors, including the White method, the Huber-White method, and the Jackknife method. These methods differ in their assumptions and the way they estimate the standard errors, but they all provide a way to account for heteroskedasticity in the data.Overall, heteroskedastic-robust standard errors are an important tool for accurately estimating the standard errors of regression coefficients in the presence of heteroskedasticity.。
Belimo NMQ24A-MF 配置可调节风扇门驱动器说明书
NMQ24A-MFConfigurable damper actuator for adjustingdampers in technical building installations• Air damper size up to approx. 1.5 m²• Torque motor 8 Nm• Nominal voltage AC/DC 24 V• Control modulating 2...10 V variable• Position feedback 2...10 V variable• Running time motor 4 s variableTechnical dataElectrical data Nominal voltage AC/DC 24 VNominal voltage frequency50/60 HzNominal voltage range AC 19.2...28.8 V / DC 21.6...28.8 VPower consumption in operation13 WPower consumption in rest position 2 WPower consumption for wire sizing23 VAPower consumption for wire sizing note Imax 20 A @ 5 msConnection supply / control Cable 1 m, 4x 0.75 mm²Parallel operation Yes (note the performance data)Functional data Torque motor8 NmTorque variable25%, 50%, 75% reducedOperating range Y 2...10 VInput impedance100 kΩOperating range Y variable Start point 0.5...30 VEnd point 2.5...32 VOperating modes optional Open/closeModulating (DC 0...32 V)Position feedback U 2...10 VPosition feedback U note Max. 0.5 mAPosition feedback U variable Start point 0.5...8 VEnd point 2.5...10 VPosition accuracy±5%Direction of motion motor selectable with switch 0/1Direction of motion note Y = 0 V: At switch position 0 (ccw rotation) /1 (cw rotation)Direction of motion variable electronically reversibleManual override with push-button, can be lockedAngle of rotation Max. 95°Angle of rotation note can be limited on both sides with adjustablemechanical end stopsMinimum angle of rotation Min. 30°Running time motor4 s / 90°Running time motor variable 4...20 sAdaptation setting range manual (automatic on first power-up)NMQ24A-MFTechnical dataFunctional data Adaptation setting range variable No actionAdaptation when switched onAdaptation after pushing the manual overridebuttonOverride control MAX (maximum position) = 100%MIN (minimum position) = 0%ZS (intermediate position, AC only) = 50%Override control variable MAX = (MIN + 32%)...100%MIN = 0%...(MAX – 32%)ZS = MIN...MAXSound power level, motor56 dB(A)Mechanical interface Universal shaft clamp 8...26.7 mmPosition indication Mechanical, pluggableSafety data Protection class IEC/EN III, Safety Extra-Low Voltage (SELV)Power source UL Class 2 SupplyDegree of protection IEC/EN IP54Degree of protection NEMA/UL NEMA 2Enclosure UL Enclosure Type 2EMC CE according to 2014/30/EULow voltage directive CE according to 2006/95/ECCertification IEC/EN IEC/EN 60730-1 and IEC/EN 60730-2-14UL Approval cULus according to UL60730-1A, UL60730-2-14and CAN/CSA E60730-1The UL marking on the actuator depends onthe production site, the device is UL-compliantin any caseHygiene test According to VDI 6022 Part 1 / SWKI VA104-01, cleanable and disinfectable, lowemissionType of action Type 1Rated impulse voltage supply / control0.8 kVPollution degree3Ambient humidity Max. 95% RH, non-condensingAmbient temperature-30...40°C [-22...104°F]Ambient temperature note Caution: 40...50°C utilisation possible onlyunder certain restrictions. Please contact yoursupplier.Storage temperature-40...80°C [-40...176°F]Servicing maintenance-freeWeight Weight 1.1 kg••••••••This device has been designed for use in stationary heating, ventilation and air-conditioning systems and must not be used outside the specified field of application, especially in aircraft or in any other airborne means of transport.Outdoor application: only possible in case that no (sea) water, snow, ice, insolation or aggressive gases interfere directly with the device and that it is ensured that the ambient conditions remain within the thresholds according to the data sheet at any time.Only authorised specialists may carry out installation. All applicable legal or institutional installation regulations must be complied with during installation.The device may only be opened at the manufacturer's site. It does not contain any parts that can be replaced or repaired by the user.Cables must not be removed from the device.Self adaptation is necessary when the system is commissioned and after each adjustment of the angle of rotation (press the adaptation push-button once).To calculate the torque required, the specifications supplied by the damper manufacturers concerning the cross-section and the design, as well as the installation situation and the ventilation conditions must be observed.The device contains electrical and electronic components and must not be disposed of as household refuse. All locally valid regulations and requirements must be observed.Product featuresOperating modeThe actuator is connected with a standard control signal of 0...10 V and drives to the position defined by the control signal. Measuring voltage U serves for the electrical display of the damper position 0...100% and as control signal for other actuators.Parametrisable actuators The factory settings cover the most common applications. Single parameters can be modified with the Belimo service tools MFT-P or ZTH EU.Simple direct mountingSimple direct mounting on the damper shaft with a universal shaft clamp, supplied with an anti-rotation device to prevent the actuator from rotating.Manual overrideManual override with push-button possible (the gear train is disengaged for as long as the button is pressed or remains locked).Adjustable angle of rotation Adjustable angle of rotation with mechanical end stops. A minimum permissible angle of rotation of 30° must be allowed for.High functional reliabilityThe actuator is overload protected, requires no limit switches in intermediate positions and automatically stops when the end stop is reached (at rest).Home positionThe first time the supply voltage is switched on, i.e. at the time of commissioning, the actuator carries out an adaptation, which is when the operating range and position feedback adjust themselves to the mechanical setting range.The detection of the mechanical end stops enables a gentle approach to the end positions, thus protecting the actuator mechanics.The actuator then moves into the position defined by the control signal.Adaptation and synchronisationAn adaptation can be triggered manually by pressing the "Adaptation" button or with the PC-Tool. Both mechanical end stops are detected during the adaptation (entire setting range).Automatic synchronisation after pressing the manual override button is configured. The synchronisation is in the home position (0%).The actuator then moves into the position defined by the control signal.A range of settings can be adapted using the PC-Tool (see MFT-P documentation)Electrical accessories Description TypeAuxiliary switch 1x SPDT add-on S1AAuxiliary switch 2x SPDT add-on S2AFeedback potentiometer 140 Ω add-on P140AFeedback potentiometer 1 kΩ add-on P1000AFeedback potentiometer 10 kΩ add-on P10000AAdapter for auxiliary switch and feedback potentiometer, Multipack 20pcs.Z-SPASignal converter voltage/current 100 kΩ 4...20 mA, Supply AC/DC 24 V Z-UICPositioner for wall mounting SGA24Positioner for built-in mounting SGE24Positioner for front-panel mounting SGF24Positioner for wall mounting CRP24-B1 Mechanical accessories Description TypeActuator arm for standard shaft clamp (one-sided)AH-25Shaft extension 240 mm ø20 mm for damper shaft ø8...22.7 mm AV8-25Shaft clamp one-sided, clamping range ø8...26 mm, Multipack 20 pcs.K-ENSAShaft clamp reversible, clamping range ø10...20 mm K-SAAnti-rotation mechanism 180 mm, Multipack 20 pcs.Z-ARS180Form fit insert 10x10 mm, Multipack 20 pcs.ZF10-NSAForm fit insert 12x12 mm, Multipack 20 pcs.ZF12-NSAForm fit insert 15x15 mm, Multipack 20 pcs.ZF15-NSAForm fit insert 16x16 mm, Multipack 20 pcs.ZF16-NSAPosition indicator, Multipack 20 pcs.Z-PIShaft clamp one-sided, clamping range ø8...26 mm with insert,Multipack 20 pcs.K-ENMAMounting kit for linkage operation for flat installation ZG-NMA Tools Description TypeService tool, with ZIP-USB function, for parametrisable and communicative Belimo actuators, VAV controller and HVAC performance devices ZTH EUBelimo PC-Tool, Software for adjustments and diagnostics MFT-P Adapter for Service-Tool ZTH MFT-CConnecting cable 5 m, A: RJ11 6/4 ZTH EU, B: 6-pin for connection to service socket ZK1-GENConnecting cable 5 m, A: RJ11 6/4 ZTH EU, B: free wire end forconnection to MP/PP terminalZK2-GEN* Adapter Z-SPAIt is imperative that this adapter will be ordered if an auxiliary switch or a feedback potentiometer is required and if at the same time the shaft clamp is installed on the rear side of the actuator (e.g. with short shaft installation).Wire colours:1 = black2 = red3 = white5 = orangeElectrical installationSupply from isolating transformer.Parallel connection of other actuators possible. Observe the performance data.NMQ24A-MFWiring diagramsAC/DC 24 V, modulatingSignal cable lengthsA = ActuatorC = Control unit (controlling unit)L1 = Connecting cable of the actuatorL2 = Customer cableLtot = Maximum signal cable lengthNote:When several actuators are connected in parallel, the maximum signal cable length must be divided by the numberof actuators.A = ActuatorC = Control unit (controlling unit)L1 = Connecting cable of the actuatorNote:There are no special restrictions on installation if the supply and the data cable are routed separately.Electrical installationNMQ24A-MFFunctionsFunctions with basic values (conventional mode)Override control with AC 24 V with relay contactsOverride control with AC 24 V with rotary switch Control remotely 0...100% with positioner SG..Minimum limit with positioner SG..Primary/secondary operation (position-dependent)Control with 4...20 mA via external resistorCaution:The operating range must be set to DC 2...10 V.The 500 Ohm resistor converts the 4...20 mA current signal to a voltage signal DC 2...10 V.NMQ24A-MFFunctions with basic values (conventional mode)Functional checkProcedure1. Connect 24 V to connections 1 and 22. Disconnect connection 3:– With direction of rotation 0: Actuator rotates to the left – With direction of rotation 1: Actuator rotates to the right3. Short-circuit connections 2 and 3:– Actuator runs in opposite direction Functions with specific parameters (Parametrisation necessary)Override control and limiting with AC 24 V with relay contactsControl open/closeOverride control and limiting with AC 24 V with rotary switchCaution:The "Close" function is only guaranteed if the start point of the operating range is defined as min. 0.5 V.FunctionsNMQ24A-MFOperating controls and indicators1Direction of rotation switch Switch over:Direction of rotation changes2Push-button and LED display green Off:No power supply or malfunction On:In operationPress button:Triggers angle of rotation adaptation, followed by standard mode3Push-button and LED display yellow Off:Standard modeOn:Adaptation or synchronisation process active Press button:No function4Manual override button Press button:Gear train disengages, motor stops, manual override possible Release button:Gear train engages, synchronisation starts, followed by standard mode5Service plugFor connecting parametrisation and service tools Check power supply connection 2 Off and 3 OnPossible wiring error in power supplyInstallation notesNegative torqueMax. 50% of the torque (Caution: Application possible only with restrictions. Please contact your supplier.)ServiceTool connectionThe actuator can be parametrised by ZTH EU via the service socket.For an extended parametrisation the PC tool can be connected.Connection ZTH EU / PC-ToolNMQ24A-MF••DimensionsSpindle lengthMin. 42Min. 20Clamping range*Option: Shaft clamp mounted below (accessory K-SA needed)*Option: Shaft clamp mounted below: If an auxiliary switch or a feedback potentiometer isused the adapter Z-SPA is required.。
A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM
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A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM
detected by a resolver or by an absolute encoder. However, these sensors are expensive and very sensitive to environmental constraints such as vibration and temperature [2]. To overcome these problems, instead of using position sensors, a sensorless control method has been developed for control of the motor using the estimated values of the position and velocity of the rotor [3]–[12]. In a conventional sliding-mode observer (SMO), a low-pass filter and an additional position compensation of the rotor are used to reduce the chattering problem commonly found in SMOs using a signum function. Currently, a sigmoid function is used for the SMO as a switching function. The observer has fast responses and has a robustness inherent in the design parameters [13], [14]. In this observer, the chattering, which happens at the observer using the signum function, has been reduced significantly [15]. The stator resistance needs to be estimated by the Lypunov function intermediate equations so as to prove the stability of the observer. The stator resistance changes during motor operation, which deteriorates the control performance, unless it is compensated for in real time [8]. The cascade control method has been proposed for the achievement of an accurate tracking performance [16], and high-gain observers have been designed to estimate the states under a scalar disturbance [17]. A hybrid terminal SMO design method is proposed to achieve sensorless drive for a PMSM [18], and a tuning method is proposed in order to obtain high-speed and high-accuracy positioning systems [19]. The general schemes of sliding-mode control have been well surveyed recently [20]. Most of the observer designs focus on the fast response and high tracking accuracy. For the fast response, the use of a sigmoid function in a boundary layer is popular. However, the observer error cannot be guaranteed to converge to zero within the boundary layer [18], [21], [22], [24]. This paper proposes a new sensorless control algorithm for a PMSM based on the new SMO which uses a sigmoid function as a switching function with variable boundary layers. Using this SMO, the position and velocity of the rotor can be calculated from the estimated back EMF [25]. Also, to overcome the sensitivity of the parameter variations in the sensorless control and to improve the steady-state performance, the stator resistance is estimated using an adaptive control scheme. The superiority of the proposed algorithm has been proved by comparison with the conventional SMO through real experiments. This paper consists of five sections, including the introduction. Section II introduces the conventional SMO, and Section III proposes the new SMO. Section IV illustrates the experimental
高三英语计算机语言单选题40题
高三英语计算机语言单选题40题1. When you are programming, you often need to use a(n) ______ to store data.A. algorithmB. variableC. functionD. loop答案：B。
本题考查计算机语言中的常见词汇。
选项A“algorithm”意为“算法”；选项B“variable”指“变量”，在编程中用于存储数据，符合题意；选项C“function”是“函数”；选项D“loop”是“循环”。
2. In computer programming, a(n) ______ is a set of instructions that tells the computer what to do.A. codeB. scriptC. commandD. syntax答案：A。
选项A“code”指“代码”，是一组指令；选项B“script”通常指“脚本”；选项C“command”意为“命令”；选项D“syntax”表示“语法”。
本题强调的是一组指令，所以选A。
3. Which of the following is NOT a type of programming language?A. PythonB. ExcelC. JavaD. C++答案：B。
选项A“Python”、选项C“Java”和选项D“C++”都是常见的编程语言；选项B“Excel”是电子表格软件，不是编程语言。
4. The process of finding and fixing errors in a program is called ______.A. debuggingB. compilingC. optimizingD. documenting答案：A。
“debugging”意为“调试”，即查找和修复程序中的错误；“compiling”是“编译”；“optimizing”指“优化”；“documenting”表示“文档化”。
SparseVB：变分贝叶斯算法为线性和逻辑回归的稀疏高维回归模型的稀疏变量选择说明书
Package‘sparsevb’October14,2022Type PackageTitle Spike-and-Slab Variational Bayes for Linear and LogisticRegressionVersion0.1.0Date2021-1-04Author Gabriel Clara[aut,cre],Botond Szabo[aut],Kolyan Ray[aut]Maintainer Gabriel Clara<*************************>Description Implements variational Bayesian algorithms to perform scalable variable selec-tion for sparse,high-dimensional linear and logistic regression models.Features in-clude a novel prioritized updating scheme,which uses a preliminary estimator of the varia-tional means during initialization to generate an updating order prioritizing large,more rele-vant,coefficients.Sparsity is induced via spike-and-slab priors with either Laplace or Gaus-sian slabs.By default,the heavier-tailed Laplace density is used.Formal derivations of the algo-rithms and asymptotic consistency results may be found in Kolyan Ray and Botond Sz-abo(2020)<doi:10.1080/01621459.2020.1847121>and Kolyan Ray,Botond Sz-abo,and Gabriel Clara(2020)<arXiv:2010.11665>.BugReports https:///gclara/varpack/-/issuesLicense GPL(>=3)Imports Rcpp(>=1.0.5),selectiveInference(>=1.2.5),glmnet(>=4.0-2),statsLinkingTo Rcpp,RcppArmadillo,RcppEnsmallenSystemRequirements C++11Encoding UTF-8RoxygenNote7.1.1NeedsCompilation yesRepository CRANDate/Publication2021-01-1509:20:02UTC12sparsevb-package R topics documented:sparsevb-package (2)svb.fit (3)Index6 sparsevb-package sparsevb:Spike-and-Slab Variational Bayes for Linear and LogisticRegressionDescriptionImplements variational Bayesian algorithms to perform scalable variable selection for sparse,high-dimensional linear and logistic regression models.Features include a novel prioritized updating scheme,which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large,more relevant,coefficients.Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs.By default,the heavier-tailed Laplace density is used.Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo(2020)<doi:10.1080/01621459.2020.1847121>and Kolyan Ray, Botond Szabo,and Gabriel Clara(2020)<arXiv:2010.11665>.DetailsFor details as they pertain to using the package,consult the svb.fit function help page.Detailed descriptions and derivations of the variational algorithms with Laplace slabs may be found in the references.Author(s)Maintainer:Gabriel Clara<*************************>Authors:•Botond Szabo•Kolyan RayReferences•Ray K.and Szabo B.Variational Bayes for high-dimensional linear regression with sparse priors.(2020).Journal of the American Statistical Association.•Ray K.,Szabo B.,and Clara G.Spike and slab variational Bayes for high dimensional logistic regression.(2020).Advances in Neural Information Processing Systems33.See AlsoUseful links:•Report bugs at https:///gclara/varpack/-/issuessvb.fit Fit Approximate Posteriors to Sparse Linear and Logistic ModelsDescriptionMain function of the sparsevb putes mean-field posterior approximations for both linear and logistic regression models,including variable selection via sparsity-inducing spike and slab priors.Usagesvb.fit(X,Y,family=c("linear","logistic"),slab=c("laplace","gaussian"),mu,sigma=rep(1,ncol(X)),gamma,alpha,beta,prior_scale=1,update_order,intercept=FALSE,noise_sd,max_iter=1000,tol=1e-05)ArgumentsX A numeric design matrix,each row of which represents a vector of covari-ates/independent variables/features.Though not required,it is recommendedto center and scale the columns to have norm sqrt(nrow(X)).Y An nrow(X)-dimensional response vector,numeric if family="linear"andbinary if family="logistic".family A character string selecting the regression model,either"linear"or"logistic".slab A character string specifying the prior slab density,either"laplace"or"gaussian".mu An ncol(X)-dimensional numeric vector,serving as initial guess for the varia-tional means.If omitted,mu will be estimated via ridge regression to initializethe coordinate ascent algorithm.sigma A positive ncol(X)-dimensional numeric vector,serving as initial guess for thevariational standard deviations.gamma An ncol(X)-dimensional vector of probabilities,serving as initial guess forthe variational inclusion probabilities.If omitted,gamma will be estimated viaLASSO regression to initialize the coordinate ascent algorithm.alpha A positive numeric value,parametrizing the beta hyper-prior on the inclusion probabilities.If omitted,alpha will be chosen empirically via LASSO regres-sion.beta A positive numeric value,parametrizing the beta hyper-prior on the inclusion probabilities.If omitted,beta will be chosen empirically via LASSO regres-sion.prior_scale A numeric value,controlling the scale parameter of the prior slab ed as the scale parameterλwhen prior="laplace",or as the standard deviationσif prior="gaussian".update_order A permutation of1:ncol(X),giving the update order of the coordinate-ascent algorithm.If omitted,a data driven updating order is used,see Ray and Szabo(2020)in Journal of the American Statistical Association for details.intercept A Boolean variable,controlling if an intercept should be included.NB:This feature is still experimental in logistic regression.noise_sd A positive numerical value,serving as estimate for the residual noise standard deviation in linear regression.If missing it will be estimated,see estimateSigmafrom the selectiveInference package for more details.Has no effect whenfamily="logistic".max_iter A positive integer,controlling the maximum number of iterations for the varia-tional update loop.tol A small,positive numerical value,controlling the termination criterion for max-imum absolute differences between binary entropies of successive iterates. DetailsSupposeθis the p-dimensional true parameter.The spike-and-slab prior forθmay be represented by the hierarchical schemew∼Beta(α,β),z j|w∼i.i.d.Bernoulli(w),θj|z j∼ind.(1−z j)δ0+z j g.Here,δ0represents the Dirac measure at0.The slab g may be taken either as a Laplace(0,λ)or N(0,σ2)density.The former has centered densityfλ(x)=λ2e−λ|x|.Givenαandβ,the beta hyper-prior has densityb(x|α,β)=xα−1(1−x)β−1 1tα−1(1−t)β−1d t.A straightforward integration shows that the prior inclusion probability of a coefficient isαα+β. ValueThe approximate mean-field posterior,given as a named list containing numeric vectors"mu", "sigma","gamma",and a value"intercept".The latter is set to NA in case intercept=FALSE.In mathematical terms,the conditional distribution of eachθj is given byθj|µj,σj,γj∼ind.γj N(µj,σ2)+(1−γj)δ0.Examples###Simulate a linear regression problem of size n times p,with sparsity level s### n<-250p<-500s<-5###Generate toy data###X<-matrix(rnorm(n*p),n,p)#standard Gaussian design matrixtheta<-numeric(p)theta[sample.int(p,s)]<-runif(s,-3,3)#sample non-zero coefficients in random locations pos_TR<-as.numeric(theta!=0)#true positivesY<-X%*%theta+rnorm(n)#add standard Gaussian noise###Run the algorithm in linear mode with Laplace prior and prioritized initialization### test<-svb.fit(X,Y,family="linear")posterior_mean<-test$mu*test$gamma#approximate posterior meanpos<-as.numeric(test$gamma>0.5)#significant coefficients###Assess the quality of the posterior estimates###TPR<-sum(pos[which(pos_TR==1)])/sum(pos_TR)#True positive rateFDR<-sum(pos[which(pos_TR!=1)])/max(sum(pos),1)#False discovery rateL2<-sqrt(sum((posterior_mean-theta)^2))#L_2-errorMSPE<-sqrt(sum((X%*%posterior_mean-Y)^2)/n)#Mean squared prediction errorIndexsparsevb,3sparsevb(sparsevb-package),2sparsevb-package,2svb.fit,2,36。
Establishment of a new peritrich ciliate genus,Pseudepistylis n.gen
Accepted by A. Wright: 23 May 2007; published: 9 Jul. 200735ZOOTAXAISSN 1175-5326 (print edition)ISSN 1175-5334(online edition)Copyright © 2007 · Magnolia PressZootaxa 1524: 35–45 (2007) /zootaxa /Establishment of a new peritrich ciliate genus, Pseudepistylis n. gen. (Ciliophora: Peritrichia: Epistylididae), with a description of a new freshwater species, Pseudepistylis songi n. sp. from Wenzhou, ChinaXIAOHUI PENG 1, XINLU SHI 1, 2, 4 & ALAN WARREN 31Hangzhou Key Laboratory for Animal Sciences and Technology, Hangzhou Normal University, Hangzhou 310036, China 2Life and Environment College, Harbin Normal University, Harbin 150080, China 3Department of Zoology, Natural History Museum, Cromwell Road, London SW7 5BD, UK 4Corresponding author. E-mail: shixl56@AbstractA new colonial sessilid peritrich genus, Pseudepistylis, is established. Pseudepistylis n. gen. belongs to the family Epi-stylididae, (i.e. colonial with a non-contractile stalk and peristomial disc not borne on a stalk) but has oral ciliature that makes less than two turns on the peristome before entering the infundibulum and a reticulate silverline system. Morphol-ogy, infraciliature and silverline system of the type species Pseudepistylis songi n . sp., isolated from a freshwater pond near Wenzhou, China, have been investigated both in vivo and following silver impregnation. The new species is charac-terized by small colony size of only one to eight zooids borne upon a slender, dichotomously branched stalk. Zooids are bell-shaped, in vivo approximately 220 × 140 μm, with wide peristomial lip that are invariably reflected aborally, single contractile vacuole at the dorsal wall of the infundibulum, and C-shaped macronucleus. The pellicle of contracted zooids shows several large, characteristic folds. The silverline system is reticulate with at least 137 silverlines between the peris-tomial area and the aboral ciliary wreath and approximately 77 between the aboral ciliary wreath and the scopula. The haplokinety and polykinety complete approximately one and one-quarter turns of the peristome before entering the infundibulum. Within the infundibulum the kineties of infundibular polykinety 2 (P2) terminate at different levels between P1 and P3. The myoneme system is also described.Key words: Taxonomy; infraciliature; morphology; silverline system; myoneme; colonyIntroductionSpecies identification of peritrich ciliates is extremely difficult for a variety of reasons: (1) comprising approximately 1,000 species, the subclass Peritrichia is one of the most speciose of all ciliate groups, and sev-eral genera (e.g. Epistylis Ehrenberg, 1830, Zoothamnium Bory de St. Vincent, 1826 and Vorticella Linnaeus,1767) contain more than 50 members; (2) many species are morphologically variable, but descriptions often fail to recognize this; (3) until comparatively recently, species descriptions were based almost exclusively on observations of live specimens (Kent 1880–1882; Kahl 1935), and data from the infraciliature and silverline system are therefore lacking for most; (4) few taxonomic revisions have been carried out in the last 20 years;consequently, descriptions of species are scattered throughout the literature; (5) stained preparations were rarely deposited in collections until recently, so type material is not available for most species. This situation was improved significantly when Foissner and Schiffmann (1974) outlined the basic criteria on which descriptions and redescriptions of peritrichs should be based. Extensive photographic documentation of live specimens and use of silver stains to reveal the infraciliature and silverline system were among their recom-mendations. These procedures have been increasingly applied by peritrich taxonomists in recent years (Ji et al. 2004; Leitner and Foissner, 1997; Song 1986, 1997; Song et al. 2002; Sun et al. 2006).Among the morphological features of peritrichs revealed by silver impregnation is the silverline system which occurs in two main patterns, transverse and reticulate. Foissner and Schiffmann (1974) established this as a generic character, following which at least two new colonial peritrich genera have been established based on their possession of a reticulate rather than transverse silverline system: Epicarchesium Jankowski, 1985 and Zoothamnopsis Song, 1997.In May 2005, a hitherto unknown epistylidid peritrich was isolated from a freshwater pond near Wenzhou, China, and was successfully cultivated in the laboratory. Subsequent observations demonstrated that this organism exhibits unusual characteristics that make it unlike any known genus of Epistylididae. Therefore, a new genus, Pseudepistylis n. gen., is established here for this organism, which is here described both from live and silver-stained specimens.Material and methodsSpecimens were collected on 12 May 2005 from a pond near Wenzhou, China (N 28°20.418’; E 120°43.817’). Organisms were attached to submerged rocks and hydrophytes near the edge of the pond and were detached using a pipette or scalpel blade. Initially, cultures were established at room temperature (~25 o C) using filtered water from the same pond. Long-term cultivation was carried out at 15–25 o C in mineral water enriched with boiled wheat grains and inoculated with Chilomastix sp. as a food organism.Observations of living specimens were conducted with bright field and phase contrast microscopy. The infraciliature and myoneme system were revealed by protargol impregnation according to the method of Shi and Frankel (1990). The ‘dry’ silver nitrate method was used to demonstrate the silverline system (Song and Wang 1995).Drawings of silver-stained specimens were made with the help of a drawing tube at 1,250× magnification. Drawings of live specimens were based on in vivo observations and on photomicrographs. Terminology is mainly according to Corliss (1979), Warren (1986), and Shi et al. (2004).ResultsClass: Oligohymenophora de Puytorac et al., 1974Subclass: Peritrichia Stein, 1859Order: Sessilida Kahl, 1933Family: Epistylididae Kahl, 1933Genus: Pseudepistylis n. gen.Species: Pseudepistylis songi n. sp.Genus Pseudepistylis n. gen.Diagnosis: Possessing diagnostic characteristics of the family Epistylididae but with reticulate rather than transverse silverline system; colonial, with branched non-contractile stalk; oral ciliary rows make less than two circuits on the peristome before entering the infundibulum.36·Zootaxa 1524 © 2007Magnolia PressPENG ET AL.Type species: Pseudepistylis songi n. sp.Etymology: Composite of the Greek adjective pseud meaning “similar to” and the generic name Epistylis. Feminine gender.Pseudepistylis songi n. sp.(Table 1; Figs. 1–37)Diagnosis: Limnetic species with transparent or translucent, dichotomously branched stalk. Zooids elongate, bell-shaped, measuring 220 × 140 μm in vivo on average, with thick peristomial lip that is invariably reflected aborally. One contractile vacuole located on dorsal wall of infundibulum. Macronucleus C-shaped and trans-versely oriented. More than 137 transverse silverlines between oral area and aboral ciliary wreath, on average 77 between aboral ciliary wreath and scopula. Haplokinety and polykinety make one and one-quarter turns on peristome; terminal ends of kineties of infundibular polykinety 2 terminate adstomally at different levels within infundibulum.Type specimens: One holotype (registration number WZ2005102504) and one paratype slide (WZ2006110204) with protargol and silver nitrate impregnated specimens respectively are deposited in the Laboratory of Zoological Systematics, Hangzhou Normal University, China. A second paratype of protargol-stained specimens is deposited in the Natural History Museum, London, UK with registration number 2006:6:20:1.Dedication: We dedicate this new species to Prof. Dr. Weibo Song, Director of Laboratory of Protozool-ogy, Ocean University of China, Qingdao, China, in acknowledgement of his outstanding contributions to the study of ciliated protozoa in China.Type locality: Freshwater pond, Lishui Street, Wenzhou, China (N 28°20.418’; E 120°43.817’).Ecological data:Freshwater,water temperature 22.4 o C, pH 7.34.Description: The zooid is bell-shaped, in vivo 170–285 μm long and 110–190 μm wide; length to width ratio of the fully extended zooid was 1.30–1.85. The peristomial lip is much wider than greatest body width, thick, and invariably reflected aborally. The peristomial disc is large, dome-shaped, and conspicuously ele-vated above the peristomial lip (Figs. 1, 11). Contracted zooids are ovoid or dumbbell-shaped (Figs. 6a,b, 15) with several conspicuous folds, usually numbering 3–4 adoral to the aboral ciliary wreath and 2–3 between the aboral ciliary wreath and the scopula (Figs. 6a,b, 12, arrowhead). The pellicle appears smooth at low mag-nification but fine, reticulate striations are visible at higher magnifications (Figs. 5, 30). Colonies without macrozooids.TABLE 1. Morphological characterization of Pseudepistylis songi n. sp. (all measurements in μm).Character n Min Max Mean SD SE CV (%) Length of zooid, in vivo1817028522031.907.7414.5 Width of zooid, in vivo1811019014022.91 5.5616.3 Width of peristomial disc, in vivo1815023018022.51 5.4612.4 Length of zooid, protargol-impregnated24106197151.329.06 6.0619.2 Width of zooid, protargol-impregnated2486207131.931.29 6.5223.7 Width of peristomial disc, protargol-impregnated245613191.417.13 3.5718.7 Number of silverlines from peristome to ACW131********.67.49 2.16 5.5 Number of silverlines from ACW to scopula13708076.6 2.660.77 3.5A NEW PERITRICH CILIATE GENUS OF EPISTYLIDIDAEZootaxa 1524 © 2007 Magnolia Press·37FIGURES 1–7. Pseudepistylis songi n. sp. in vivo (1–4, 6), protargol impregnation (7), silver nitrate impregnation (5).1. Typical zooid; arrow indicates micronucleus.2. Telotroch.3. Colony of eight zooids showing both extended and con-tracted states as well as structure of stalk.4. Detached zooid; arrowheads mark pellicular folds.5. General silverline sys-tem; arrows indicate aboral ciliary wreath.6. Zooids at low magnification, showing varieties of body shape when contracted; arrowheads mark pellicular folds.7. Myoneme system; arrows indicate aboral ciliary wreath. Abbreviations: CRF, central ring fibers; LLF, long longitudinal fibers; PDF, peristomial disc fibers; PLF, peristomial longitudinal fibers; PRF, peristomial ring fibers; SLF, short longitudinal fibers. Scale bars: 100μm (Fig. 1), 600 μm (Fig. 3).The cytoplasm is grey or colourless, containing many large food vacuoles 13–40 μm in diameter (Figs. 1, 6b, 11–15). There is one contractile vacuole, ca. 29–37 μm in diameter, located on the dorsal wall of the infundibulum slightly aboral to the peristomial disc (Figs. 1, 11). The thick, C-shaped macronucleus is trans-versely oriented and located in the mid-body region (Figs. 1, 18, 33); the micronucleus lies near the mid-region of the macronucleus (Figs. 1, 33, arrow).The stalk is transparent or translucent, dichotomously branched, and slender, with a main trunk measuring approximately 500 μm long and 9 μm in diameter and zooid-bearing branches measuring 400–500 μm long and 10 μm in diameter (Figs. 3, 20, 21). The stalk surface is smooth. In culture, colonies usually contained only one or two zooids, occasionally four, rarely eight or more (Fig. 3).38·Zootaxa 1524 © 2007Magnolia PressPENG ET AL.FIGURES 8–10. Infraciliature of oral apparatus of Pseudepistylis songi n. sp. following protargol impregnation. 8. Typ-ical arrangement of three infundibular polykineties showing the short outer row of P3 (arrowhead) and the inner row of P3 (double-arrowhead). 9. An alternative arrangement of the three infundibular polykineties showing the short outer row of P3 (arrowhead), the shorter inner row of P3 (double-arrowhead) and the short additional branch of P2 (arrow). 10. General infraciliature of the oral apparatus. Abbreviations: F, filamentous reticulum; G, germinal kinety; H, haplokinety; P1–P3, infundibular polykineties 1–3; Po, polykinety (peristomial continuation of P1).Zooids are very sensitive to stimuli and readily detach from the stalk to become free-swimming by means of their prominent oral cilia (Figs. 4, 14, 16, 17); free-swimming zooids usually have several pellicular folds (Figs. 4, 14, arrowhead). The telotroch is cylindrical, measuring approximately 220 × 120 μm in vivo, with a conical posterior and domed-shaped anterior protuberance (Figs. 2, 19). Detached zooids can transform into telotrochs.The haplokinety and polykinety of the peristomial infraciliature make approximately one and one-quarter circuits (450o) around the peristome before plunging into the infundibulum, where they make a further com-plete circuit (Fig. 10). The haplokinety and polykinety at the peristomial region are folded after protargol impregnation (Figs. 10, 25, 31). This folding is almost certainly a consequence of the extremely broad peris-tome being compacted into the restricted confines of the peristome proper when the organism contracts. There are three infundibular polykineties, each consisting of three rows of kinetosomes. The adstomal ends of the three rows of infundibular polykinety 1 (P1) terminate at the same level while those of P2 terminate at differ-ent levels, the kinetosome row next to P3 being the shortest, and the one next to P1 the longest (Figs. 8, 9, 26, 27). The latter sometimes has a short, additional branch (Figs. 9, arrow, 27, arrow). The row of P3 farthest from P2 (outer row) is shorter than the other two rows (Figs. 8, arrowhead, 9, arrowhead). The outer two rows of P3 join together approximately 1/3 of the way from their abstomal to adstomal ends. The inner row of P3 terminates adstomally either at the same level as the outer two (Fig. 8, double arrowhead) or above the outer two at about the same level as the outer row of P2 (Fig. 9, double arrowhead).A NEW PERITRICH CILIATE GENUS OF EPISTYLIDIDAEZootaxa 1524 © 2007 Magnolia Press·39PENG ET AL.40 · Zootaxa 1524 © 2007 Magnolia PressFIGURES 11–21.Pseudepistylis songi n . sp. in vivo. 11. Typical zooid. 12, 15, 18. Contracted zooid. 13, 14, 16, 17.Detached zooid. 19. Telotroch. 20. Stalk. 21. Newly divided zooids and stalk. Arrow in 11 indicates contractile vacuole;arrowheads in 12 and 14 indicate pellicular folds; arrows in 12 and 15 indicate food vacuoles; arrow in 13 indicates peris-tomial disc; arrow in 18 indicates macronucleus; arrow in 19 indicates aboral ciliary wreath. Scale bars: 100 μm (Fig.11), 300 μm (Fig. 20).Zootaxa 1524 © 2007 Magnolia Press · 41A NEW PERITRICH CILIATE GENUS OF EPISTYLIDIDAEFIGURES 22–37. Pseudepistylis songi n . sp. protargol (22–29, 31–37) and silver nitrate (30) impregnation. 22, 25. Typ-ical zooid. 23, 24. Peristomial disc and myonemal fibers. 26, 27. Infraciliature of oral apparatus. 28, 29. Myoneme sys-tem. 30. Pellicular striae. 31. Haplokinety and polykinety. 32, 34. Aboral ciliary wreath of telotroch. 33. Zooid showing macronucleus and micronucleus (arrow). 35. Aboral ciliary wreath of typical zooid. 36. Oral fiber. 37. Scopula. Arrows in 30, 32, 34, 35 indicate aboral ciliary wreath. Abbreviations: CRF, central ring fibers; F, filamentous reticulum; G, ger-minal kinety; H, haplokinety; LLF, long longitudinal fibers; P1–P3, infundibular polykineties 1–3; PDF, peristomial disc fibers; PLF, peristomial longitudinal fibers; Po, polykinety (peristomial continuation of P1); PRF, peristomial ring fibers;SLF, short longitudinal fibers. Scale bars: 50 μm (Fig. 25).The haplokinety passes around the infundibulum on the wall opposite to the infundibular polykineties. The germinal kinety (G) is relatively long, lying adoral to and parallel to the haplokinety and terminates adstomally slightly above the adstomal end of the haplokinety (Figs. 10, 26, 27). The aboral ciliary wreath (ACW) of the zooid consists of two staggered rows of kinetosomes (dikinety) that encircle the cell at approx-imately 1/3 of the distance between the scopula and the peristome (Figs. 7, arrow, 35, arrow). The ACW is significantly broader in the telotroch because of the proliferation of kinetosomes to form a polykinety (Figs. 32, 34, arrow).The silverline system is reticulate, consisting of both transverse and longitudinal silverlines (Figs. 5, 30). The transverse silverlines in the oral region of the cell are more widely spaced than those in the aboral region (Fig. 5). The ACW is represented by two parallel silverlines (Figs. 5, 30, arrow). There are 125–145 trans-verse silverlines between the peristome and the ACW and 70–80 between the ACW and the scopula (Fig. 5). The vertical silverlines are irregularly spaced (Fig. 30). No pellicular pores associated with silverlines were observed. The scopula is the most argentophilic part of the cell (Fig. 37).There are two main kinds of myonemal fibers in Pseudepistylis songi, the longitudinal fibers and the cir-cular fibers, both of which comprise several morphological subtypes. The longitudinal and circular fibers are interconnected to form a single, complete myoneme system.Longitudinal fibers: Three types of longitudinal fiber can be recognized: long longitudinal fibers (LLF), short longitudinal fibers (SLF) and peristomial longitudinal fibers (PLF) (Figs. 7, 22–24, 28, 29). The LLF are less numerous, more diffuse, and more slender than the other two types of longitudinal fibers and extend throughout the entire length of the zooid (Figs. 7, 28, 29). The SLF are the most densely packed and thickest in diameter of the three types and are confined to the region between the PLF and the adoral one third of the zooid (Figs. 7, 29). The PLF are also densely packed, being intermediate in thickness between the other two types, and are confined to the region of the peristomial lip (Figs. 7, 22–24, 29). All three types of longitudinal fibers branch and interconnect with nearby fibers, thus forming a complete, anastomosing, meshlike network (Figs. 7, 28, 29).Circular fibers: Three types of circular fibers were observed in the oral region of the cell. From outermost to innermost these are: peristomial ring fibers (PRF), central ring fibers (CRF), and peristomial disc fibers (PDF) (Figs. 7, 22–25, 29). The PRF consists of approximately 10 individual circular fibers located within the peristomial lip (Figs. 7, 22–24, 29, 36). The CRF comprises several circular fibers that combine to form a con-spicuously thickened fiber located in the adoral part of the cell and connecting to the adoral ends of the PLF (Figs. 7, 22–25, 29). It appears that contraction of the CRF causes the peristomial disc to elevate when the zooid contracts (Figs. 13, 24, 25). The PDF are relatively inconspicuous, short, bundles (Figs. 23, 24).DiscussionComparison of Pseudepistylis with similar genera: According to Lynn and Small (2002), three of six genera within the family Episylididae are colonial: Epistylis, Heteropolaria and Campanella. Pseudepistylis can be distinguished from Epistylis and Heteropolaria by its reticulate silverline system (both transverse and longitu-dinal silverlines vs. transverse silverlines only) (Foissner and Schubert, 1977; Foissner et al., 1992). Further-more, the telotrochs of Heteropolaria are flattened in the oral-aboral plane (i.e. transversely polarized) whereas those of Pseudepistylis are cylindroid (longitudinally polarized) (Foissner and Schubert, 1977). Cam-panella has a reticulate silverline system like that of Pseudepistylis, but its oral ciliature is clearly different: the haplokinety and polykinety of Campanella encircle the peristome 4–6 times (vs <twice in Pseudepistylis) before plunging into the infundibulum (Foissner et al. 1992; Shi et al. 2004). Epicarchesium and Zootham-nopsis are other colonial peritrichs with reticulate silverline systems; however, both of these genera have con-tractile stalks that can be distinguished easily from the non-contractile stalk of Pseudepistylis (Leitner and42·Zootaxa 1524 © 2007Magnolia PressPENG ET AL.Zootaxa 1524 © 2007 Magnolia Press · 43A NEW PERITRICH CILIATE GENUS OF EPISTYLIDIDAEFoissner 1997; Song 1997).Comparison of P . songi with similar species: Four epistylidid species are generally similar to Pseude-pistylis songi in morphology (e.g., similar shape and size of body, broad peristomial lip, similar shape and position of macronucleus, dichotomously branched and non-contractile stalk) and should be compared with it:Campanella umbellaria Linnaeus, 1758, Epistylis chlorelligerum Shen, 1980, E. galea Ehrenberg, 1831 and E. hentscheli Kahl, 1935 (Table 2).TABLE 2. Comparison of Pseudepistylis songi n . sp. with similar morphotypes. The freshwater species, Campanella umbellaria, is a large epistylidid ciliate, the infraciliature and myo-neme system of which were recently described by Shi et al . (2004). Like P . songi , C. umbellaria has a reticu-late silverline system, however C. umbellaria clearly differs from P . songi in the following combination of characters: the oral ciliary rows make more than four turns of the peristome before plunging into the infundibulum (vs. one and one-quarter turns in P . songi ); the peristomial lip has several transverse folds and is not reflected aborally (vs. peristomial lip with a single fold and invariably reflected aborally in P. songi ); theCharacterPseudepistylis songiCampanella umbellaria Epistylischlorelligerum Epistylis galea Epistylis hentscheli Length of body in vivo (μm)170–285200–305160–250200–300110–235Width of body in vivo (μm)110–190100–13545–8370–9038–80Profile of peristo-mial lipwith single trans-verse fold; turned aborally with many transverse folds; not turned aborally with single trans-verse fold; not turned aborally with single trans-verse fold; not turned aborally with single trans-verse fold; not turned aborally Diameter of stalk(μm), shape 9–107–1315–2018–2912–25Position of CV dorsal dorsal unknown dorsal ventral Number of silver-lines from peris-tome to ACW125–14532–40unknown150unknownNumber of silver-lines from ACW to scopula70–8033–40unknown 100unknownNumber of ciliary circuits on peris-tome1.254.52 1.5unknownNumber of zooids in colony1–8up to 800 zoo-ids/cm 2up to 256up to 200up to 370Shape, orientation of macronucleus short C,transverse irregular C,transverse horse-shoe,transverse J-shaped,longitudinal horse-shoe,longitudinal Shape of telotroch cylindrical cylindrical unknown cylindrical globular Shape of zooid bell bell columnar elongate, tapered asymmetric bellSilverline system reticulate reticulate transverse transverse transverse Source of dataPresent paperFoissner et al . (1992), Shi et al . (2004)Shen (1980)Foissner et al . (1992), Qi et al . (2005)Foissner et al . (1992)LLF of the myoneme system are well developed, thick and dense (vs. myoneme system with weakly devel-oped, slender and sparse LLF in P. songi); the colonies are often large, sometimes several centimeters in size and may contain many (>100) zooids (vs. colony small, rarely with more than 4 zooids in P. songi) (Foissner et al.1992; Shi et al. 2004).Epistylis chlorelligerum,E. galea and E. hentscheli all resemble Pseudepistylis songi in having a broad peristomial lip and oral ciliatures that make less than two turns of the peristome before entering the infundibu-lum. However, all three can be separated from P. songi by the following combination of characters: transverse silverline system (vs. reticulate in P. songi); peristomial lip not reflected aborally (vs. peristomial lip invari-ably reflected aborally in P. songi); colony often large and containing 200 or more zooids (vs. colony small, seldom with more than 4 zooids in P. songi) (Shen, 1980; Foissner et al., 1992; Qi et al. 2005). See Table 2 for further comparison of these taxa.The myoneme system of Pseudepistylis songi may be compared with that of another epistylid, Cam-panella umbellaria, which was described in detail by Shi et al. (2004) and comprises: short longitudinal fibers (SLF), medium-length longitudinal fibers (MLF), long longitudinal fibers (LLF), peristomial ring fibers (PRF), linking fibers (LF), support fibers (SF), and persitomial disc fibers(PDF) forming a whole myoneme system. All of these fibers were also found in P. songi apart from the MLF, the LF and the SF. By contrast, two types of fibers, the peristomial longitudinal fibers (PLF) and central ring fibers (CRF), are present in P. songi but were not found in C. umbellaria. By Shi et al. (2004). It seems that the myoneme system of C. umbellaria is better developed than that of P. songi in that the LLF is thick and dense in C. umbellaria (vs. slender and sparse in P. songi), and the MLF, LF and SF are all present in C. umbellaria (vs. absent in P. songi) (Shi et al. 2004).The shape of the peristomial lip in P. songi is highly distinctive and unusual among peritrichs. Vorticella marginata Stiller, 1931 also has a very wide peristomial lip that appears to be reflected aborally, but this form awaits redescription and it is unclear from the original illustration whether the arrangement of the peristomial lip is comparable to that of P. songi (Stiller, 1931). Finally, mention must be made of an organism that awaits formal description but will almost certainly prove to be another species of Pseudepistylis. This is a marine form that was recently isolated by Ping Sun (Ocean University of China) from coastal waters near Qingdao, NE China and is also an epistylidid with a reticulate silverline system and rows of oral cilia that encircle the peristome less than twice before entering the infundibulum (Sun, pers. comm.).AcknowledgementsThis work was supported by funding from the Natural Science Foundation of China (grants 30670222 and 30170108); the Natural Science Foundation of Zhejiang Province, China (grant Y304410); and Hangzhou Key Laboratory for Animal Sciences and Technology, Hangzhou Normal University. We gratefully acknowl-edge Ms. Ping Sun and Prof. Dr. Weibo Song (Ocean University of China) for their helpful comments and dis-cussion.ReferencesBory de St. Vincent, J.B. (1826) Essai d’une Classification des Animaux Microscopiques. Agasse, Paris 104 pp. Corliss, J.O. (1979) The Ciliated Protozoa: Characterization, Classification and Guide to the Literature, 2nd Ed. Perga-mon Press, New York.Ehrenberg, C.G. (1830). Beiträge zur Kenntniss der Organisation der Infusorien und ihrer geographischen Verbeitung, besonders in Siberien. Abhandlungen der Akademie der Wissenschaften zu Berlin, year 1830, 1-88.Ehrenberg, C.G. (1831) Űber die Entwickelung und Lebensdauer der Infusionsthiere: nebst ferneren Beiträgen zu einer Vergleichung ihrer organischen Systeme. Abhandlungen der Akademie der Wissenschaften zu Berlin, year 1831, 1-44·Zootaxa 1524 © 2007Magnolia PressPENG ET AL.154.Foissner, W. & Schiffmann, H. (1974) Vergleichende Studien an argyrophilen Strukturen von vierzehn peritrichen Cili-aten. Protistologica, 10, 489–508.Foissner, W. & Schubert, G. (1977) Morphologie der Zooide und Schwärmer von Heteropolaria colisarum gen. nov., spec. nov. (Ciliata, Peritrichida), einer symphorionten Epistylidae von Colisa fasciata (Anabantoidei, Belontiidae).Acta Protozoologica, 16, 231–247.Foissner, W., Berger H. & Kohmann F. (1992) Taxonomische und Ökologische Revision der Ciliaten des Saprobiensys-tems—Band II: Peritrichia, Heterotrichida, Odontostomatida. Informationsberichte der ndesamtes für Wasserwirtschaft, 5/92, 1–502.Jankowski, A.W. (1985). Life cycles and taxonomy of generic groups, Scyphidia, Heteropolaria, Zoothamnium and Cothurnia (Class Peritricha). Trudy Zoologicheskogo Institute, Akademiya Nauk SSSR, Leningrad. 129, 74-100 (in Russian).Ji, D., Song, W. & Warren, A. (2004) Rediscovery and redescription of the marine peritrichous ciliate Epicarchesium abrae (Precht, 1935) nov. comb. (Protozoa, Ciliophora, Peritrichia). European Journal of Protistology, 40, 219–224. Kahl, A. (1933) Ciliata libera et ectocommensalia. In Grimpe, G. & Wagler, E. (Ed.), Die Tierwelt der Nord- und Ostsee, Lief 23 (Tiel II, c), Leipzig, pp. 29-146.3Kahl, A. (1935) Urtiere oder Protozoa I: Wimpertiere oder Ciliata (Infusoria). 4. Peritricha und Chonotricha. In: Dahl, F.(Ed.), Die Tierwelt Deutschlands, 30, 651–886.Kent, W.S. (1880–1882) A Manual of the Infusoria: including a description of all known flagellate, ciliate, and tentacu-liferous protozoa, British and foreign, and an account of the organization and the affinities of the sponges. David Bogue, London. (V ol. I, 1880, 1–432; Vol. II, 1881, 433–720; Vol. II, 1882, 721–913; V ol. III, 1882, Plates). Leitner, A.R. & Foissner, W. (1997) Taxonomic characterization of Epicarchesium granulatum (Kellicott, 1887) Jan-kowski, 1985 and Pseudovorticella elongata (Fromentel, 1876) nov. comb., two peritrichs (Protozoa, Ciliophora) from activated sludge. European Journal of Protistology, 33, 13–29.Linnaeus, C. (1767) Systema Naturae. V ol. 1. 12th ed. Salvii Holmiae, 1327 pp.Lynn, D.H. & Small, E.B. (2002) Phylum Ciliophora. In: Lee, J.J., Leedale, G.F. and Bradbury, P. (Ed.) An Illustrated Guide to the Protozoa, 2nd ed. Society of Protozoologists, Lawrence, Kansas, 371–656.Puytorac, P. de et al. (1974) Proposition d’une classification du phylum Ciliophora Doflein, 1901 (reunion de systéma-tique, Clermont-Ferrand). Compte Rendu de l’Akademie des Sciences. 278, 2799-2802.Qi, G., Shi, X.L. & Feng, W. (2005) Studies on the morphology and infraciliature of Epistylis galea. Acta Hydrobiologica Sinica, 29, 444–449. (in Chinese with English summary).Shen, Y. (1980) Six new species of periphytic protozoa in Lake Dong Hu, Wuhan. Acta Hydrobiologica Sinica, 7, 245–252. (in Chinese with English summary).Shi, X.B. & Frankel, J. (1990) Morphology and development of mirror-image doubles of Stylonychia mytilus. Journal of Protozoology, 37, 1–13.Shi, X.L., Warren, A., Yu, Y. & Shen, Y. (2004) Infraciliature and myoneme system of Campanella umbellaria (Proto-zoa, Ciliophora, Peritrichida). Journal of Morphology, 261, 43–51.Song, W. & Wang, M. (1995) Newly improved dry silver-nitrate method applied to silverline system of ciliate. Chinese Journal of Zoology, 30, 38–39. (in Chinese).Song, W. (1986) Descriptions of seven new species of peritrichs on Penaeus orientalis (Peritricha: Zoothamnidae: Epi-stylididae). Acta Zootaxonomica Sinica, 11, 225–235. (in Chinese with English summary).Song, W. (1997) A new genus and two new species of marine peritrichous ciliates (Protozoa, Ciliophora, Peritrichida) from Qingdao, China. Ophelia, 47, 203–214.Song, W., AL-Rasheid, K.A.S. & Hu, X. (2002) Notes on the poorly known marine peritrichous ciliate, Zoothamnium plumula Kahl, 1933 (Protozoa: Ciliophora), an ectocommensal organism from cultured scallops in Qingdao, China.Acta Protozoologica, 41, 163–168.Stein, F. (1859) Der Organismus der Infusionsthiere nach eigenen Forschungen in systematischer Reihenfolge bearbe-itet. I. Leipzig. 206 pp.Stiller, J. (1931) Die Peritrichen Infusorien von Tihany und Umgebung. Arbeiten des Ungarischen Biologischen Fors-chungs Institutes, 4, 171–205.Sun, P., Song, W. & Warren, A. (2006) Taxonomic characterization of two marine peritrichous ciliates, Epicarchesium corlissi n. sp. and Pseudovorticella jiangi n. sp. (Ciliophora: Peritrichia), from northern China. European Journal of Protistology, 42, 281–289.Warren, A. (1986) A revision of the genus Vorticella (Ciliophora: Peritrichida). Bulletin of the British Museum (Natural History) (Zoology Series), 50, 1–57.A NEW PERITRICH CILIATE GENUS OF EPISTYLIDIDAEZootaxa 1524 © 2007 Magnolia Press·45。
el标签用法
el标签用法一、EL表达式基本用法1. 在JSP页面中输出变量值- 在JSP中，如果有一个JavaBean，例如一个名为 `User` 的类，里面有一个属性 `name`，在JSP页面中设置了这个JavaBean。
在JSP页面中就可以这样使用EL表达式来获取这个属性的值：- 假设在JSP中有 `<% User user = new User();user.setName("John"); pageContext.setAttribute("user", user); %>`，那么在页面中可以用 `${}`，这就像是打开了一个魔法盒子，直接就能看到里面的宝贝（变量的值），多么方便呀！就像你知道有个宝藏藏在某个地方，用EL表达式就能轻松找到它。
- 再比如，如果有一个List类型的变量 `fruits`，里面存放着各种水果的名字，在JSP页面中设置了这个变量。
如果想获取列表中的第一个元素，可以用 `${fruits[0]}`。
这就好比从一篮子水果中，你可以轻松地用手（EL表达式）拿出最上面的那个水果（第一个元素）。
2. 进行简单的运算- EL表达式可以进行数学运算。
例如，如果有两个变量 `num1` 和`num2`，在JSP页面中分别被设置为5和3。
那么 `${num1 + num2}` 就会得到8。
这就像你有5个苹果和3个苹果，用EL表达式一下子就能算出总共有多少个苹果，是不是很神奇呢？- 也可以进行比较运算。
如果有一个变量 `age`，在JSP页面中被设置为18。
那么 `${age >= 18? '成年人' : '未成年人'}`，就像一个智能的判断器，根据年龄的值来判断是成年人还是未成年人。
这可比我们自己在JSP中写一堆Java代码来判断要简洁得多呀！3. 访问对象的属性和方法（有限制的方法调用）- 对于对象的属性访问，如前面提到的JavaBean的属性访问。
Population-level...
CommentPopulation-level neutral model alreadyexplains linguistic patternsIn customizing the neutral model for language trans-mission,Reali &Griffiths [1]have added a new,‘Bayesian’learning interpretation to a neutral model that has been used in cultural evolution studies for some time (e.g.[2–6]).While Reali &Griffiths [1,p.435]dismiss previous applications of this neutral model as being used merely ‘as a metaphor’,it has been explored in quantitative detail,and applied even to word frequencies [7].We therefore question whether this fairly complex description of Bayesian learning is necess-ary,as it does not change the results of the model,and runs the risk of obscuring the advances made both through careful modifications and wider applications of this powerful model.The Reali &Griffiths [1]version of the model is mathe-matically equivalent to previous versions,in representing a set of N variants per time step,with the next generation of N variants constructed by repeatedly sampling variants from the previous time step,with some probability of ‘mutation’,i.e.introduction of a variant of a unique new variant.Reali &Griffiths [1]apply the model in a novel sense to individual cognition,whereas it has previously been considered in terms of a population of fixed size N ,which is replaced by N new agents in each time step.In this population version,each new individual either copies an existing variant from the last time step (with probability proportionate to the previous choices of agents),or chooses a new variant.With probability 12m ,an incom-ing agent copies its variant from that of an agent within the previous time step,or else with probability m ,the agent innovates by choosing a unique new variant at random.This model has already been shown to yield the inverse power law in the probability of variant frequencies that Reali and Griffiths report,from which the other inverse power law,in word frequency versus replacement rate,also follows.We demonstrate this briefly below,in order to show how the discussion of the model has advanced beyond this,including the incremental addition of a new extra parameter,‘memory’[8].Before we consider the model predictions,however,we address the question of the psychological plausibility of the assumptions that are made about agent behaviour.The Bayesian learning approach assumes agents use ‘a rational procedure for belief updating that explicitly rep-resents the expectations of learners’(Reali &Griffiths [1,p.430]).This raises a very important topic in social sciences.Standard economic theory,for example,assumes rational agents capable of obtaining all relevantinformation and then processing this to arrive at an opti-mal decision.The less demanding postulate of bounded rationality restricts access to full information,but the hypothesis of optimal decisions is retained (subject to information constraints).Individual rationality is not the only way,however,that learning can be represented (e.g.[9,10]),and alternative hypotheses have support both in particular applications and in the wider literature.At the other extreme,for instance,the assumption of literally ‘zero-intelligence’agents,based on the particle model of physics,has pro-vided a powerful explanation of many population-scale phenomena,such as financial asset price changes [11].Daniel Kahneman [12]argued in his Nobel prize lecture,awarded for his work in economic psychology,that ‘the central characteristic of agents is not that they reason poorly,but that they often act intuitively.And the behav-iour of these agents is not guided by what they are able to compute,but by what they happen to see at a given moment’.Heuristically,this seems perfectly consistent with a population-level view of learning,rather than the standard rational-individual model.We might,therefore,alternatively assume that agents (including language speakers)observe the relative popu-larity of previous choices among other agents,and copy in proportion to these popularities,with a small prob-ability of making an innovative choice.This population view,which is able to offer a good account of a wide range of cultural phenomena,assumes that agents are indifferent—‘neutral’—to the particular agent that they copy.The result of this neutrality is that agents tend to copy variants in proportion to the previous choices made by other agents;a more popular choice is more likely to be copied than a less popular one.In the special case where m ¼0,the model is formally equivalent to a process of preferential attachment (e.g.[13]).This population view also can provide extra flexibility for modifying the model.In the Bayesian learning rep-resentation,for instance,each individual commits previous experience to individual memory.In the popu-lation view,previous experience is copied directly from other individuals,and ‘memory’becomes a useful added parameter.In our own version [8],this variable memory,designated as m ,allows agents to copy from m previous time steps.This effectively means that the orig-inal variants can ‘linger’,because even if replaced in the current time step,they might still be ‘remembered’again within m subsequent time steps.Adding this memory parameter adds both flexibility and power to replicate real-world data patterns,including the three that Reali &Griffiths [1]report;in the inverse power law probability distribution of variant frequencies,The accompanying reply can be viewed at /10.1098/rspb.2011.0349.Proc.R.Soc.B (2011)278,1770–1772doi:10.1098/rspb.2010.2581Published online 23March 2011Received 30November 2010Accepted 22December 20101770This journal is q 2011The RoyalSocietyand in‘S’curves of replacement.Furthermore,by keep-ing the model simple in description,it is then easier to explore different phenomena,such as theflux of variants on a ranked popularity list[8,14].We can visit each of the three main results of Reali&Griffiths[1]to show the advantages.Firstly,Reali&Griffiths[1]fit their results to a power law probability distribution with exponent21.7.Among the many processes that can produce Zipf’s laws[15], the simpler neutral model yields this very same power law exponent(between about21.6and21.8)when var-iants are counted cumulatively(e.g.[16]).Notably,the exponent of bestfit varies with the choice of mutation rate,and in fact as we move away from N m¼1(N is population,m is mutation),a power law no longerfits [17];increasing N m tends to reduce highest frequencies and push the distribution towards a an exponential distri-bution,whereas decreasing N m increases the highest frequency,ultimately to a‘winner-take-all’distribution at N m¼0[3,8,18].In addition,the added memory par-ameter m enables the modified neutral model to generate a much larger family of right-skew frequency distributions[8].Secondly,Reali&Griffiths[1]report an inverse power law relating word frequency to replacement rate,compar-able to real languages[19].This has also already been shown in the population version of the model[14], albeit in a slightly different way.Consider a ranked list of the V most popularly used variants in a particular var-iant pool,such as the V most popularly used words in a language.Regarding this list of variants,ranked in order of popularity from1(most popular)to V(least popular), the turnover on the entire list(word replacement rate among the top V words)is simply proportional to the size,V,of the list—as has been shown through simulation [14,20]and analytically[21].The turnover just at rank V is equivalent to the turnover on the entire list from rank1 to rank V,because each new entry must,at some instant, displace the one at the bottom or V th position(this turn-over decreases moving up the rankings,because reaching rank V does not guarantee reaching rank V21).Zipf’s law implies that the frequency of a word at rank V has usage frequency proportional to V-a,meaning the prob-ability distribution function(PDF)has an exponent of –(1þ1/a).Plotting turnover at position V on the y-axis versus the word frequency V-a on the x-axis therefore yields an inverse power law with negative slope1/a, which is just the slope of the PDF(1þ1/a)minus1. Reali&Griffiths([1],fig.2)show word frequency PDF with exponent21.74,so we expect a plot of turnover versus word frequency to have a power law slope of 20.74,which is quite close to their demonstrated slope of20.8.Thirdly,Reali&Griffiths[1]demonstrate‘S’curves of replacement that follow naturally from their rational-individual version of the model,following Wright–Fisher. This is an important realization,and by allowing for a population view,we can(i)add the memory parameter to gainflexibility in the resulting turnover,and(ii)apply to multiple variants in such contemporary representations as the ranked popularity list.T o see why,consider an orig-inal pool of V different variants at time zero,and let x(t)be the number of new variants replacing this original pool. These V variants could represent the different words in a vocabulary,for example,or perhaps different possible grammatical rules in use.This replacement rate is pro-portional to the constant turnover rate,z,times the diminishing proportion(12x/V)of original variants remaining in the pool:d xd t¼z1ÀxV;ð1Þwhich is a separable differential equation,with a simple solutionxðtÞ¼V1ÀeÀzt=V:ð2ÞThis is not an S curve,because even though it asymptoti-cally approaches V over time,it begins quickly(when x¼0,d x/d t¼z),rather than beginning slowly and speeding up.Adding the memory parameter,however,for certain combinations,such as m¼10time steps and m¼10%, enables the modified neutral model to generate an S curve of replacement(figure1).In summary,the basic versions of the two models are mathematically the same,but radically different in interpretation:one is aimed at populations of simple imi-tators,whereas the other is focused on rationally based individual learning through iterated sampling.We argue that there are two reasons not to abandon the population representation.Firstly,it keeps things simple,and modifi-cations and advances from this model—e.g.adding new extra parameters such as copying bias[6]or memory [8]—are more easy to benefit from and to apply generally. Secondly,in many situations of collective behaviour—which includes language,by its very definition—a representation of very simple social learning in a popu-lation may be more realistic than a model of individual rational actors.In any case,as vast sets of new word fre-quency data become available[22],the simplest,most flexible model has the best chance of generating insights across the range of social evolutionary phenomena where the same patterns occur.2040608010050100150200250time step%replacementFigure1.The effect of the simple memory parameter on replacement rate under the simple neutral model,with N¼1000,and m¼10%.Here we consider an original vocabulary of100popular words,and how those words are replaced over time under the model(note that an‘S’curve does not follow from the simpler version of the model,where m¼1).Dashed line,m¼1;thick grey line,m¼5;thin grey line,m¼10; thin black line,m¼15;thick black line,m¼20.Comment.Simpler neutral model explains patterns R.A.Bentley et al.1771 Proc.R.Soc.B(2011)R.Alexander Bentley1,*,Paul Ormerod1,2andStephen Shennan3 1Department of Anthropology,Durham University,South Road,Durham DH13LE,UK2V olterra Consulting,135c Sheen Lane,London SW148AE,UK 3Institute of Archaeology,University College London,31-34Gordon Square,London WC1H0PY,UK *Author for correspondence(*********************.uk). REFERENCES1Reali,F.&Griffiths,T.L.2010Words as alleles:con-necting language evolution with Bayesian learners to models of genetic drift.Proc.R.Soc.B277,429–436.(doi:10.1098/rspb.2009.1513)2Lipo,C.P.,Madsen,M.E.,Dunnell,R.C.&Hunt,T.1997Population structure,cultural transmission and frequency seriation.J.Anthropol.Archaeol.16, 301–333.(doi:10.1006/jaar.1997.0314)3Neiman,F. D.1995Stylistic variation in evolutionary perspective.Am.Antiquity60,7–36.(doi:10.2307/28 2074)4Shennan,S.J.&Wilkinson,J.R.2001Ceramic style change and neutral evolution:a case study from Neolithic Europe.Am.Antiquity66,577–594.(doi:10.2307/2694174)5Lieberman,E.,Hauert,C.&Nowak,M.A.2005Evol-utionary dynamics on graphs.Nature433,312–316.(doi:10.1038/nature03204)6Mesoudi,A.&Lycett,S.J.2009Random copying,fre-quency-dependent copying and culture change.Evol.Hum.Behav.30,41–44.(doi:10.1016/j.evolhumbehav.2008.07.005)7Bentley,R.A.2008Random drift versus selection in academic vocabulary.PLoS ONE3,e3057.(doi:10.1371/journal.pone.0003057)8Bentley,R.A.,Ormerod,P.&Batty,M.In press.Evol-ving social influence in large populations.Behav.Ecol.Sociobiol.(doi:10.1007/s00265-010-1102-1)9Laland,K.N.2004Social learning strategies.Learn.Behav.32,4–14.10Shennan,S.J.2002Genes,memes and human history.London,UK:Thames and Hudson.11Farmer,J.D.,Patelli,P.&Zovko,I.I.2005The pre-dictive power of zero intelligence infinancial markets.Proc.Natl A102,2254–2259.(doi:10.1073/pnas.0409157102)12Kahneman,D.2003Maps of bounded rationality:psy-chology for behavioral economics.Am.Econ.Rev.93, 1449–1475.(doi:10.1257/000282803322655392)13Simon,H.A.1955On a class of skew distribution func-tions.Biometrika42,425–440.14Bentley,R.A.,Lipo,C.P.,Herzog,H.A.&Hahn,M.W.2007Regular rates of popular culture change reflect random copying.Evol.Hum.Behav.28,151–158.(doi:10.1016/j.evolhumbehav.2006.10.002)15Newman,M.E.J.2005Power laws,Pareto distributions and Zipf’s law.Contemp.Phys.46,323–351.(doi:10.1080/00107510500052444)16Hahn,M.W.&Bentley,R.A.2003Drift as a mechanism for cultural change:an example from baby names.Proc.R.Soc.Lond.B270(Suppl.1),S120–S123.(doi:10.1098/rsbl.2003.0045)17Evans,T.S.2007Exact solutions for network rewiring models.Eur.Phys.J.B56,65–69.(doi:10.1140/epjb/ e2007-00084-8)18Bentley,R.,Hahn,M.W.&Shennan,S.J.2004Random drift and culture change.Proc.R.Soc.Lond.B271, 1443–1450.(doi:10.1098/rspb.2004.2746)19Pagel,M.,Atkinson,Q. D.&Meade, A.2007 Frequency of word-use predicts rates of lexical evolution throughout Indo-European history.Nature449, 717–720.(doi:10.1038/nature06176)20Bentley,R.A.,Ormerod,P.&Madsen,M.E.2009Phys-ical space and long-tail markets.Physica A388,691–696.(doi:10.1016/j.physa.2008.11.009)21Eriksson,K.,Jansson,F.&Sjo¨strand,J.2010Bentley’s conjecture on popularity toplist turnover under random copying.Ramanujan J.23,371–396.(doi:10.1007/ s11139-010-9280-z)22Michel,J.-P.et al.&The Google Books T eam.2011 Quantitative analysis of culture using millions of digitized books.Science331,176–182.(doi:10.1126/science.1199644)1772R.A.Bentley et ment.Simpler neutral model explains patterns Proc.R.Soc.B(2011)。
random variable 的英文解释
random variable 的英文解释Random variables are an essential concept in probability theory and statistics, as they play a crucial role in modeling and analyzing uncertain phenomena. A random variable is a mathematical function that assigns a numerical value to each possible outcome of a random experiment. In other words, it is a variable that can take on different values with certain probabilities.The concept of a random variable is fundamental in many areas of study, including finance, engineering, biology, and social sciences, where researchers and analysts need to deal with uncertainty and make decisions based on probabilistic information. By understanding and working with random variables, we can gain insights into the behavior of complex systems, make more informed decisions, and better understand the world around us.One of the key characteristics of a random variable is its probability distribution, which describes the likelihood of the variable taking on different values. The probability distribution can be either discrete or continuous, depending on the nature of the random variable. Discrete random variables can only take on a finite or countable number of values, while continuous random variables can take onany value within a specified range.Discrete random variables are often used to model situations where the possible outcomes are distinct and countable, such as the number of heads in a series of coin flips or the number of defective items in a production process. In these cases, the probability distribution can be described by a probability mass function, which assigns a probability to each possible value of the random variable.Continuous random variables, on the other hand, are used to model situations where the possible outcomes are not discrete, such as the height of individuals in a population or the time it takes for a machine to break down. In these cases, the probability distribution can be described by a probability density function, which specifies the relative likelihood of the random variable taking on different values within a continuous range.One of the most important properties of random variables is their expected value, also known as the mean or average. The expected value represents the long-term average or typical value of the random variable, and it is calculated as the weighted average of all possible values, with the weights being the corresponding probabilities. The expected value is a crucial measure in decision-making, as it provides a way to quantify the central tendency of a random variable.Another important property of random variables is their variance, which measures the spread or dispersion of the values around the expected value. The variance reflects the degree of uncertainty or variability associated with the random variable, and it is calculated as the average of the squared deviations from the expected value. The square root of the variance, known as the standard deviation, is also a commonly used measure of variability.Random variables can also be classified based on their independence or dependence. Independent random variables are those whose values are not influenced by the values of other random variables, while dependent random variables are those whose values are related to the values of other random variables. Understanding the relationships between random variables is crucial in many applications, as it allows for more accurate modeling and better decision-making.In addition to these basic properties, random variables can also exhibit more complex characteristics, such as skewness (the asymmetry of the probability distribution) and kurtosis (the peakedness or "tailedness" of the distribution). These higher-order moments can provide additional insights into the behavior of the random variable and its potential impact on the system or process being studied.The concept of random variables is not only important in theoretical studies but also has numerous practical applications. In finance, for example, random variables are used to model stock prices, interest rates, and other financial variables, which are crucial for investment decisions and risk management. In engineering, random variables are used to model the reliability and performance of systems, such as the lifetime of electronic components or the strength of materials. In biology, random variables are used to model the genetic variations within a population or the spread of infectious diseases.In conclusion, random variables are a fundamental concept in probability theory and statistics, and they play a crucial role in modeling and analyzing uncertain phenomena. By understanding the properties and characteristics of random variables, researchers and analysts can make more informed decisions, gain deeper insights into complex systems, and better understand the world around us.。
VariableDescriptions
The following table contains variable descriptions as reported in: Ihlanfeldt and Taylor,Externality Effects of Small-Scale Hazardous Waste Sites: Evidence from Urban CommercialProperty Markets, forthcoming, Journal of Environmental Economics and Management.Table A.1. Description of variables used in hedonic price analysis.Variable DescriptionNameProperty Characteristics: Obtained from Property Data Systems, Inc.saleprice Most recent sales price of the property.yr5-22Dummy variables indicating the year in which the property sold for the years 1981 to 1998. sqft (sqft2)Square feet, in thousands of feet, of all improvements on a property (square feet squared). numimp Number of improvements on a property.acre (acre2)Acreage of the property (acreage squared).age (age2)The age of the primary improvement (age squared).gradeab, Dummy variable indicating the structures on the property were scored by the tax assessors gradec with an A or B rating, the two best possible ratings on a scale of A through E. Gradec indicates the property received a quality scoring of C. Gradeab and gradec are compared togrades D or E, the two categories left out of the models. D and E are combined due to smallnumbers of observations receiving an E score.concrete Dummy variable indicating whether the exterior wall of the primary structure is concrete. glass Dummy variable indicating whether or not the exterior wall of the primary structure was glass.frame Dummy variable indicating whether or not the exterior wall of the primary structure was frame.extmisc Dummy variable indicating whether or not the exterior wall of the primary structure was combined wall types.brick Dummy variable indicating whether or not the exterior wall of the primary structure was brick (category left out of the models).pkadeq Dummy variable indicating whether or not the property has adequate parking.front2Dummy variable indicating if the property fronts a major strip.Variable DescriptionNameloccod2-Dummy variables indicating the type of location for the property. Categories are: CBD or loccod9permanent CBD (loccod2), business cluster (loccod3), major strip (loccod4), secondary strip (loccod5), neighborhood or spot (loccod6), commercial/industrial park (loccod7), industrialsite (loccod8), apartment/condominium (loccod9). The category not included in the modelvaries across land uses.ad1-ad3Dummy variables indicating specific land-use codes within apartments and hotels.Categories are: non-high-rise apartments (ad1), hotels/motels (ad2), nursing/boarding homes(ad3).rd1, rd2Dummy variables indicating specific land-use codes within retail. Categories are eating and drinking establishments (rd1), and fast food (rd2).od1Dummy variable indicating land-use is a high-rise office.manuf Dummy variable indicating industrial land use is for manufacturing.d1-d4Dummy variables indicating specific land-use codes within industrial, other thanmanufacturing. Categories are: cold storage (d1), truck terminal (d1), mini warehouse (d1),prefab warehouse (d1). Category not included in the model is warehouse.Location-Oriented Variables: Created with ARCVIEW Geographic Information Systemsjuris1-9Dummy variables indicating the tax jurisdiction in which the property is located. Categories are: Alpharetta (juris1), Atlanta (juris2), College Park (juris3), East Point (juris4), Fairburn(juris5), Fulton County unincorporated (juris6), Hapeville (juris7), Palmetto (juris8), Roswell(juris9). Category not included in the model varies by land-use type. Sales included in ourmodels did not occur in every jurisdiction for each land use.north Dummy variable equal to one of the property is located north of the central-point of the central business district.cbd (ncbd)Distance to the center-point of the central business district. The center is the central public rail transit station (5-points MARTA station) in downtown Atlanta (distance to the center-point of the central business district interacted with the dummy variable ‘north’).marta1Dummy variable equal to one if a property was within one mile of a MARTA station at the time of sale.exit (nexit) Distance to the nearest highway exit (distance to the nearest highway exit interacted with the dummy variable ‘north’).harts (harts2)Distance to Hartsfield International Airport (distance to the airport squared).Variable DescriptionNameCensus-tract Variables: Obtained from the Atlanta Regional Commission (ARC) and Donnelly, Inc.rmedinc Real median income, by year, of the census tract in which the property is located. Real [from median income for years 1981-1989 and 1991-1996 are estimated based on census data from Donnelly, Inc.]1980 and 1990. Estimates for 1997 were not available and so sales in 1997 are assigned values from 1996.popden Population density (persons per acre of land), by year, of the census tract in which the (npopden)property is located. Population densities in non-census years are assigned in the same [from ARC]manner as described for rmedinc (population density interacted with dummy variable ‘north’).empden Employment density (workers in all sectors per acre of land), by year, of the census tract in (from ARC)which the property is located. Employment densities in non-census years are assigned in the same manner as described for rmedinc.minority Percent non-white in the census tract in which the property is located. Racial compositions (nminority)in non-census years are estimated by ARC by conducting field surveys (minority interacted [from ARC]with the dummy variable ‘north’).vacant Percent of the land-area that is vacant in the census year closest to the sale date in the census (from ARC)tract in which the property is located.Proximity to Listed Sites Variables:Created with ARCVIEW Geographic Information SystemsIDL1B Inverse of distance from property to List1 site (measured in quarter-mile increments) if sale occurred before the site was listed, otherwise 0. Measurements are not rounded. Thus, 500meters = 1.24 quarter miles = 0.31 miles.IDL1A Inverse of distance from property to List1 site (measured in quarter-mile increments) if sale occurred after the site was listed, otherwise 0.IDL2B Inverse of distance from property to List2 site (measured in quarter-mile increments) if sale occurred before the site was listed, otherwise 0.IDL2A Inverse of distance from property to List2 site (measured in quarter-mile increments) if sale was after the site was placed on CERCLIS but before de-listing, otherwise 0.IDL2D Inverse of distance from property to List2 site (measured in quarter-mile increments) if sale was after de-listing (i.e., after site was listed as NFRAP), otherwise 0.。
Torrance Tests of Creative Thinking
Torrance Tests of Creative ThinkingFrom Wikipedia, the free encyclopediaThe Torrance Tests of Creative Thinking is a test of creativity.Contents[hide]Description[edit]Building on J.P. Guilford's work and created by Ellis Paul Torrance, the Torrance Tests of Creative Thinking (TTCT), a test of creativity, originally involved simple tests of divergent thinking and other problem-solving skills, which were scored on four scales:•Fluency. The total number of interpretable, meaningful, and relevant ideas generated in response to the stimulus.•Flexibility. The number of different categories of relevant responses.•Originality. The statistical rarity of the responses.•Elaboration. The amount of detail in the responses.The third edition of the TTCT in 1984 eliminated the Flexibility scale from the figural test, but added Resistance to Premature Closure (based on Gestalt Psychology) and Abstractness of Titles as two new criterion-referenced scores on the figural. Torrance called the new scoring procedure Streamlined Scoring. With the five norm-referenced measures that he now had (fluency, originality, abstractness of titles, elaboration and resistance to premature closure), he added 13criterion-referenced measures which include: emotional expressiveness, story-telling articulateness, movement or actions, expressiveness of titles, syntheses of incomplete figures, synthesis of lines, of circles, unusual visualization, extending or breaking boundaries, humor, richness of imagery, colourfulness of imagery, and fantasy.[1]According to Arasteh and Arasteh (1976) the most systematic assessment of creativity in elementary school children has been conducted by Torrance and his associates (1960a,1960b,1960c, 1961,1962,1962a,1963a 1964), who have developed and administered the Minnesota Tests of Creative Thinking (MTCT), which was later renamed as the TTCT, to several thousands of school children. Although they have used many of Guilfords concepts in their test construction, the Minnesota group, in contrast to Guilford, has devised tasks which can be scored for several factors, involving both verbal and non-verbal aspects and relying on senses other than vision. These tests represent a fairly sharp departure from the factor type tests developed by Guilford and his associates (Guilford, Merrifield and Cox, 1961; Merrifield, Guilford and Gershan,1963), and they also differ from the battery developed by Wallach and Kogan (1965), which contains measures representing creative tendencies that are similar in nature (Torrance, 1968).To date, several longitudinal studies have been conducted to follow up the elementary school-aged students who were first administered the Torrance Tests in 1958 in Minnesota. There was a 22-year follow-up,[2][3][4] a 40-year follow-up,[5]and a 50 year follow-up [6]Torrance (1962) grouped the different subtests of the Minnesota Tests of Creative Thinking (MTCT) into three categories.1. Verbal tasks using verbal stimuli2. Verbal tasks using non-verbal stimuli3. Non-verbal tasksTasks[edit]A brief description of the tasks used by Torrance is given below:Unusual Uses[edit]The unusual uses tasks using verbal stimuli are direct modifications of Guilford’s Brick uses test. After preliminary tryouts, Torrance (1962) decided to substitute tin cans and books for bricks. It was believed the children would be able to handle tin cans and books more easily since both are more available to children than bricks.Impossibilities taskIt was used originally by Guilford and his associates (1951) as a measure of fluencyinvolving complex restrictions and large potential. In a course in personality developmentand mental hygiene, Torrance has experimented with a number of modifications of thebasic task, making the restrictions more specific. In this task the subjects are asked to list as many impossibilities as they can.Consequences taskThe consequences task was also used originally by Guilford and his associates (1951).Torrance has made several modifications in adapting it. He chose three improbablesituations and the children were required to list out their consequences.Just suppose taskIt is an adaptation of the consequences type of test designed to elicit a higher degree of spontaneity and to be more effective with children. As in the consequence task, the subject is confronted with an improbable situation and asked to predict the possible outcomes from the introduction of a new or unknown variable.Situations taskThe situat ion task was modeled after Guilford’s (1951) test designed to assess the ability to see what needs to be done. Subjects were given three common problems and asked to think of as many solutions to these problems as they can. For example, if all schools were abolished, what would you do to try to become educated?Common problems taskThis task is an adoption of Guilford’s (1951) Test designed to assess the ability to see defects, needs and deficiencies and found to be one of the tests of the factors termed sensitivity to problems. Subjects are instructed that they will be given common situations and that they will be asked to think of as many problems as they can that may arise in connection with these situations. For example, doing homework while going to school in the morning.Improvement taskThis test was adopted from Guilford’s (1952) apparatus test which was designed to assess ability to see defects and all aspects of sensitivity to problems. In this task the subjects are given a list of common objects and are asked to suggest as many ways as they can to improve each object. They are asked not to bother about whether or not it is possible to implement the change thought of.Mother- Hubbard problemThis task was conceived as an adoption of the situations task for oral administration in the primary grades and also useful for older groups. This test has stimulated a number of ideas concerning factors which inhibit the development of ideas.Imaginative stories taskIn this task the child is told to write the most interesting and exciting story he can think of. Topics are suggested (e.g., the dog that did not bark); or the child may use his own ideas.Cow jumping problemsThe Cow jumping problem is a companion task for the Mother- Hubbard problem and has been administered to the same groups under the same conditions and scored according to the similar procedures. The task is to think of all possible things which might have happened when the cow jumped over the moon.Verbal tasks using nonverbalstimuli[edit]Ask and guess taskThe ask and guess task requires the individual first to ask questions about a picture –questions which cannot be answered by just looking at the picture. Next he is asked to make guesses or formulate hypotheses about the possible causes of the event depicted, and then their consequences both immediate and remote.Product improvement taskIn this task common toys are used and children are asked to think of as many improvements as they can which would make the toy ‘more fun to play with’. Subjects are then asked to think of unusual uses of these toys other than 'something to play with’.Unusual uses taskIn this task, along with the product improvement task another task (unusual uses) is used. The child is asked to think of the cleverest, most interesting and most unusual uses of the given toy, other than as a plaything. These uses could be for the toy as it is, or for the toy as changed.Non-verbal tasks[edit]Incomplete figures taskIt is an adaptation of the ‘Drawing completion test’ developed by Kate Franck and used by Barron (1958). On an ordinary white paper an area of fifty four square inches is divided into six squares each containing a different stimulus figure. The subjects are asked to sketch some novel objects or design by adding as many lines as they can to the six figures.Picture construction task or shapestaskIn this task the children are given shape of a triangle or a jelly bean and a sheet of white paper. The children are asked to think of a picture in which the given shape is an integral part. They should paste it wherever they want on the white sheet and add lines with pencil to make any novel picture. They have to think of a name for the picture and write it at the bottom.Circles and squares taskIt was originally designed as a nonverbal test of ideational fluency and flexibility, then modified in such a way as to stress originality and elaboration. Two printed forms are used in the test. In one form, the subject is confronted with a page of forty two circles and asked to sketch objects or pictures which have circles as a major part. In the alternate form, squares are used instead of circles.Creative design task Hendrickson has designed it which seems to be promising, but scoring procedures are being tested but have not been perfected yet. The materials consist of circles and strips of various sizes and colours, a four page booklet, scissors and glue. Subjects are instructedto construct pictures or designs, making use of all of the coloured circles and strips with a thirty minute time limit. Subjects may use one, two, three, or four pages; alter the circles and strips or use them as they are; add other symbols with pencil or crayon.。
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Abstract: Blowfish, a new secret-key block cipher, is proposed. It is a Feistel network, iterating a simple encryption function 16 times. The block size is 64 bits, and the key can be any length up to 448 bits. Although there is a complex initialization phase required before any encryption can take place, the actual encryption of data is very efficient on large microprocessors.
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Random bit generation. The algorithm should be efficient in producing single random bits. Packet encryption. The algorithm should be efficient in encrypting packetsized data. (An ATM packet has a 48-byte data field.) It should implementable in an application where successive packets may be encrypted or decrypted with different keys. Hashing. The algorithm should be efficient in being converted to a one-way hash function. PLATFORMS A standard encryption algorithm must be implementable on a variety of different platforms, each with their own requirements. These include: Special hardware. The algorithm should be efficiently implementable in custom VLSI hardware. Large processors. While dedicated hardware will always be used for the fastest applications, software implementations are more common. The algorithm should be efficient on 32-bit microprocessors with 4 kbyte program and data caches. Medium-size processors. The algorithm should run on microcontrollers and other medium-size processors, such as the 68HC11. Small processors. It should be possible to implement the algorithm on smart cards, even inefficiently. The requirements for small processors are the most difficult. RAM and ROM limitations are severe for this platform. Also, efficiency is more important on these small machines. Workstations double their capacity almost annually. Small embedded systems are the same year after year, and there is little capacity to spare. If there is a choice, the extra computation burden should be on large processors rather than small processors. ADDITIONAL REQUIREMENTS These additional requirements should, if possible, be levied on a standard encryption algorithm. The algorithm should be simple to code. Experiences with DES [19] show that programmers will often make implementation mistakes if the algorithm is complicated. If possible, the algorithm should be robust against these mistakes. The algorithm should have a flat keyspace, allowing any random bit string of
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the required length to be a possible key. There should be no weak keys. The algorithm should facilitate easy key-management for software implementations. Software implementations of DES generally use poor key management techniques. In particular, the password that the user types in becomes the key. This means that although DES has a theoretical keyspace of 256, the actual keyspace is limited to keys constructed with the 95 characters of printable ASCII. Additionally, keys corresponding to words and near words are much more likely. Th.e algorithm should be easily modifiable for different levels of security, both minimum and maximum requirements. All operations should manipulate data in byte-sized blocks. Where possible, operations should manipulate data in 32-bit blocks. DESIGN DECISIONS Based on the above parameters, we have made these design decisions. The algorithm should: Manipulate data in large blocks, preferably 32 bits in size (and not in single bits, such as DES). Have either a 64-bit or a 128-bit block size. Have a scalable key, from 32 bits to at least 256 bits. Use simple operations that are efficient on microprocessors: e.g., exclusiveor, addition, table lookup, modular-multiplication. It should not use variablelength shifts or bit-wise permutations, or conditional jumps. Be implementable on an 8-bit processor with a minimum of 24 bytes of RAM (in addition to the RAM required to store the key) and 1 kilobyte of ROM. Employ precomputable subkeys. On large-memory systems, these subkeys can be precomputed for faster operation. Not precomputing the subkeys will result in slower operation, but it should still be possible to encrypt data without any precomputations. Consist of a variable number of iterations. For applications with a small key size, the trade-off between the complexity of a brute-force attack and a differential attack make a large number of iterations superfluous. Hence, it should be possible to reduce the number of iterations with no loss of security (beyond that of the reduced key size). If possible, have no weak keys. If not possible, the proportion of weak keys should be small enough to make it unlikely to choose one at random. Also,