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GSPBOX_-Atoolboxforsignalprocessingongraphs_GSPBOX:A toolbox for signal processing on graphsNathanael Perraudin,Johan Paratte,David Shuman,Lionel Martin Vassilis Kalofolias,Pierre Vandergheynst and David K.HammondMarch 16,2016AbstractThis document introduces the Graph Signal Processing Toolbox (GSPBox)a framework that can be used to tackle graph related problems with a signal processing approach.It explains the structure and the organization of this software.It also contains a general description of the important modules.1Toolbox organizationIn this document,we brie?y describe the different modules available in the toolbox.For each of them,the main functions are brie?y described.This chapter should help making the connection between the theoretical concepts introduced in [7,9,6]and the technical documentation provided with the toolbox.We highly recommend to read this document and the tutorial before using the toolbox.The documentation,the tutorials and other resources are available on-line 1.The toolbox has ?rst been implemented in MATLAB but a port to Python,called the PyGSP,has been made recently.As of the time of writing of this document,not all the functionalities have been ported to Python,but the main modules are already available.In the following,functions pre?xed by [M]:refer to the MATLAB implementation and the ones pre?xed with [P]:refer to the Python implementation. 1.1General structure of the toolbox (MATLAB)The general design of the GSPBox focuses around the graph object [7],a MATLAB structure containing the necessary infor-mations to use most of the algorithms.By default,only a few attributes are available (see section 2),allowing only the use of a subset of functions.In order to enable the use of more algorithms,additional ?elds can be added to the graph structure.For example,the following line will compute the graph Fourier basis enabling exact ?ltering operations.1G =gsp_compute_fourier_basis(G);Ideally,this operation should be done on the ?y when exact ?ltering is required.Unfortunately,the lack of well de?ned class paradigm in MATLAB makes it too complicated to be implemented.Luckily,the above formulation prevents any unnecessary data copy of the data contained in the structure G .In order to avoid name con?icts,all functions in the GSPBox start with [M]:gsp_.A second important convention is that all functions applying a graph algorithm on a graph signal takes the graph as ?rst argument.For example,the graph Fourier transform of the vector f is computed by1fhat =gsp_gft(G,f);1Seehttps://lts2.epfl.ch/gsp/doc/for MATLAB and https://lts2.epfl.ch/pygsp for Python.The full documentation is also avail-able in a single document:https://lts2.epfl.ch/gsp/gspbox.pdf1a r X i v :1408.5781v 2 [c s .I T ] 15 M a r 2016The graph operators are described in section4.Filtering a signal on a graph is also a linear operation.However,since the design of special?lters(kernels)is important,they are regrouped in a dedicated module(see section5).The toolbox contains two additional important modules.The optimization module contains proximal operators,projections and solvers compatible with the UNLocBoX[5](see section6).These functions facilitate the de?nition of convex optimization problems using graphs.Finally,section??is composed of well known graph machine learning algorithms.1.2General structure of the toolbox(Python)The structure of the Python toolbox follows closely the MATLAB one.The major difference comes from the fact that the Python implementation is object-oriented and thus allows for a natural use of instances of the graph object.For example the equivalent of the MATLAB call:1G=gsp_estimate_lmax(G);can be achieved using a simple method call on the graph object:1G.estimate_lmax()Moreover,the use of class for the"graph object"allows to compute additional graph attributes on the?y,making the code clearer as its MATLAB equivalent.Note though that functionalities are grouped into different modules(one per section below) and that several functions that work on graphs have to be called directly from the modules.For example,one should write:1layers=pygsp.operators.kron_pyramid(G,levels)This is the case as soon as the graph is the structure on which the action has to be performed and not our principal focus.In a similar way to the MATLAB implementation using the UNLocBoX for the convex optimization routines,the Python implementation uses the PyUNLocBoX,which is the Python port of the UNLocBoX. 2GraphsThe GSPBox is constructed around one main object:the graph.It is implemented as a structure in Matlab and as a class in Python.It stores the nodes,the edges and other attributes related to the graph.In the implementation,a graph is fully de?ned by the weight matrix W,which is the main and only required attribute.Since most graph structures are far from fully connected, W is implemented as a sparse matrix.From the weight matrix a Laplacian matrix is computed and stored as an attribute of the graph object.Different other attributes are available such as plotting attributes,vertex coordinates,the degree matrix,the number of vertices and edges.The list of all attributes is given in table1.2Attribute Format Data type DescriptionMandatory?eldsW N x N sparse matrix double Weight matrix WL N x N sparse matrix double Laplacian matrixd N x1vector double The diagonal of the degree matrixN scalar integer Number of verticesNe scalar integer Number of edgesplotting[M]:structure[P]:dict none Plotting parameterstype text string Name,type or short descriptiondirected scalar[M]:logical[P]:boolean State if the graph is directed or notlap_type text string Laplacian typeOptional?eldsA N x N sparse matrix[M]:logical[P]:boolean Adjacency matrixcoords N x2or N x3matrix double Vectors of coordinates in2D or3D.lmax scalar double Exact or estimated maximum eigenvalue U N x N matrix double Matrix of eigenvectorse N x1vector double Vector of eigenvaluesmu scalar double Graph coherenceTable1:Attributes of the graph objectThe easiest way to create a graph is the[M]:gsp_graph[P]:pygsp.graphs.Graph function which takes the weight matrix as input.This function initializes a graph structure by creating the graph Laplacian and other useful attributes.Note that by default the toolbox uses the combinatorial de?nition of the Laplacian operator.Other Laplacians can be computed using the[M]:gsp_create_laplacian[P]:pygsp.gutils.create_laplacian function.Please note that almost all functions are dependent of the Laplacian de?nition.As a result,it is important to select the correct de?nition at? rst.Many particular graphs are also available using helper functions such as:ring,path,comet,swiss roll,airfoil or two moons. In addition,functions are provided for usual non-deterministic graphs suchas:Erdos-Renyi,community,Stochastic Block Model or sensor networks graphs.Nearest Neighbors(NN)graphs form a class which is used in many applications and can be constructed from a set of points (or point cloud)using the[M]:gsp_nn_graph[P]:pygsp.graphs.NNGraph function.The function is highly tunable and can handle very large sets of points using FLANN[3].Two particular cases of NN graphs have their dedicated helper functions:3D point clouds and image patch-graphs.An example of the former can be seen in thefunction[M]:gsp_bunny[P]:pygsp.graphs.Bunny.As for the second,a graph can be created from an image by connecting similar patches of pixels together.The function[M]:gsp_patch_graph creates this graph.Parameters allow the resulting graph to vary between local and non-local and to use different distance functions [12,4].A few examples of the graphs are displayed in Figure1.3PlottingAs in many other domains,visualization is very important in graph signal processing.The most basic operation is to visualize graphs.This can be achieved using a call to thefunction[M]:gsp_plot_graph[P]:pygsp.plotting.plot_graph. In order to be displayable,a graph needs to have2D(or3D)coordinates(which is a?eld of the graph object).Some graphs do not possess default coordinates(e.g.Erdos-Renyi).The toolbox also contains routines to plot signals living on graphs.The function dedicated to this task is[M]:gsp_plot_ signal[P]:pygsp.plotting.plot_signal.For now,only1D signals are supported.By default,the value of the signal is displayed using a color coding,but bars can be displayed by passing parameters.3Figure 1:Examples of classical graphs :two moons (top left),community (top right),airfoil (bottom left)and sensor network (bottom right).The third visualization helper is a function to plot ?lters (in the spectral domain)which is called [M]:gsp_plot_filter [P]:pygsp.plotting.plot_filter .It also supports ?lter-banks and allows to automatically inspect the related frames.The results obtained using these three plotting functions are visible in Fig.2.4OperatorsThe module operators contains basics spectral graph functions such as Fourier transform,localization,gradient,divergence or pyramid decomposition.Since all operator are based on the Laplacian de? nition,the necessary underlying objects (attributes)are all stored into a single object:the graph.As a ?rst example,the graph Fourier transform [M]:gsp_gft [P]:pygsp.operators.gft requires the Fourier basis.This attribute can be computed with the function [M]:gsp_compute_fourier_basis[P]:/doc/c09ff3e90342a8956bec0975f46527d3240ca692.html pute_fourier_basis [9]that adds the ?elds U ,e and lmax to the graph structure.As a second example,since the gradient and divergence operate on the edges of the graph,a search on the edge matrix is needed to enable the use of these operators.It can be done with the routines [M]:gsp_adj2vec[P]:pygsp.operators.adj2vec .These operations take time and should4Figure 2:Visualization of graph and signals using plotting functions.NameEdge derivativefe (i,j )Laplacian matrix (operator)Available Undirected graph Combinatorial LaplacianW (i,j )(f (j )?f (i ))D ?WV Normalized Laplacian W (i,j ) f (j )√d (j )f (i )√d (i )D ?12(D ?W )D ?12V Directed graph Combinatorial LaplacianW (i,j )(f (j )?f (i ))12(D ++D ??W ?W ?)V Degree normalized Laplacian W (i,j ) f (j )√d ?(j )?f (i )√d +(i )I ?12D ?12+[W +W ?]D ?12V Distribution normalized Laplacianπ(i ) p (i,j )π(j )f (j )? p (i,j )π(i )f (i )12 Π12PΠ12+Π?12P ?Π12 VTable 2:Different de?nitions for graph Laplacian operator and their associated edge derivative.(For directed graph,d +,D +and d ?,D ?de?ne the out degree and in-degree of a node.π,Πis the stationary distribution of the graph and P is a normalized weight matrix W .For sake of clarity,exact de?nition of those quantities are not given here,but can be found in [14].)be performed only once.In MATLAB,these functions are called explicitly by the user beforehand.However,in Python they are automatically called when needed and the result stored as an attribute. The module operator also includes a Multi-scale Pyramid Transform for graph signals [6].Again,it works in two steps.Firstthe pyramid is precomputed with [M]:gsp_graph_multiresolution [P]:pygsp.operators.graph_multiresolution .Second the decomposition of a signal is performed with [M]:gsp_pyramid_analysis [P]:pygsp.operators.pyramid_analysis .The reconstruction uses [M]:gsp_pyramid_synthesis [P]:pygsp.operators.pyramid_synthesis .The Laplacian is a special operator stored as a sparse matrix in the ?eld L of the graph.Table 2summarizes the available de?nitions.We are planning to implement additional ones.5FiltersFilters are a special kind of linear operators that are so prominent in the toolbox that they deserve their own module [9,7,2,8,2].A ?lter is simply an anonymous function (in MATLAB)or a lambda function (in Python)acting element-by-element on the input.In MATLAB,a ?lter-bank is created simply by gathering these functions together into a cell array.For example,you would write:51%g(x)=x^2+sin(x)2g=@(x)x.^2+sin(x);3%h(x)=exp(-x)4h=@(x)exp(-x);5%Filterbank composed of g and h6fb={g,h};The toolbox contains many prede?ned design of?lter.They all start with[M]:gsp_design_in MATLAB and are in the module[P]:pygsp.filters in Python.Once a?lter(or a?lter-bank)is created,it can be applied to a signal with[M]: gsp_filter_analysis in MATLAB and a call to the method[P]:analysis of the?lter object in Python.Note that the toolbox uses accelerated algorithms to scale almost linearly with the number of sample[11].The available type of?lter design of the GSPBox can be classi?ed as:Wavelets(Filters are scaled version of a mother window)Gabor(Filters are shifted version of a mother window)Low passlter(Filters to de-noise a signal)High pass/Low pass separationlterbank(tight frame of2lters to separate the high frequencies from the low ones.No energy is lost in the process)Additionally,to adapt the?lter to the graph eigen-distribution,the warping function[M]:gsp_design_warped_translates [P]:pygsp.filters.WarpedTranslates can be used[10].6UNLocBoX BindingThis module contains special wrappers for the UNLocBoX[5].It allows to solve convex problems containing graph terms very easily[13,15,14,1].For example,the proximal operator of the graph TV norm is given by[M]:gsp_prox_tv.The optimization module contains also some prede?ned problems such as graph basis pursuit in[M]:gsp_solve_l1or wavelet de-noising in[M]:gsp_wavelet_dn.There is still active work on this module so it is expected to grow rapidly in the future releases of the toolbox.7Toolbox conventions7.1General conventionsAs much as possible,all small letters are used for vectors(or vector stacked into a matrix)and capital are reserved for matrices.A notable exception is the creation of nearest neighbors graphs.A variable should never have the same name as an already existing function in MATLAB or Python respectively.This makes the code easier to read and less prone to errors.This is a best coding practice in general,but since both languages allow the override of built-in functions,a special care is needed.All function names should be lowercase.This avoids a lot of confusion because some computer architectures respect upper/lower casing and others do not.As much as possible,functions are named after the action they perform,rather than the algorithm they use,or the person who invented it.No global variables.Global variables makes it harder to debug and the code is harder to parallelize.67.2MATLABAll function start by gsp_.The graph structure is always therst argument in the function call.Filters are always second.Finally,optional parameter are last.In the toolbox,we do use any argument helper functions.As a result,optional argument are generally stacked into a graph structure named param.If a transform works on a matrix,it will per default work along the columns.This is a standard in Matlab(fft does this, among many other functions).Function names are traditionally written in uppercase in MATLAB documentation.7.3PythonAll functions should be part of a module,there should be no call directly from pygsp([P]:pygsp.my_function).Inside a given module,functionalities can be further split in differentles regrouping those that are used in the same context.MATLAB’s matrix operations are sometimes ported in a different way that preserves the efciency of the code.When matrix operations are necessary,they are all performed through the numpy and scipy libraries.Since Python does not come with a plotting library,we support both matplotlib and pyqtgraph.One should install the required libraries on his own.If both are correctly installed,then pyqtgraph is favoured unless speci?cally speci?ed. AcknowledgementsWe would like to thanks all coding authors of the GSPBOX.The toolbox was ported in Python by Basile Chatillon,Alexandre Lafaye and Nicolas Rod.The toolbox was also improved by Nauman Shahid and Yann Sch?nenberger.References[1]M.Belkin,P.Niyogi,and V.Sindhwani.Manifold regularization:A geometric framework for learning from labeled and unlabeledexamples.The Journal of Machine Learning Research,7:2399–2434,2006.[2] D.K.Hammond,P.Vandergheynst,and R.Gribonval.Wavelets on graphs via spectral graph theory.Applied and ComputationalHarmonic Analysis,30(2):129–150,2011.[3]M.Muja and D.G.Lowe.Scalable nearest neighbor algorithms for high dimensional data.Pattern Analysis and Machine Intelligence,IEEE Transactions on,36,2014.[4]S.K.Narang,Y.H.Chao,and A.Ortega.Graph-wavelet?lterbanks for edge-aware image processing.In Statistical Signal ProcessingWorkshop(SSP),2012IEEE,pages141–144.IEEE,2012.[5]N.Perraudin,D.Shuman,G.Puy,and P.Vandergheynst.UNLocBoX A matlab convex optimization toolbox using proximal splittingmethods.ArXiv e-prints,Feb.2014.[6] D.I.Shuman,M.J.Faraji,and P.Vandergheynst.A multiscale pyramid transform for graph signals.arXiv preprint arXiv:1308.4942,2013.[7] D.I.Shuman,S.K.Narang,P.Frossard,A.Ortega,and P.Vandergheynst.The emerging?eld of signal processing on graphs:Extendinghigh-dimensional data analysis to networks and other irregular domains.Signal Processing Magazine,IEEE,30(3):83–98,2013.7[8] D.I.Shuman,B.Ricaud,and P.Vandergheynst.A windowed graph Fourier transform.Statistical Signal Processing Workshop(SSP),2012IEEE,pages133–136,2012.[9] D.I.Shuman,B.Ricaud,and P.Vandergheynst.Vertex-frequency analysis on graphs.arXiv preprint arXiv:1307.5708,2013.[10] D.I.Shuman,C.Wiesmeyr,N.Holighaus,and P.Vandergheynst.Spectrum-adapted tight graph wavelet and vertex-frequency frames.arXiv preprint arXiv:1311.0897,2013.[11] A.Susnjara,N.Perraudin,D.Kressner,and P.Vandergheynst.Accelerated?ltering on graphs using lanczos method.arXiv preprintarXiv:1509.04537,2015.[12] F.Zhang and E.R.Hancock.Graph spectral image smoothing using the heat kernel.Pattern Recognition,41(11):3328–3342,2008.[13] D.Zhou,O.Bousquet,/doc/c09ff3e90342a8956bec0975f46527d3240ca692.html l,J.Weston,and B.Sch?lkopf.Learning with local and global consistency.Advances in neural informationprocessing systems,16(16):321–328,2004.[14] D.Zhou,J.Huang,and B.Sch?lkopf.Learning from labeled and unlabeled data on a directed graph.In the22nd international conference,pages1036–1043,New York,New York,USA,2005.ACM Press.[15] D.Zhou and B.Sch?lkopf.A regularization framework for learning from graph data.2004.8。

Package‘doRNG’January16,2023Type PackageTitle Generic Reproducible Parallel Backend for'foreach'LoopsVersion1.8.6Encoding UTF-8Description Provides functions to performreproducible parallel foreach loops,using independentrandom streams as generated by L'Ecuyer's combinedmultiple-recursive generator[L'Ecuyer(1999),<DOI:10.1287/opre.47.1.159>].It enables to easily convert standard'%dopar%'loops intofully reproducible loops,independently of the numberof workers,the task scheduling strategy,or the chosenparallel environment and associated foreach backend.License GPL(>=2)LazyLoad yesURL https://renozao.github.io/doRNG/BugReports https:///renozao/doRNG/issuesVignetteBuilder knitrDepends R(>=3.0.0),foreach,rngtools(>=1.5)Imports stats,utils,iteratorsSuggests doParallel,doMPI,doRedis,rbenchmark,devtools,knitr,rbibutils(>=1.3),testthat,pkgmaker(>=0.32.7),covrRoxygenNote7.2.3NeedsCompilation noAuthor Renaud Gaujoux[aut,cre]Maintainer Renaud Gaujoux<**********************>Repository CRANDate/Publication2023-01-1611:00:03UTC12doRNG-packageR topics documented:doRNG-package (2)doRNGversion (3)registerDoRNG (5)%dorng% (6)Index8doRNG-package Generic Reproducible Parallel Backend for foreach LoopsDescriptionThe doRNG package provides functions to perform reproducible parallel foreach loops,using inde-pendent random streams as generated by L’Ecuyer’s combined multiple-recursive generator(L’Ecuyer(1999)).It enables to easily convert standard%dopar%loops into fully reproducible loops,indepen-dently of the number of workers,the task scheduling strategy,or the chosen parallel environmentand associated foreach backend.It has been tested with the following foreach backend:doMC,doSNOW,doMPI.ReferencesL’Ecuyer P(1999).“Good Parameters and Implementations for Combined Multiple Recursive Ran-dom Number Generators.”_Operations Research_,*47*(1),159-164.ISSN0030-364X,doi:10.1287/opre.47.1.159 <https:///10.1287/opre.47.1.159>.See AlsodoRNG,RNGseqExamples#register parallel backendlibrary(doParallel)cl<-makeCluster(2)registerDoParallel(cl)##standard%dopar%loop are not reproducibleset.seed(123)r1<-foreach(i=1:4)%dopar%{runif(1)}set.seed(123)r2<-foreach(i=1:4)%dopar%{runif(1)}identical(r1,r2)##%dorng%loops_are_reproducibleset.seed(123)r1<-foreach(i=1:4)%dorng%{runif(1)}set.seed(123)r2<-foreach(i=1:4)%dorng%{runif(1)}identical(r1,r2)#alternative way of seedinga1<-foreach(i=1:4,.options.RNG=123)%dorng%{runif(1)}a2<-foreach(i=1:4,.options.RNG=123)%dorng%{runif(1)}identical(a1,a2)&&identical(a1,r1)##sequences of%dorng%loops_are_reproducibleset.seed(123)s1<-foreach(i=1:4)%dorng%{runif(1)}s2<-foreach(i=1:4)%dorng%{runif(1)}identical(s1,r1)&&!identical(s1,s2)set.seed(123)s1.2<-foreach(i=1:4)%dorng%{runif(1)}s2.2<-foreach(i=1:4)%dorng%{runif(1)}identical(s1,s1.2)&&identical(s2,s2.2)##Non-invasive way of converting%dopar%loops into reproducible loopsregisterDoRNG(123)s3<-foreach(i=1:4)%dopar%{runif(1)}s4<-foreach(i=1:4)%dopar%{runif(1)}identical(s3,s1)&&identical(s4,s2)stopCluster(cl)doRNGversion Back Compatibility Option for doRNGDescriptionSets the behaviour of%dorng%foreach loops from a given version number.UsagedoRNGversion(x)Argumentsx version number to switch to,or missing to get the currently active version num-ber,or NULL to reset to the default behaviour,i.e.of the latest version.Valuea character string If x is missing this function returns the version number from the current behaviour.If x is specified,the function returns the old value of the version number(invisible).Behaviour changes in versions1.4The behaviour of doRNGseed,and therefore of%dorng%loops,changed in the case where thecurrent RNG was L’ing set.seed before a non-seeded loop used not to be identical to seeding via.options.RNG.Another bug was that non-seeded loops would share most of their RNG seed!1.7.4Prior to this version,in the case where the RNG had not been called yet,thefirst seeded%dorng%loops would not give the identical results as subsequent loops despite using the same seed(see https:///renozao/doRNG/issues/12).This has beenfixed in version1.7.4,where the RNG is called once(sample(NA)),whenever the.Random.seed is not found in global environment.Examples##Seeding when current RNG is L Ecuyer-CMRGRNGkind("L Ecuyer")doRNGversion("1.4")#in version>=1.4seeding behaviour changed to fix a bugset.seed(123)res<-foreach(i=1:3)%dorng%runif(1)res2<-foreach(i=1:3)%dorng%runif(1)stopifnot(!identical(attr(res, rng )[2:3],attr(res2, rng )[1:2]))res3<-foreach(i=1:3,.options.RNG=123)%dorng%runif(1)stopifnot(identical(res,res3))#buggy behaviour in version<1.4doRNGversion("1.3")res<-foreach(i=1:3)%dorng%runif(1)res2<-foreach(i=1:3)%dorng%runif(1)stopifnot(identical(attr(res, rng )[2:3],attr(res2, rng )[1:2]))res3<-foreach(i=1:3,.options.RNG=123)%dorng%runif(1)stopifnot(!identical(res,res3))#restore default RNGRNGkind("default")#restore to current doRNG versiondoRNGversion(NULL)registerDoRNG5registerDoRNG Registering doRNG for Persistent Reproducible Parallel ForeachLoopsDescriptionregisterDoRNG registers the doRNG foreach backend.Subsequent%dopar%loops are then per-formed using the previously registered foreach backend,but are internally performed as%dorng% loops,making them fully reproducible.UsageregisterDoRNG(seed=NULL,once=TRUE)Argumentsseed a numerical seed to use(as a single or6-length numerical value)once a logical to indicate if the RNG sequence should be seeded at the beginning of each loop or only at thefirst loop.DetailsBriefly,the RNG is set,before each iteration,with seeds for L’Ecuyer’s CMRG that overall generatea reproducible sequence of statistically independent random streams.Note that(re-)registering a foreach backend other than doRNG,after a call to registerDoRNG disables doRNG–which then needs to be registered.ValueThe value returned by foreach::setDoParSee Also%dorng%Exampleslibrary(doParallel)cl<-makeCluster(2)registerDoParallel(cl)#One can make reproducible loops using the%dorng%operatorr1<-foreach(i=1:4,.options.RNG=1234)%dorng%{runif(1)}#or convert%dopar%loops using registerDoRNGregisterDoRNG(1234)r2<-foreach(i=1:4)%dopar%{runif(1)}identical(r1,r2)stopCluster(cl)#Registering another foreach backend disables doRNGcl<-makeCluster(2)registerDoParallel(cl)set.seed(1234)s1<-foreach(i=1:4)%dopar%{runif(1)}set.seed(1234)s2<-foreach(i=1:4)%dopar%{runif(1)}identical(s1,s2)#doRNG is re-nabled by re-registering itregisterDoRNG()set.seed(1234)r3<-foreach(i=1:4)%dopar%{runif(1)}identical(r2,r3)#NB:the results are identical independently of the task scheduling#(r2used2nodes,while r3used3nodes)#argument once=FALSE reseeds doRNG s seed at the beginning of each loopregisterDoRNG(1234,once=FALSE)r1<-foreach(i=1:4)%dopar%{runif(1)}r2<-foreach(i=1:4)%dopar%{runif(1)}identical(r1,r2)#Once doRNG is registered the seed can also be passed as an option to%dopar%r1.2<-foreach(i=1:4,.options.RNG=456)%dopar%{runif(1)}r2.2<-foreach(i=1:4,.options.RNG=456)%dopar%{runif(1)}identical(r1.2,r2.2)&&!identical(r1.2,r1)stopCluster(cl)%dorng%Reproducible Parallel Foreach BackendDescription%dorng%is a foreach operator that provides an alternative operator%dopar%,which enable repro-ducible foreach loops to be performed.Usageobj%dorng%exArgumentsobj a foreach object as returned by a call to foreach.ex the R expression to evaluate.Value%dorng%returns the result of the foreach loop.See foreach::%dopar%.The whole sequence of RNG seeds is stored in the result object as an e attr(res, rng )to retrieve it.Global optionsThese options are for advanced users that develop‘foreach backends:•’doRNG.rng_change_warning_skip’:if set to a single logical FALSE/TRUE,it indicates whethera warning should be thrown if the RNG seed is changed by the registered parallel backend(de-fault=FALSE).Set it to TRUE if you know that running your backend will change the RNG stateand want to disable the warning.This option can also be set to a character vector that specifiesthe name(s)of the backend(s)for which the warning should be skipped.See Alsoforeach,doParallel,registerDoParallel,doMPIExampleslibrary(doParallel)cl<-makeCluster(2)registerDoParallel(cl)#standard%dopar%loops are_not_reproducibleset.seed(1234)s1<-foreach(i=1:4)%dopar%{runif(1)}set.seed(1234)s2<-foreach(i=1:4)%dopar%{runif(1)}identical(s1,s2)#single%dorng%loops are reproducibler1<-foreach(i=1:4,.options.RNG=1234)%dorng%{runif(1)}r2<-foreach(i=1:4,.options.RNG=1234)%dorng%{runif(1)}identical(r1,r2)#the sequence os RNG seed is stored as an attributeattr(r1, rng )#stop clusterstopCluster(cl)#More examples can be found in demo doRNG##Not run:demo( doRNG )##End(Not run)Index∗packagedoRNG-package,2%dorng%,5,6doMPI,7doParallel,7doRNG,2doRNG-package,2doRNGversion,3foreach,6,7foreach::%dopar%,7foreach::setDoPar,5 registerDoParallel,7 registerDoRNG,5RNGseq,28。

Formal Description of OCL Specification Patterns for Behavioral Specification of Software ComponentsJörg AckermannChair of Business Informatics and Systems Engineering,University of Augsburg, Universitätsstr. 16, 86135 Augsburgjoerg.ackermann@wiwi.uni-augsburg.deAbstract. The Object Constraint Language (OCL) is often used for behavioralspecification of software components. One current problem in specifying be-havioral aspects comes from the fact that editing OCL constraints manually istime consuming and error-prone. To simplify constraint definition we proposeto use specification patterns for which OCL constraints can be generated auto-matically. In this paper we outline this solution proposal and develop a wayhow to formally describe such specification patterns on which a library of reus-able OCL specifications is based.Keywords. Software Component Specification, OCL, Specification Patterns1 IntroductionThe Object Constraint Language (OCL) [20] has great relevance for component-based software engineering (CBSE): A crucial prerequisite for applying CBSE successfully is an appropriate and standardized specification of software components [27]. Behav-ioral aspects of components are often specified using OCL (see Sect. 2). From this results one of the current problems in component specifications: Editing OCL con-straints manually is time consuming and error-prone (see Sect. 3).To simplify constraint definition we propose to utilize specification patterns for which OCL constraints can be generated automatically (see Sect. 4). [4] identifies nine patterns that frequently occur in behavioral specifications of software components. In this paper we develop a solution how to formally describe specification patterns that enable a precise pattern specification and aid the implementation of constraint genera-tors (Sect. 5). We conclude with discussion of related work (Sect. 6) and a summary (Sect. 7).The main contributions of this paper are: the proposal to use specification patterns to simplify component specifications and the formal description of specification pat-terns by use of so called OCL pattern functions – together with the identified patterns we obtain a library of reusable OCL specifications.The results are not specific for software components and might therefore be interesting for any user of OCL con-straints.2 Specification of Software ComponentsThe basic paradigm of component-based software engineering is to decouple the pro-duction of components (development for reuse) from the production of complete sys-tems out of components (development by reuse). Applying CBSE promises (amongst others) a shorter time to market, increased adaptability and reduced development costs [8,25].A critical success factor for CBSE is the appropriate and standardized specification of software components: the specification is prerequisite for a composition methodol-ogy and tool support [23] as well as for reuse of components by third parties [26]. With specification of a component we denote the complete, unequivocal and precise description of its external view - that is which services a component provides under which conditions [27].Various authors addressed specifications for specific tasks of the development process as e.g. design and implementation [9,10], component adaptation [28] or com-ponent selection [15]. Approaches towards comprehensive specification of software components are few and include [7,23,27]. Objects to be specified are e.g. business terms, business tasks (domain-related perspective), interface signatures, behavior and coordination constraints (logical perspective) and non-functional attributes (physical perspective).Behavioral specifications (which are topic of this paper) describe how the compo-nent behaves in general and in borderline cases. This is achieved by defining con-straints (invariants, pre- and postconditions) based on the idea of designing applica-tions by contract [18]. OCL is the de-facto standard technique to express such con-straints – cf. e.g. [9,10,23,27].Fig. 1. Interface specification of component SalesOrderProcessingTo illustrate how behavioral aspects of software components are specified we intro-duce a simplified exemplary component SalesOrderProcessing. The business task of the component is to manage sales orders. This component is used as example through-out the rest of the paper.16 J. AckermannFig. 1 shows the interface specification of SalesOrderProcessing using UML [21]. We see that the component offers the interface ISalesOrder with operations to create, check, cancel or retrieve specific sales orders. The data types needed are also defined in Fig. 1. Note that in practice the component could have additional operations and might offer additional order properties. For sake of simplicity we restricted ourselves to the simple form shown in Fig. 1 which will be sufficient as example for this paper. To specify the information objects belonging to the component (on a logical level) one can use a specification data model which is realized as an UML type diagram and is part of the behavioral specification [3]. Fig. 2 displays such a model for the compo-nent SalesOrderProcessing. It shows that the component manages sales orders (with attributes id, date of order, status, customer id) and sales order items (with attributes id, quantity, product id) and that there is a one-to-many relationship between sales orders and sales order items.18 J. Ackermanncan only be called for a sales order that already exists in the component. (More pre-cise: there must exist a sales order which id equals the value of the input parameter orderId. Note that the invariant guarantees that there is at most one such sales order). context SalesOrderinv: SalesOrder.allInstances()->forAll(i1, i2 | i1 <> i2implies i1.id <> i2.id)context ISalesOrder::getOrderData(orderId: string, orderHeader: OrderHeaderData, orderItem: OrderItemData, orderStatus: Order-Status)pre: SalesOrder.allInstances()->exists(id = orderId)Fig. 3. (Partial) Behavioral specification of component SalesOrderProcessing3 Problems in Behavioral Specification of ComponentsMost component specification approaches recommend notations in formal languages since they promise a common understanding of specification results across different developers and companies. The use of formal methods, however, is not undisputed. Some authors argue that the required effort is too high and the intelligibility of the specification results is too low – for a discussion of advantages and liabilities of for-mal methods compare [14].The disadvantages of earlier formal methods are reduced by UML OCL [20]: The notation of OCL has a simple structure and is oriented towards the syntax of object-oriented programming languages. Software developers can therefore handle OCL much easier than earlier formal methods that were based on set theory and predicate logic. This is one reason why OCL is recommended by many authors for the specifica-tion of software components.Despite its advantages OCL can not solve all problems associated with the use of formal methods: One result of two case studies specifying business components [1,2] was the insight that editing OCL constraints manually is nevertheless time consuming and error-prone. Similar experiences were made by other authors that use OCL con-straints in specifications (outside the component area), e.g. [13,17]. They conclude that it takes a considerable effort to master OCL and use it effectively.It should be noted that behavioral aspects (where OCL is used) have a great impor-tance for component specifications: In the specification of a rather simple component in case study [2], for example, the behavioral aspects filled 57 (of altogether 81) pages and required a tremendous amount of work. For component specifications to be prac-tical it is therefore mandatory to simplify the authoring of OCL constraints.Formal Description of OCL Specification Patterns 194 Solution Proposal: Utilizing Specification PatternsSolution strategies to simplify OCL specifications include better tool support (to re-duce errors) and an automation of constraint editing (to reduce effort) – the latter can e.g. be based on use cases or on predefined specification patterns (compare Sect. 6). To use specification patterns seems to be particularly promising for the specifica-tion of business components: When analyzing e.g. the case study [2] one finds that 70% of all OCL constraints in this study can be backtracked to few frequently occur-ring specification patterns. Based on this observation we analyzed a number of com-ponent specifications and literature about component specification and identified nine specification patterns that often occur [4]. These specification patterns are listed in Table 1. Although the nine patterns occurred most often in the investigated material there will be other useful patterns as well and the list might be extended in future.Table 1. Behavioral specification patterns identified in [4]Constraint type Pattern nameInvariant Semantic Key AttributeInvariant Invariant for an Attribute Value of a ClassPrecondition Constraint for a Input Parameter ValuePrecondition Constraint for the Value of an Input Parameter FieldPrecondition Instance of a Class ExistsPrecondition Instance of a Class does not ExistPostcondition Instance of a Class CreatedDefinition Variable Definition for an Instance of a ClassPrecondition Constraint for an Instance Attribute for an Operation CallUnder (OCL) specification pattern we understand an abstraction of OCL constraints that are similar in intention and structure but differ in the UML model elements used. Each pattern has one or more pattern parameters(typed by elements of the UML metamodel) that act as placeholder for the actual model elements. With pattern instan-tiation we denote a specific OCL constraint that results from binding the pattern pa-rameters with actual UML model elements.As an example let us consider the pattern “Semantic Key Attribute”: It represents the situation that an attribute of a class (in the specification data model – cf. Fig. 2) plays the semantic role of a key – that is all instances of the class differ in their value of the key attribute. Pattern parameters are class and attribute and a pattern instantia-tion (for the class SalesOrder and attribute id) can be seen in the upper part of Fig. 3.Table 2. Description scheme for pattern Semantic Key Attribute [4] CharacteristicDescription Pattern nameSemantic Key Attribute Pattern parameterclass: Class; attribute: Property Restrictionsattribute is an attribute of class class Constraint typeInvariant Constraint context classConstraint body name(class).allInstances()->forAll(i1, i2 |i1 <> i2 implies (attribute) <>(attribute)) Based on the ideas of [11] we developed a description scheme that details the proper-ties of a specification pattern: pattern name, pattern parameters, restrictions for pattern use as well as type, context and body of the resulting constraint [4]. Note that the constraint body is a template showing text to be substituted in italic. The description scheme for the pattern Semantic Key Attribute is displayed in Table 2.Fig. 4. Selection screen for generating an OCL constraintThe following points connected with the exemplary pattern are worth mentioning: For sake of simplicity we presented the pattern with only one key attribute. In its regular version the pattern allows that the key is formed by one or more attributes of the class. (Note that this is the reason for not using the operator isUnique which would be rather constructed for more than one attribute.) One can also see that the patterns presented20 J. Ackermannhere are rather static – they allow for substituting UML model elements but do not allow for structural changes. For structural variations on the pattern (e.g.: the attribute id of class SalesOrderItem in Fig. 2 is only unique in the context of a specific instance of class SalesOrder ) one has to define additional patterns. We will now illustrate how such patterns can be exploited for specifications: Sup-pose the person who specifies our exemplary component is in the middle of the speci-fication process and wants to formulate the invariant from Fig. 3. He checks the li-brary of predefined specification patterns (which is part of his specification tool) and finds the pattern for a semantic key attribute (compare section 1 of Fig. 4). After se-lecting this pattern the tool will show him the pattern description and an associated template OCL constraint (showing the pattern parameters in italic). The user has to select model elements for the parameters (in section 3 of Fig. 4) – in our example the class SalesOrder and its attribute id are selected. Note that the tool can be built in such a way that it restricts the input to those model elements that are allowed for a pattern – in section 3 of Fig. 4 for instance you can see that the tool only offers the attributes of class SalesOrder for selection. After providing pattern and parameter values the user can start the generation. The tool checks the input for consistency and then generates the desired OCL constraint (compare section 4 of Fig. 5) which can beincluded into the component specification.Fig. 5. Display of the generated OCL constraintFollowing this approach has the following advantages: For the specification provider maintenance of specifications is simplified because it becomes faster, less error-prone and requires less expert OCL knowledge. For a specification user the understanding of Formal Description of OCL Specification Patterns 2122 J. Ackermannspecifications is simplified because generated constraints are uniform and are there-fore easier recognizable. Moreover, if the patterns were standardized, it would be enough to specify a pattern and the parameter values (without the generated OCL text) which would make recognition even easier.5 Technical Details of the SolutionTo realize the solution outlined in Sect. 4 we need a way to formally describe the specification patterns. Such a formal pattern description is on one hand prerequisite for a tool builder to implement corresponding constraint generators – on the other hand it might also be interesting for a user creating specifications to check if a pattern meets his expectations (although one would not generally expect that a user has the knowledge to understand the formal pattern specifications). In this section we discuss how the specification patterns can be formalized and be described such that their in-tention, structure and application become unambiguous.To do so we first show how such patterns can be formally described and applied (Sect. 5.1). After that we discuss the relationship of the solution to the UML meta-model (Sect. 5.2), argue why we have chosen it compared to other approaches (Sect.5.3) and cover some implementation aspects (Sect. 5.4).5.1 Defining OCL Pattern Functions for Specification PatternsThe basic idea how to formally describe the specification patterns is as follows: For each OCL specification pattern a specific function (called OCL pattern function) is defined. The pattern parameters are the input of the pattern function. Result of the pattern function is a generated OCL constraint which is returned and (if integrated with the specification tool) automatically added to the corresponding UML model element. The OCL pattern functions themselves are specified by OCL – from this specification one can determine the constraint properties (e.g. invariant) and its textual representation. All pattern functions are assigned as operations to a new class OclPat-tern which logically belongs to the layer of the UML metamodel (layer M2 in the four-layer metamodel hierarchy of UML [19] – compare also Sect. 5.2).This approach will now be discussed in detail for the specification pattern “Seman-tic Key Attribute” (see Sect. 4). For this pattern we define the OCL pattern function Create_Inv_SemanticKeyAttribute. Input of the function are a class cl and an attribute attr which is the key attribute of cl – both understood as UML model elements. (To avoid naming conflicts with UML metamodel elements we did not use the pattern parameter names as displayed in the tool in Fig. 4 (like class) but more technical ones (as cl) as input parameters of the pattern functions.) Result is an UML model element of type Constraint. The complete specification of this pattern function is shown in Fig. 6.Formal Description of OCL Specification Patterns 23 context OclPattern::Create_Inv_SemanticKeyAttribute(cl: Class,attr: Property): Constraint(1) pre: attr.class = cl(2) post: result.oclIsNew(3) post: space = result.context(4) post: result.specification.isKindOf(OpaqueExpression)(5) post: nguage = ‘OCL’(6) post: = ’invariant’(7) post: result.context = cl(8) post: = ‘Semantic Key Attribute’(9) post: result.specification.body = OclPattern.Multiconcat(, ‘.allInstances()->forAll( i1, i2 | i1 <> i2implies i1.’, , ‘ <> i2.’, , ‘)’) Fig. 6. Specification of pattern function OclPattern.Create_Inv_SemanticKeyAttributeThe specification of each OCL pattern function consists of three parts: •Preconditions specific for each pattern function (1)•General postconditions (2)-(5)•Postconditions specific for each pattern function (6)-(9).The function specific preconditions describe which restrictions must be fulfilled when calling the pattern function. These preconditions must assure that the actual parame-ters conform to the specification pattern. For instance defines the signature of the pattern function in Fig. 6 only, that cl is any class and attr is any property. The pre-condition (1) demands additionally that attr is an attribute that belongs to class cl.The general postconditions (2)-(5) are identical for all OCL pattern functions and represent in a way the main construction details. These postconditions (together with the functions signature) establish the following:•The return of each pattern function is a UML model element of type Constraint. •This constraint is added to the model (2) and is assigned to the model element which is the context of the constraint (3).•The attribute specification of the constraint is of type OpaqueExpression (4) and is edited in the language OCL (5). (This is in conjunction with the newest version of OCL [20] from June 2005 – earlier there was an inconsistency in the OCL 2.0 specification. Compare Fig. 29 of [20].)In difference to the general postconditions (2)-(5) the postconditions (6)-(9) vary between different pattern functions. The function specific postconditions establish the following:•(6) describes of which constraint type (e.g. invariant, pre- or postcondition) the returned constraint is. The constraint of our example is an invariant.•(7) defines the context of the constraint to be the class cl. The context of an in-variant is always some class and the context of a pre- or postcondition is the clas-sifier to which the operation belongs. Note that OCL imposes additional condi-tions depending on the constraint type. (An invariant, for instance, can only con-strain one model element.) These additional constraints are part of the OCL speci-fication [20, p. 176ff.] and will therefore not be repeated here.24 J. Ackermann•Constraint is a subtype of NamedElement and therefore has an attribute called name [21, p. 94]. This attribute is used in (8) where the constraint is assigned a name which is derived from the specification pattern (in our example the name SemanticKeyAttribute).•The textual OCL representation of a constraint can be found in the attribute body of the property specification(which is of type OpaqueExpression) of the con-straint. Postcondition (9) specifies this textual representation by combining fixed substrings (as ‘ <> i2.’) with the name of model elements which were supplied as pattern parameter values (e.g. ).Note that standard OCL contains the function concatenate which allows concatenating two substrings. In postconditions like (9) of Fig. 6 it is necessary to concatenate many substrings. Technically one could do so by repeated application of OCL concatenate but the resulting expressions were hard to read. Instead we define a help function OclPattern.Multiconcat. Input of this function is a sequence of string arguments and its result is a string which is formed by repeated concatenation of the arguments (in the order given by the sequence).constr := OclPattern.Create_Inv_SemanticKey Attribute(SalesOr-der, id)Fig. 7. Call of pattern function OclPattern.Create_Inv_SemanticKeyAttributeFig. 7 shows how the pattern function Create_Inv_SemanticKeyAttribute is called in our example from Fig. 3: As values for the pattern parameters the class SalesOrder and the property id are used. The precondition is fulfilled because id is indeed an attribute of SalesOrder. The generated constraint constr is an invariant and its textual OCL representation is (as expected) the one shown as result in Fig. 5. (Due to missing UML syntax for operation calls we use in Fig. 7 a syntax that resembles the OCL syntax for operation calls.)Other specification patterns can be described analogously. When defining OCL pat-tern functions one must be careful to select the correct UML metamodel elements for the pattern parameters (classes, properties (of classes), parameters, properties (of parameters) etc.) and to denote all relevant preconditions.One aspect to be mentioned is that some specification patterns require pattern pa-rameters with multiplicity higher than one. (In the regular version of the semantic key pattern there can be one or more attributes that form together the key of the class.) This can be solved by allowing input parameters of a pattern function to have multi-plicity greater than one ([1..*]) and by employing the OCL operator iterate to con-struct the textual OCL specification in something like a loop.5.2 Relationship with the UML MetamodelThe aim of this section is to discuss the relationship of the new class OclPattern with the UML language definition.The UML metamodel is based on a four-layer metamodel hierarchy [19, p. 17ff.]: Layer M0 consist of the run time instances of model elements as e.g. the sales orderwith id ‘1234’. Layer M1 contains the actual user model in which e.g. the class Sale-sOrder is defined. Layer M2 defines the language UML itself and contains e.g. the model element Class. Note that layers M2 and M1 are the meta-layers for layers M1 and M0, respectively. Additionally there exists the layer M3 for the Meta Object Fa-cility (MOF) which is an additional abstraction to define metamodels like UML.For the constraint patterns we defined in Sect. 5.1 a new class OclPattern. To de-cide to which layer this class logically belongs we can analyze input and output of the pattern functions: Input of an OCL pattern function are elements of a UML model (like class SalesOrder or attribute id – on layer M1) that are typed by elements of the UML metamodel (like Class or Property – on layer M2). Analogously the output is always a constraint for a UML model element and is typed by the metamodel element Constraint (on layer M2). Consequently the pattern functions operate on layer M2 and therefore the new class OclPattern logically also belongs to layer M2.On first glance it might seem desirable to integrate the class OclPattern into the UML metamodel (layer M2). The definition of UML, however, does not allow defin-ing new elements in its metamodel. Adding the class OclPattern to layer M2 would effectively mean to define a new modeling language UML’ which consists of UML and one extra class – leaving standard UML yields to many disadvantages (potential compatibility and tool problems) and is not an adequate solution.When looking more closely one finds that it is not necessary to integrate the class OclPattern that tightly into the UML metamodel because it does not change the lan-guage in the sense of introducing new model elements or changing dependencies.As a conclusion it was decided: the class OclPattern will be denoted with the stereotype «oclHelper», operates on layer M2 but stands in parallel to the UML meta-model. The class needs only to be known to the specification tool implementing the constraint generators and is of no direct relevance for model users. The class might be integrated into the UML metamodel at a later time if the UML definition allows it. Note that on a related question OCL users asked to allow user defined OCL functions (Issue 6891 of OCL FTF) which was not realized in OCL 2.0.5.3 Discussion of the SolutionIn this section we will discuss the reasons why the approach presented in Sect. 5.1 was chosen and compare it with other solution approaches that seem (at least at first glance) possible.By defining OCL pattern functions for the specification patterns it became possible to formally describe the patterns completely and quite elegantly: the pattern parame-ters can be found as function parameters and the function specification (which uses again OCL) describes the prerequisites to apply the pattern and the properties of the constraint to be generated. Moreover it is possible to actually specify that the con-straint is added to the UML model element in consideration (assuming the pattern generator is integrated with the specification tool). One big advantage is that this ap-proach only uses known specification techniques and does not require the invention of new ones. There is only one new class OclPattern that encapsulates the definition of all patterns.An alternative approach would be to use a first-hand representation for the abstract constraints before parameter binding – [5] uses this approach and calls this representa-tion constraint schema. The advantage is its explicit representation of the constraint schema. The disadvantage, however, is that constraint schemata are not defined in the UML metamodel – specifying them requires the invention of a special description technique (either outside UML or by introducing a new UML metamodel element). Therefore we decided against using this approach.UML itself offers a mechanism called Templates that allows parameterizing model elements. The following approach seems to be promising and elegant: For each pat-tern one defines a template constraint which is parameterized by the pattern parame-ters – when applying the pattern these parameters are bound to the actual model ele-ments. Unfortunately this solution is technically not possible because UML does not allow parameterizing Constraints (only Classifiers, Packages and Operations) [21, p. 600].To use UML templates nevertheless one might think about parameterizing the con-text of a constraint (which is a classifier or an operation). But this approach is rather constructed and results in many disadvantages: For each invariant pattern used there needs to be a type in the specification data model and all business types using the pattern need to be bound to it. As a result the model would become overcrowded con-tradicting the clarity guideline from the guidelines of modeling [6]. (Similar problems occur with patterns of type pre- or postcondition where template operations need to be added to the interface model.)5.4 Prototype ImplementationConstraint generators for specification patterns were implemented as a prototype (compare Fig. 4 and 5 in Sect. 4). The prototype enables to select a specification pat-tern and values for the pattern specific parameters. As far as possible pattern precondi-tions were considered when providing input for pattern parameters. All other precon-ditions must be checked after value selection. As a result the prototype generates the desired OCL constraint and displays it for the user. Planned for the future is an inte-gration of constraint generators into a component specification tool – that would per-mit to automatically add the generated constraint to the correct model element of the UML model in work.It shall be noted that the pattern parameters to be filled and the preconditions to be checked depend on the specification pattern – in the prototype these were hard coded. One could imagine something like a meta description that enables to (semi)automatically generate the constraint generator. The associated effort, however, seemed not appropriate for only nine specification patterns.6 Related WorkDue to its importance component specifications are discussed by many authors (e.g. [9,10,23,27] – for an overview compare e.g. [23]). Most current specification ap-。

Short description of HAWC2HAWC2简要说明Torben Juul LarsenRisø National LaboratorySeptember 24, 2009Ri s￠国家实验室2009.9.24The HAWC2 code is a code intended for calculating wind turbine response in time domain. The core of the code was mainly developed within the years 2003-2007 at the Aeroelastic Design research program at Risø, National Laboratory Denmark.HAWC2编码是用来计算风力机在时域内响应的编码。

编码的核心主要发展是从2003到2007年间在丹麦国家实验室气动力弹性研究项目发展来的。

The structural part of the code is based on a multibody formulation. In this formulation the wind turbine main structures is subdivided into a number of bodies where each body is an assembly of Timoshenko beam elements. Each body includes its own coordinate system with calculation of internal inertia loads when this coordinate system is moved in space, hence large rotation and translation of the body motion is accounted for. Inside a body the formulation is linear, assuming small deflections and rotations. This means that a blade modeled as a single body will not include the same nonlinear geometric effects related to large deflections as a blade divided into several bodies. The bodies representing the mechanical parts of the turbine are connected by joints also referred to as constraints. The constraints are formulated as algebraic equations that impose limitations of the bodies’ motion. This could in principal be a trajectory the body needs to follow, but related to the wind turbine implementation there are so far the possibility of a fixed connection to a global point (e.g. tower bottom clamping), a fixed coupling of the relative motion (e.g. fixed pitch, yaw), a frictionless bearing and a bearing where the rotation angle is controlled by the user. It may be worth to notice, that also for the last constraint where the rotation is specified, inertial forces related to this movement is accounted for in the response.编码的结构部分是基于多体构想。

Journal of Hazardous Materials 162(2009)860–865Contents lists available at ScienceDirectJournal of HazardousMaterialsj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /j h a z m atHeterogeneous photo-Fenton degradation of polyacrylamide in aqueous solution over Fe(III)–SiO 2catalystTing Liu,Hong You ∗,Qiwei ChenDepartment of Environmental Science and Engineering,Harbin Institute of Technology,P.O.Box 2606,202Haihe Road,Harbin 150090,PR Chinaa r t i c l e i n f o Article history:Received 29December 2007Received in revised form 8April 2008Accepted 22May 2008Available online 28May 2008Keywords:Photo-Fenton PolyacrylamideFe(III)–SiO 2catalystHeterogeneous catalysisa b s t r a c tThis article presents preparation,characterization and evaluation of heterogeneous Fe(III)–SiO 2catalysts for the photo-Fenton degradation of polyacrylamide (PAM)in aqueous solution.Fe(III)–SiO 2catalysts are prepared by impregnation method with two iron salts as precursors,namely Fe(NO 3)3and FeSO 4,and are characterized by Brunauer–Emmett–Teller (BET),X-ray diffraction (XRD)and X-ray photoelectron spectroscopy (XPS)methods.The irradiated Fe(III)–SiO 2is complexed with 1,10-phenanthroline,then is measured by UV–vis-diffuse reflectance spectroscopy (UV–vis-DRS)and XPS to confirm the oxidation state of Fe in solid state.By investigating the photo-Fenton degradation of PAM in aqueous solution,the results indicate that Fe(III)–SiO 2catalysts exhibit an excellent photocatalytic activity in the degradation of PAM.Moreover,the precursor species and the OH −/Fe mole ratio affect the photocatalytic activity of Fe(III)–SiO 2catalysts to a certain extent.Finally,the amount of Fe ions leaching from the Fe(III)–SiO 2catalysts is much low.©2008Elsevier B.V.All rights reserved.1.Introduction“Produced water”is the largest volume of waste generated by the oil industry.In particular,with the application of polymer flood-ing technology in tertiary oil recovery processes in China,a kind of new produced water containing polyacrylamide (PAM),a high molecular weight polymer,have been produced.The conventional method to dispose of such produced water is either re-injected into the subsurface for permanent disposal or discharged directly to the marine environment.However,both methods have caused serious contamination to the ground water and surface water.On the other hand,the current physical treatment processes (settling separation and filtration)can not satisfy the treatment requirement.Hence,the treatment technologies of produced water containing PAM have become a key problem in oil industry in China.Physical [1,2],biological [3,4]and chemical methods [5]are presently used for treatment of PAM.It was found that the degrada-tion ratio of PAM in aqueous solution was slow by using biological methods.Recently,some investigators have reported the successful application of advanced oxidation processes for PAM degradation [6,7].One of advanced oxidation processes,Fenton (a powerful source of oxidative HO •generated from H 2O 2in the presence of Fe 2+ions)or photo-Fenton reaction has been used in the degradation of∗Corresponding author.Tel.:+8645186283118;fax:+8645186283118.E-mail address:youhong@ (H.You).many organic compounds [8,9].Even though these systems are con-sidered as a very effective approach to remove organic compounds,it should be pointed out that there is a major drawback because the post-treatment of Fe sludge is an expensive process.This short-coming can be overcome by using heterogeneous photo-Fenton reaction.Therefore,a lot of effort has been made in developing heterogeneous photo-Fenton catalysts.For example,Parra et al.pre-pared Nafion/Fe structured membrane catalyst and used it in the photo-assisted immobilized Fenton degradation of 4-chorophenol [10].However,Nafion/Fe structured membrane catalyst is much expensive for practical use.Thus,the low cost supports such as the C structured fabric [11,12],activated carbon [13],mesoporous silica SBA-15[14–16],zeolite [17,18]and clay [19–21],have been used for the immobilization of active iron species.Remirez et al.prepared the catalysts using four iron salts as precursors for the heteroge-neous Fenton-like oxidation of Orange II solutions [22].The results showed that the nature of the iron salt had a significant effect on the process performance.So,it is necessary to discuss the photo-catalytic activities of the catalysts by using different iron salts as precursors.In this paper,a series of Fe(III)–SiO 2catalysts were prepared at different OH −/Fe mole ratio and by using two iron salts as precur-sors,namely Fe(NO 3)3and FeSO 4.All catalysts were characterized by BET,XRD and XPS.The oxidation state of Fe in the solid state was detected by the UV–vis-DRS and XPS measurement.The pho-tocatalytic activity of Fe(III)–SiO 2catalyst was evaluated in the photo-assisted Fenton degradation of PAM in aqueous solution in0304-3894/$–see front matter ©2008Elsevier B.V.All rights reserved.doi:10.1016/j.jhazmat.2008.05.110T.Liu et al./Journal of Hazardous Materials 162(2009)860–865861the presence of H 2O 2and UV light at an initial solution pH of 6.8.The effects of the precursor species and the OH −/Fe mole ratio on the photocatalytic activities of Fe(III)–SiO 2catalysts were also stud-ied.In addition,the leaching behavior of Fe from the catalyst surface was discussed.2.Experimental 2.1.MaterialsThe analytical grade PAM,H 2O 2(30%,w/w),Fe(NO 3)3·9H 2O,FeSO 4·7H 2O,NaOH and 1,10-phenanthroline were used for this experiment without further purification.The average molecular weight of PAM was 5000000Da and degree of hydrolysis of PAM was about 30%.Silica gel (40–60mesh)as a support was purchased from Qingdao Ocean Chemical Company,China.The aqueous solu-tion of PAM was prepared by dissolving a weighed quantity of PAM in distilled water.2.2.Preparation of the catalystsA series of catalysts were prepared by two methods as follows:(1)Two catalysts were prepared by impregnation of 20g silicagel in aqueous solution containing 0.05mol/L Fe(NO 3)3and 0.05mol/L FeSO 4,respectively and kept stirring for 6h.After aging for 40h at 105◦C,the samples were separated and washed several times with deionized water,then dried overnight at 80◦C.The dried samples were calcined at 500◦C for 5h in an oven.Finally,two Fe(III)–SiO 2catalysts were obtained,namely S-Fe 3+and S-Fe 2+.(2)Twenty grams of silica gel carrier were first added into the aque-ous solution containing 0.05mol/L Fe(NO 3)3and 0.05mol/L FeSO 4,respectively and kept under vigorous stirring for 2h.Then,NaOH aqueous solution with different concentration was added drop by drop under stirring until the OH −/Fe 3+or OH −/Fe 2+mole ratio was equal to 1and 2.After aging for 40h at 105◦C,the solid product were separated and washed several times with deionized water and dried overnight at 80◦C.The dried samples were calcined at 500◦C for 5h in an oven and the catalysts were named as S-Fe 3+/1,S-Fe 3+/2,S-Fe 2+/1and S-Fe 2+/2,respectively.2.3.Characterization of the catalystsThe iron content of Fe(III)–SiO 2catalysts were verified by an inductively coupled plasma (ICP)(Model:Perkin-Elmer 5300DV)after acidic digestion of the catalysts.Brunauer–Emmett–Teller (BET)specific surface area,total pore volume and average pore size of synthesized Fe(III)–SiO 2catalysts were measured by adsorption of nitrogen at 77K,by using auto-mated volumetric adsorption instrument (model Quantachrome Autosorb-1).X-ray diffraction (XRD)measurement was employed using a Rigaku D/max-rB system with Cu K ␣radiation operating at 45kV and 40mA.The 2Âranged from 10to 90◦.X-ray photo-electron spectroscopy (XPS)measurements were performed using a PHI 5700spectrometer.The X-ray source was operated at 250W and 12.5kV and the C 1s signal was adjusted to 284.62eV as the reference.The curve fitting was achieved by using a Physical Electronics PC-ACCESSESCA-V6.0E program with a Gaussian–Lorentzian sum function.Finally,UV–vis-diffuse reflectance spectroscopy (UV–vis-DRS)measurements were recorded on TU1901with a sphere reflectanceaccessory.Fig.1.Schematic diagram of three-phase fluidized bed photoreactor.2.4.Catalytic activityThe photocatalytic activities of Fe(III)–SiO 2catalysts were evaluated by degradation of PAM from aqueous solutions in a three-phase fluidized bed photoreactor (Fig.1).The light source was UV lamp (Philips,8W,254nm)fixed inside of a cylindrical quartz tube.The total volume of PAM aqueous solution was 1500mL.In order to ensure a good dispersion of Fe(III)–SiO 2catalysts and good mix-ture in solution,compressed air was bubbled from the bottom at a flow rate of 3.3L/min.For each experiment,the concentration of PAM and H 2O 2were 100and 200mg/L,respectively.The cata-lyst loading was fixed at 1.0g/L.The Fe(III)–SiO 2catalyst and H 2O 2were added into the photoreactor,at the same time,UV light was turned on and this was considered as the initial time for reaction.Then,samples were withdrawn at time intervals.The concentration of PAM in solution was measured by starch-CdI 2spectrophotom-etry [23].To determine mineralization of PAM solution,the total organic carbon (TOC)of the reaction solution was measured by a TOC-V CPN Shimadzu TOC analyzer.In addition,the concentration of Fe in reaction solution was monitored by ICP.2.5.Characterization of Fe(III)–SiO 2after the reactionIn order to know the oxidation state of Fe on Fe(III)–SiO 2catalyst surface under irradiation,the 1,10-phenanthroline was used which would be complexed with Fe(II)–SiO 2in solid state [17].0.03%1,10-phenanthroline and 0.6mol/L acetate buffer (pH 8.60)was added into the photoreactor and the S-Fe 2+/1catalyst was irradiated for 2h.After reaction,the sample was filtered,washed several times and dried,then characterized by UV–vis-DRS and XPS measure-ment.3.Results and discussion3.1.Characterization of the catalysts before reactionThe content of Fe in Fe(III)–SiO 2catalysts is shown in Table 1.It is observed that the Fe content in catalysts increases with the increase of OH −/Fe mole ratio in both Fe(NO 3)3and FeSO 4used as precursor.It should be mentioned that,with the increase of OH −/Fe mole ratio,the structure of iron species in the solution develops from the low-molecular-weight species into high polymerization degree cationic polymer [24].Therefore,with the increase of OH −/Fe mole ratio,862T.Liu et al./Journal of Hazardous Materials 162(2009)860–865Table 1The content of Fe in catalysts and the results of BET tests Samples The content of Fe (wt.%)BET surface area (m 2/g)Total porevolume (cm 3/g)Average porewidth (˚A)SiO 20419.00.9388.9969S-Fe 3+0.404446.00.9988.7406S-Fe 3+/10.496462.0 1.0288.4524S-Fe 3+/20.684470.0 1.0488.2624S-Fe 2+0.184442.60.9888.6380S-Fe 2+/10.534411.80.9996.5204S-Fe 2+/20.976314.91.07135.8424the polymerization degree of iron which was absorbed on SiO 2car-rier increased.It indicates that increasing OH −concentration can improve the loading of Fe in Fe(III)–SiO 2catalyst.The BET surface area,total pore volume and average pore width of the investigated Fe(III)–SiO 2catalysts are also listed in Table 1.The surface area of S-Fe 3+and S-Fe 2+catalysts were 446.0and 442.6m 2/g,respectively,higher than the SiO 2carrier.When using Fe(NO 3)3as precursor,with the increase of OH −/Fe mole ratio,the surface area and total pore volume of catalysts increased and the average pore width of catalysts changed a little.On the contrary,when using FeSO 4as precursor,with the increase of OH −/Fe mole ratio,the surface area of catalysts reduced,while the total pore vol-ume of catalysts and the average pore width of catalysts increased.The results show that the pore structure of Fe(III)–SiO 2catalysts prepared by the second method are affected remarkably by the precursor species and the OH −/Fe mole ratio.The XRD patterns of S-Fe 3+,S-Fe 3+/2,S-Fe 2+and S-Fe 2+/2cata-lysts are illustrated in Fig.2.The pattern showed a typical broad peak,which indicated that silica gel used as a support was a pure amorphous structure.On the other hand,the XRD patterns did not show iron oxides peaks,even for catalyst with 6.2wt.%of iron (not shown in the figure).It may be proposed that the XRD techniques are not sensitive enough to detect little iron oxides because the higher background of XRD measurement caused by amorphous SiO 2.The oxidation state of Fe on the surface of catalysts was charac-terized by XPS and the results are presented in Fig.3.The binding energy of Fe 2p 3/2was determined to be 710.945eV,710.595eV and 710.975eV for S-Fe 3+,S-Fe 3+/1and S-Fe 3+/2catalyst,respectively,which was ascribable to Fe 2O 3[25].When FeSO 4was used as the precursor,the Fe 2p 3/2peak was found at 711.195eV,711.345eV and 711.850eV for S-Fe 2+,S-Fe 2+/1and S-Fe 2+/2catalyst,respectively,strongly suggesting that the iron on the catalysts was Fe(III)[21].When FeSO 4was used as precursor,the binding energy of Fe 2p 3/2Fig.2.XRD patterns of the Fe(III)–SiO 2catalysts.Fig.3.XPS spectra of the Fe 2p region for the Fe(III)–SiO 2catalysts.in catalysts were higher than that of catalysts prepared by Fe(NO 3)3.It was difficult to give an adequate explanation of increasing in the binding energy of Fe 2p 3/2yet.O 1s survey scan further indicated the oxygen status on the catalyst surface.As shown in Fig.4,the O 1s region can be fitted into four peaks,which are attributed to the chemisorbed oxygen,the lattice oxygen of SiO 2,the lattice oxygen of Fe 2O 3and the chemically or physically adsorbed water.Accord-ing to the reports [26,27],the chemisorbed oxygen can take an active part in the oxidation process and greatly improve the cat-alyst activity.It can be seen from Table 2that the percentage of chemisorbed oxygen of catalyst was improved when FeSO 4was used as precursor and the S-Fe 2+/1catalyst had the highest per-centage of chemisorbed oxygen.3.2.Characterization of the catalysts after the reactionThe catalyst was characterized by UV–vis-DRS and XPS to confirm the formation of Fe(II)–SiO 2when Fe(III)–SiO 2was irradi-ated by photon.The UV–vis diffuse reflectance absorption spectra of Fe(III)–SiO 2catalyst before and after reaction are shown in Fig.5.The results clearly shows a new broad absorption band at 505–525nm after irradiation which is characteristic band of [Fe(1,10-phenanthroline)]2+complex [17].It is accounted for that the Fe(III)–SiO 2on irradiation with photon isconverted into Fe(II)–SiO 2that would be complexed with 1,10-phenanthroline in solid state.The binding energy of Fe 2p for the catalyst before and after reaction is shown in Fig.6.It is observed that the binding energy of Fe 2p 3/2is slightly shifted to lower BE value from 711.345to 710.600eV after irradiation,which is due to the reduction of Fe(III)–SiO 2to Fe(II)–SiO 2during the irradiation.Fig.4.O 1s curve fitting of S-Fe 2+/1catalyst.T.Liu et al./Journal of Hazardous Materials 162(2009)860–865863Table 2XPS data of O element on the surface of the catalysts CatalystsBinding energy (eV)Percentage of O ad or O L (%)O ad aO L b (SiO 2)O L b (Fe 2O 3)O L c (H 2O)O ad O L (SiO 2)O L (Fe 2O 3)O L (H 2O)S-Fe 3+531.80532.80529.79533.8926.9448.52 2.9521.59S-Fe 3+/1531.80532.80529.99533.8924.1644.39 3.5727.87S-Fe 3+/2531.80532.84529.79533.8927.7344.597.4820.2S-Fe 2+531.80532.80529.79533.8931.3942.28 4.3621.98S-Fe 2+/1531.81532.87529.79533.7938.4634.537.6019.41S-Fe 2+/2531.89532.80529.99533.7032.2230.0711.9325.79a The chemisorbed oxygen.b The latter oxygen.cThe chemically or physically adsorbed water.Fig.5.UV–vis diffuse reflectance spectra of S-Fe 2+/1catalyst:(a)Fe(III)–SiO 2and (b)Fe(II)–(1,10-phenanthroline)–SiO 2sample.3.3.Degradation and mineralization of PAM by heterogeneous photo-Fenton processesThe degradation of 100mg/L PAM in aqueous solutions under different conditions was preformed by using S-Fe 2+/1as a photo-Fenton catalyst at an initial solution pH of 6.8,and theresults are shown in Fig.7.In the presence of UV lamp,about 5%degradation of PAM in aqueous solution was observed,indicating that the degra-dation of PAM caused by direct photolysis is very limited.In the presence of 1.0g/L S-Fe 2+/1catalyst,the removal of PAM was less than 5%,which was caused by the adsorption of PAM on the catalyst.With 1.0g/L S-Fe 2+/1catalyst and 200mg/L H 2O 2in dark,the degra-dation of PAM was low,implying that the PAM degradation in the course of heterogeneous Fenton reaction is limited in neutral cir-cumstance.In the presence of UV and 200mg/L H 2O 2without anyFig.6.XPS spectra of the Fe 2p region for the S-Fe 2+/1catalyst:(a)Fe(III)–SiO 2and (b)Fe(II)–(1,10-phenanthroline)–SiO 2sample.catalyst,the concentration of PAM decreased significantly.It is due to the oxidation of PAM by •OH radicals formed direct photolysis of H 2O 2.In the presence of 1.0g/L S-Fe 2+/1catalyst,UV and 200mg/L H 2O 2,the concentration of PAM decreased rapidly and about 94%PAM degradation in 90min.As the leaching of Fe from Fe(III)–SiO 2catalyst was negligible (described as follows),the degradation of PAM in aqueous solutions was almost caused by the heterogeneous photo-Fenton reaction,indicating that S-Fe 2+/1catalyst exhibits a good photocatalytic activity in PAM degradation.It is assumed that Fe(III)species on the surface of catalysts transform to Fe(II)species under irradiation of UV light,then,the Fe(II)species generate •OH radicals by the decomposition of H 2O 2[28,29].At the same time,the UV light irradiates hydrogen peroxide to produce the •OH radicals.Finally,PAM is oxidized by •OH radicals.Therefore,the mechanism for the photo-Fenton degradation of PAM using Fe(III)–SiO 2catalyst as a heterogeneous catalyst is proposed below:H 2O 2+h →2•OH(1)Fe(III)−X +H 2O +h →Fe(II)−X +•OH +H +(2)Fe(II)−X +H 2O 2→Fe(III)−X +OH −+•OH (3)PAM +•OH →Intermediates →CO 2+H 2O(4)where X represents the surface of Fe(III)–SiO 2catalyst.The mineralization process of PAM aqueous solutions under dif-ferent conditions was measured and the results are shown in Fig.8.Only with 8W UV,there was almost no mineralization of PAM.In the present of 1.0g/L S-Fe 2+/1catalyst and 200mg/L H 2O 2in dark,about 20%TOC of PAM was removed after 180min,indicating that the mineralization of PAM by heterogeneous Fenton reaction is limited in neutral circumstance.In the presence of 8W UV and 200mg/L H 2O 2,the mineralization of PAM is significant,about 40%Fig.7.Degradation of PAM under different conditions:(a)1g/L S-Fe 2+/1catalyst,(b)8W UV,(c)1g/L S-Fe 2+/1catalyst +200mg/L H 2O 2,(d)8W UV +200mg/L H 2O 2and (e)8W UV +200mg/L H 2O 2+1g/L S-Fe 2+/1catalyst.864T.Liu et al./Journal of Hazardous Materials 162(2009)860–865Fig.8.Mineralization of PAM under different conditions:(a)8W UV,(b)1g/L S-Fe 2+/1catalyst +200mg/L H 2O 2,(c)8W UV +200mg/L H 2O 2and (d)8W UV +200mg/L H 2O 2+1g/L S-Fe 2+/1catalyst.TOC of PAM was removed after 180min.With the present 1.0g/L S-Fe 2+/1catalyst,8W UV and 200mg/L H 2O 2,the mineralization of PAM was significantly accelerated.After 180min,about 70%TOC of PAM was removed,suggesting that the S-Fe 2+/1catalyst show a significant photocatalytic activity for the mineralization of PAM.3.4.Effects of the precursor species and the OH −/Fe mole ratio on the PAM degradationTo check the photocatalytic activity of catalysts prepared by different methods,degradation of PAM in aqueous solutions by Fe(III)–SiO 2catalysts was evaluated and the results are presented in Fig.9.It was observed that the catalysts prepared with two pre-cursor species and different OH −/Fe mole ratio showed different photocatalytic activity.At the same OH −/Fe mole ratio,catalysts prepared with FeSO 4shown a higher photocatalytic activity than Fe(NO 3)3.The most effective catalyst seems to be that prepared with FeSO 4and the OH −/Fe mole ratio at 1.By using S-Fe 2+/1cata-lyst,98.6%of degradation was obtained after 120min.In contrast,S-Fe 3+/1catalyst gave rise to the less photocatalytic activity,which produced an efficiency degradation of 89.7%.The different photo-catalytic activity was observed when two precursors are used.The results are not clear,and it will be the aim of further work (iron oxidation state effect).3.5.Fe leaching from Fe(III)–SiO 2catalystsIn addition to having a high photocatalytic activity,stability is another important factor for a catalyst prepared.Theconcentra-Fig.9.Degradation of PAM by using different Fe(III)–SiO 2catalysts.Fig.10.Fe concentration in solution by using different Fe(III)–SiO 2catalysts.tion of Fe ions in solution with different catalysts was examined by ICP and the results are shown in Fig.10.It can be seen that there is no significant difference in the patterns of the curves.The concentration of Fe ions increased as reaction time increased,and reached a peak value,then followed by a decrease.The same phe-nomenon has been reported by Feng et al.[30],but the reason is still not clear.At the same OH −/Fe mole ratio,the Fe leaching from the catalysts prepared by Fe(NO 3)3was usually lower than the catalysts prepared by FeSO 4.The maximum concentration of Fe among all the catalysts was 0.17mg/L,suggesting that the Fe leach-ing from the Fe(III)–SiO 2catalysts is negligible,and the degradation of PAM aqueous solutions are almost caused by the heterogeneous photo-Fenton reaction in neutral circumstance.After 120min of the reaction,the percentage of Fe leached from the S-Fe 2+/1catalyst is only about 0.62%,the results also suggest that the catalysts have a long-term stability.4.ConclusionsFe(III)–SiO 2catalysts have been synthesized by two methods with Fe(NO 3)3and FeSO 4as precursors,and were characterized by the BET,XRD and XPS method.The percentage of chemisorbed oxygen on the surface of catalysts prepared by FeSO 4is higher than that prepared by Fe(NO 3)3.The results confirm the formation of Fe(II)–SiO 2when Fe(III)–SiO 2was irradiated by photon.The photocatalytic activities of Fe(III)–SiO 2catalysts were eval-uated by the degradation of PAM from aqueous solution in the photo-Fenton reaction and all the catalysts exhibited a better photocatalytic activities.However,the precursor species and the OH −/Fe mole ratio have influence on the photocatalytic activi-ties of the catalysts.At the same OH −/Fe mole ratio,the catalysts could present the better photocatalytic activities when using FeSO 4as precursor.The best efficiency for the degradation of PAM in heterogeneous photo-Fenton reaction was 94%degrada-tion in 90min and 70%TOC removal in 180min at an initial pH of 6.8.Finally,it was observed that Fe leaching from Fe(III)–SiO 2cata-lysts was negligible,indicating that the catalysts have a long-term stability and the degradation of PAM from aqueous solution are almost caused by the heterogeneous photo-Fenton reaction.AcknowledgmentsThe authors gratefully acknowledge the financial supports from the National Basis Research Foundation of China (973Program,No.2004CB418505)and the Research 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四阶切比雪夫二型带通滤波器是一种常见的数字信号处理工具，它在信号处理领域具有重要的应用。

本文将介绍如何使用Python实现四阶切比雪夫二型带通滤波器，并对其原理和应用进行深入探讨。

1. 切比雪夫滤波器切比雪夫滤波器是数字信号处理中常用的一种滤波器类型，它具有高通、低通、带通和带阻等多种形式。

在这些形式中，带通滤波器可以选择信号中的特定频率范围进行增强或抑制，因此在语音处理、图像处理等领域有着广泛的应用。

2. 四阶切比雪夫二型带通滤波器的设计四阶切比雪夫二型带通滤波器的设计可以分为两个步骤：首先是在模拟域中设计一个带通滤波器，然后将其转换为数字域。

需要注意的是，切比雪夫滤波器的设计需要满足一定的通带波纹和阻带衰减要求，这在实际应用中需要仔细权衡。

3. Python实现在Python中，可以使用scipy库中的signal模块来实现数字滤波器的设计和应用。

可以使用signal.iirfilter函数设计滤波器的系数，然后利用signal.lfilter函数对信号进行滤波处理。

通过这种方式，可以方便地实现四阶切比雪夫二型带通滤波器。

4. 应用实例接下来，我们将介绍一个音频信号处理的应用实例，通过Python实现四阶切比雪夫二型带通滤波器对音频信号进行处理。

通过对比处理前后的音频信号，可以直观地感受到滤波器对信号的影响，并了解滤波器在语音处理中的实际效果。

5. 个人观点和总结从实际开发应用来看，Python作为一种简洁、灵活和强大的编程语言，非常适合于数字信号处理领域。

通过对四阶切比雪夫二型带通滤波器的Python实现，我们不仅可以深入了解滤波器的原理和设计方法，还可以在实际项目中应用这些知识，从而更好地处理数字信号。

通过本文的介绍和实例分析，相信读者对四阶切比雪夫二型带通滤波器的原理和Python实现有了更深入的理解。

在实际应用中，可以根据具体需求选择合适的滤波器类型，并结合Python的强大功能进行开发和实现。

2010;2:a000562 originally published online July 14, 2010Cold Spring Harb Perspect Biol Susan R. Wente and Michael P. Rout The Nuclear Pore Complex and Nuclear Transport References/content/2/10/a000562.full.html#ref-list-1 This article cites 179 articles, 82 of which can be accessed free service Email alerting click here box at the top right corner of the article or Receive free email alerts when new articles cite this article - sign up in the Subject collections (29 articles)The Nucleus Articles on similar topics can be found in the following collections/site/misc/subscribe.xhtml go to: Cold Spring Harbor Perspectives in Biology To subscribe to Copyright © 2010 Cold Spring Harbor Laboratory Press; all rights reservedThe Nuclear Pore Complex and Nuclear TransportSusan R.Wente1and Michael P.Rout21Department of Cell and Developmental Biology,Vanderbilt University Medical Center,Nashville,T ennessee372322Laboratory of Cellular and Structural Biology,The Rockefeller University,New Y ork,New Y ork10065 Correspondence:susan.wente@ and rout@Internal membrane bound structures sequester all genetic material in eukaryotic cells.The most prominent of these structures is the nucleus,which is bounded by a double membrane termed the nuclear envelope(NE).Though this NE separates the nucleoplasm and genetic material within the nucleus from the surrounding cytoplasm,it is studded throughout with portals called nuclear pore complexes(NPCs).The NPC is a highly selective,bidirectional transporter for a tremendous range of protein and ribonucleoprotein cargoes.All the whilethe NPC must prevent the passage of nonspecific macromolecules,yet allow the free diffu-sion of water,sugars,and ions.These many types of nuclear transport are regulated at mul-tiple stages,and the NPC carries binding sites for many of the proteins that modulate and modify the cargoes as they pass across the NE.Assembly,maintenance,and repair of the NPC must somehow occur while maintaining the integrity of the NE.Finally,the NPC appears to be an anchor for localization of many nuclear processes,including gene acti-vation and cell cycle regulation.All these requirements demonstrate the complex designof the NPC and the integral role it plays in key cellular processes.T axonomically speaking,all life on earth falls into one of two fundamental groups,the prokaryotes and the eukaryotes.The prokar-yotes,thefirst group to evolve,are single cell organisms bounded by a single membrane. About1.5billion years later,a series of evo-lutionary innovations led to the emergence of eukaryotes.Eukaryotes have multiple inner membrane structures that allow for compart-mentalization within the cell,and therefore dif-ferentiation of the cell and regulation within it. Ultimately,the greater cellular complexity of eukaryotes allowed them to adopt a multicel-lular lifestyle,as seen in the plants,fungi and animals of today(reviewed in Field and Dacks 2009).Internal membrane bound structures se-quester all genetic material in eukaryotic cells. The most prominent of these structures,which gives the eukaryotes their Greek-rooted name,is the nucleus—the central“kernel”(gr.“karyo-”) of the cell.The nucleus is bounded by a double membrane termed the nuclear envelope(NE), which separates the nucleoplasm and geneticEditors:Tom Misteli and David L.SpectorAdditional Perspectives on The Nucleus available at Copyright#2010Cold Spring Harbor Laboratory Press;all rights reserved;doi:10.1101/cshperspect.a000562Cite this article as Cold Spring Harb Perspect Biol2010;2:a0005621material from the surrounding cytoplasm. However the genetic material in the nucleus is not totally isolated from the rest of the cell. Studded throughout the NE are portals called nuclear pore complexes(NPCs).The NPC is a highly selective,bidirectional transporter for a tremendous range of cargoes.Going into the nucleus,these cargoes include inner nuclear membrane proteins and all the proteins in the nucleoplasm.Going out are RNA-associated proteins that are assembled into ribosomal sub-units or messenger ribonucleoproteins(mRNPs). Once transported,the NPC must ensure these cargos are retained in their respective nuclear and cytoplasmic compartments.All the while the NPC must prevent the passage of nonspe-cific macromolecules,yet allow the free diffu-sion of water,sugars,and ions.These many types of nuclear transport are regulated at mul-tiple stages,providing a powerful extra level of cellular control that is not necessary in prokar-yotes.Assembly,maintenance,and repair of the NPC must somehow occur while maintain-ing the integrity of the NE.Finally,the NPC ap-pears to be an anchor for localization of many nuclear processes,including gene activation and cell cycle regulation(reviewed in Ahmed and Brickner2007;Hetzer and W ente2009). All these requirements demonstrate the com-plex design of the NPC and the integral role it plays in key cellular processes. STRUCTURE OF THE NPC:SET UP OF THE MACHINEThe specifications of the NPC’s transport mac-hinery represent a huge engineering challenge for evolution.No transitional forms of this elab-orate transport system have yet been found in modern day organisms to reveal how it evolved. However,recent clues show that the NPC itself retains in its core a fossil of its ancient origins, indicating that the same mechanism that gen-erated the internal membranes of eukaryotes might also have been responsible for the NPCs and the transport machinery.In the electron microscope,the NPC ap-pears as a complex cylindrical structure with strong octagonal symmetry,measuring some 100–150nm in diameter and50–70nm in thickness depending on the organism(reviewed in W ente2000;Lim et al.2008).This overall ap-pearance seems broadly conserved throughout all eukaryotes.The two membranes of the NE, the outer and inner membranes,join only in a specialized,sharply curved piece of“pore membrane”that forms a grommet in the NE within which the NPC sits.Within each NPC is a core structure containing eight spokes surrounding a central tube.This central hole ( 30nm diameter and 50nm long)is where the nucleoplasm connects to the cytoplasm and where macromolecular exchange occurs. Peripheralfilaments are attached to the core,filling the central hole as well as emanating into the nucleoplasm and cytoplasm.Thesefil-aments form a basket-like structure on the nuclear side of the NPC(Fig.1).One can envision the NPC as being com-prised of layers of interacting proteins,starting with the core structure,moving outwards through its peripheralfilaments,and then to associating clouds of soluble transport factors and peripherally associating protein complexes in the nucleus and cytoplasm(Rout and Aitch-ison2001).These protein interactions can oc-cur on radically different time scales.Some proteins form relatively permanent associations with the core structure,and so are termed nu-clear pore complex components or“nucleo-porins”(“Nups”).Other proteins associate transiently with the NPC,either constantly cycling on and off or attaching only at particular times in the cell’s life cycle.The NPC is covered in binding sites for these transiently associating proteins.Because the NPC is neither a motor nor an enzyme,the interactions provided by its binding sites wholly define the function of the NPC.Recent work,mainly in the yeast Saccharo-myces cerevisiae and in vertebrates,has begun to elucidate the molecular architecture of the NPC(Rout et al.2000;Cronshaw et al. 2002;Alber et al.2007b).Given its large size, the main body of the NPC comprises a surpris-ingly small number of 30unique proteins (Table1).However,because of the NPC’s eight-fold symmetry,these Nups are each present inS.R.Wente and M.P.Rout2Cite this article as Cold Spring Harb Perspect Biol2010;2:a000562multiple copies (usually 16per NPC)resulting in around 400polypeptides for each NPC in every eukaryote (Rout et al.2000;Cronshaw et al.2002;DeGrasse et al.2009).Further redun-dancy is evident from the recent mapping of the yeast NPC.Indeed,the NPC’s structure is mod-ular,consisting of a few highly repetitive protein fold types (Devos et al.2006;Alber et al.2007b;DeGrasse et al.2009).This suggests that the bulk of the NPC’s structure has evolved through multiple duplications of a small precursor set of genes encoding just a handful of progenitor Nups.T o understand its evolutionary origins,the NPC of the highly divergent Trypanosoma was recently characterized (DeGrasse et al.2009).Despite significant divergence in primary struc-ture,the Trypanosome NPC consists mainly of the motifs and domains found in vertebrate and yeast NPCs,indicating on a molecular level that the basic structural components of the NPC are conserved across all eukaryotes.Importantly,this also strongly implies that the last common eukaryotic ancestor had many fea-tures in common with contemporary NPCs,and perhaps provided a key adaptive advantage CentralbasketCytoplasmic SymmetricFG nupsFigure 1.Major structural features of the NPC (based on the architectural map of Alber et al.(2007b);see Table 1and main text for details).Table 1.Nucleoporin homologs of yeast and vertebratesNPC substructure Y east components Vertebrate componentsOuter Ring Nup84subcomplex (Nup84,Nup85,Nup120,Nup133,Nup145C,Sec13,Seh1)Nup107-160complex (Nup160,Nup133,Nup107,Nup96,Nup75,Seh1,Sec13,Aladin,Nup43,Nup37)Inner Ring Nup170subcomplex (Nup170,Nup157,Nup188,Nup192,Nup59,Nup53)Nup155subcomplex (Nup155,Nup205,Nup188,Nup35)Cytoplasmic FG Nups and FilamentsNup159,Nup42Nup358,Nup214,Nlp1Lumenal Ring Ndc1,Pom152,Pom34Gp210,Ndc1,Pom121Symmetric FG Nups Nsp1,Nup57,Nup49,Nup145N,Nup116,Nup100Nup62,Nup58/45,Nup54,Nup98Linker Nups Nup82,Nic96Nup88,Nup93Nucleoplasmic FG Nups and FilamentsNup1,Nup60,Mlp1,Mlp2Nup153,TprNuclear Pore Complexes and Nuclear TransportCite this article as Cold Spring Harb Perspect Biol 2010;2:a0005623for this organism that has been retained,little changed,ever since.The structural proteins making up the bulk of the spokes and rings give the NPC its shape and strength(Fig.1).These core proteins of the NPC also maintain the stability of the nuclear envelope and facilitate the bending of the pore membrane into the inner and outer NE mem-branes.The most equatorial rings,termed the inner rings,are comprised of the Nup170com-plex(yeast)or Nup155complex(vertebrates) (Aitchison et al.1995;Grandi et al.1997;Miller et al.2000)(Fig.1).The inner rings are sand-wiched between the outer rings,which are com-prised of the Nup84complex(yeast)or Nup107 complex(vertebrates)(Table1)(Siniossoglou et al.1996;Fontoura et al.1999;Siniossoglou et al.2000;Belgareh et al.2001;Vasu et al. 2001).T ogether,these Nup complexes form a scaffold that hugs the curved surface of the pore membrane and helps form the central tube through which macromolecular exchange oc-curs(Alber et al.2007a;Alber et al.2007b).Nups in the core scaffold represent roughly half the mass of the whole NPC and are com-posed almost entirely of either b-propeller folds,a-solenoid folds,or a distinct arrange-ment of both in an amino-terminal b-propeller followed by a carboxy-terminal a-solenoid fold. The core scaffold of all eukaryotes appears to retain this basic fold composition(Devos et al.2004;Devos et al.2006;DeGrasse et al. 2009).Strikingly,there are similarities between the structures of the core NPC scaffold curving around the pore membrane and other mem-brane-associated complexes such as clathrin/ adaptin,COPI,and COPII(Fig.1)(Devos et al. 2004;Devos et al.2006).Clathrin/adaptin is involved in coat-mediated endocytosis at the plasma membrane,and COPI and COPII are responsible for coat-mediated vesicular trans-port between the plasma membrane and endo-membrane systems such as the Golgi and ER. Indeed,the similarities between core scaffold Nups and coating complexes have been borne out in numerous crystallographic studies(Berke et al.2004;Hsia et al.2007;Brohawn et al.2008; Debler et al.2008;Brohawn et al.2009;Leksa et al.2009;Seo et al.2009;Whittle and Schwartz 2009),although nearly2billion years of evolu-tion have made it difficult atfirst glance to rec-ognize the common origin of these two groups. However,their common b-propeller and helix-turn-helix repeat structure is still unmistakable (Brohawn et al.2008;Field and Dacks2009). In NPCs the“coat”comprises the core scaffold of the NPC,where—analogous to the curved membrane of a vesicle being stabilized by a COP or clathrin coat—it stabilizes the curved pore membrane.These similarities also give a tantalizing glimpse into the deep evolutionary origins of eukaryotes.It seems early proto-eukaryotes distinguished themselves from their prokaryotes by acquiring a membrane-curving protein module,the“proto-coatomer”(likely composed of a simple b-propeller/a-solenoid protein),that allowed them to mold their plasma membranes into internal compartments. Modern eukaryotes diversified this module into many specialized membrane coating complexes, accounting for the evolution of their internal membrane systems(Devos et al.2004;Devos et al.2006).The framework of the NPC serves two key transport purposes:to form a barrier of defined permeability within the pore,and to facilitate transport of selected macromolecules across it.Both processes are dependent on the correct positioning of critical Nups in the NPC architecture(Radu et al.1995;Strawn et al. 2004;Liu and Stewart2005).Attached to the inside face of the NPC core scaffold,facing the central tube’s cavity,are groups of nucleoporins termed“linker nucleoporins”(Fig.1).T ogether with the inner ring,these seem to form most of the attachment sites for a last set of nucleop-orins,termed“FG Nups”(Alber et al.2007b). These FG Nups,named for their phenylala-nine-glycine repeats,are the direct mediators of nucleocytoplasmic transport(Radu et al. 1995;Strawn et al.2004;Liu and Stewart2005) (see the following section).The core NPC scaffold is connected to a set of integral membrane proteins,which form an outer luminal ring in the NE lumen and anchor the NPC into the NE(Nehrbass et al.1996; Alber et al.2007a;Alber et al.2007b)(Fig.1). Oddly,the membrane nucleoporins seem poorlyS.R.Wente and M.P.Rout4Cite this article as Cold Spring Harb Perspect Biol2010;2:a000562conserved—if at all—across the eukaryotes.The fact that all the currently known pore mem-brane proteins in Aspergillus nidulans can seem-ingly be dispensed with for NPC function and assembly might indicate that there are not st-rong pressures for their conservation,and that there are other membrane proteins that can serve the role(Liu et al.2009).This fact also sets up a quandary—if most or all of the NPC’s presumed membrane anchors are dispensible, how then is the NPC reliably anchored to the membrane?Several groups are seeking the an-swer to this question(Hetzer and W ente2009). OPERATION OF THE MACHINE:THE SOLUBLE PHASEUnderstanding the transport machine requires resolving both its barrier and binding activities. How the NPC machine balances both of these selective functions has been a challenging mys-tery.Studies offluorescently labeled sized dex-trans or gold particles microinjected into cells (Feldherr and Akin1997;Keminer and Peters 1999)have defined the practical permeability limits of the NPC,showing that under physio-logical time scales,macromolecules greater than 40kDa in size do not show any measureable redistribution between the nucleus and cyto-plasm and thus,no movement through the NPC.Conversely,metal ions,small metabolites, and molecules less than 40kDa in mass or 5 nm in diameter can pass relatively freely.NPC permeability is altered in several yeast nup mutants,pinpointing NPC structural elements, including core scaffold components,that are critical to the assembly or maintenance of this barrier(Shulga et al.2000;Denning et al.2001; Shulga and Goldfarb2003;Strawn et al.2004; Patel et al.2007).Larger macromolecules over-come this permeability barrier by interacting either directly with the NPC themselves or through soluble transport factors.These macro-molecules account for a tremendous variety of cargo including proteins,tRNAs,ribosomal subunits,and viral particles(reviewed in Mac-ara2001).Overall,the NPC is capable of trans-porting cargo up to39nm in diameter.This is on par with the size of the ribosomal subunits and viral capsids that are known to move as intact complexes(Pante and Kann2002).Mac-romolecules larger than this can still be trans-located across the NPC,including mRNPs (mRNAs coated with RNA-binding proteins) with masses reaching several hundred thousand daltons.EM images of Balbiani ring mRNP particles associated with the NPC show the posttranscriptional 50nm mRNA–protein particles to rearrange into rodlike structures,de-creasing their maximum diameter to 25nm (Mehlin et al.1992).Thus,cargoes above a limit-ing diameter must rearrange to pass through the selective barrier of the NPC(Daneholt2001).A transport signal and a shuttling receptor for that transport signal are the minimal require-ments for any facilitated translocation(reviewed in Mattaj and Englmeier1998;Pemberton and Paschal2005).The targeting of proteins into or out of the nucleus requires specific amino acid sequence spans,termed nuclear local-ization sequences(NLSs)or nuclear export sequences(NESs).All the information required to target a protein to the nucleus is within these short sequences.In fact,fusion of an NLS to a nonnuclear protein is sufficient to mediate its transport and import to the nucleus(Goldfarb et al.1986).For proteins,there are many distinct types of NLSs and NESs.For example,the clas-sical NLS(cNLS)is the simplefive amino acid peptide KKKRK,necessary and sufficient for targeting its attached protein to the nucleus (Goldfarb et al.1986),whereas many proteins carry a more complex“bipartite”NLS con-sisting of two clusters of basic amino acids,sep-arated by a spacer of roughly10amino acids (Dingwall et al.1988).However,the full spec-trum of sequences recognized by each transport receptor has not yet been carefully and fully defined.The most in depth analysis of NLS structural recognition by a transport receptor and extrapolation to predicting cargoes on a broader genome level has only been reported for one transport receptor(Lee et al.2006).The key parameters defining an NLS or NES include critical tests for necessity and sufficiency in the endogenous protein.Importantly,some proteins undergo dynamic cycles of nuclear import and export and harbor both NLSs and Nuclear Pore Complexes and Nuclear TransportCite this article as Cold Spring Harb Perspect Biol2010;2:a0005625NESs.This can increase the complexity of iden-tifying the signals.Moreover,the recognition and accessibility of the signals can be controlled by signaling,cell cycle,and developmental events(reviewed in W eis2003;T erry et al.2007).During NPC translocation,soluble trans-port factors are required to either bring cargo to the NPC or modulate cargo translocation across the NPC.Most of these soluble transport factors come from the family of proteins known as the karyopherins(Table2).The karyopherins (also called importins,exportins,and tranpor-tins)were thefirst family of shuttling transport factors discovered.Fourteen karyopherin family members are found in Saccharomyces cerevisiae whereas at least20have been found in metazo-ans(reviewed in Fried and Kutay2003;Pember-ton and Paschal2005).Most karyopherins bind their cargoes directly.However,in some cases an adaptor protein is needed in addition to theTable2.Karyopherin transport factors of yeast and vertebratesÃS.cerevisiae KaryopherinsVertebrateKaryopherins Examples of Cargo(s):(v)–vertebrate,(sc)–S.cerevisiaeKap95Importin-b1Imports via sc-Kap60/v-importin-a adaptor proteinswith cNLS;Imports via v-Snurportin the UsnRNPs;with no adaptor,imports v-cargo SREBP-2,HIV Rev,HIV T A T,cyclin BKap104Transportin orTransportin2Imports sc-cargo–Nab2,Hrp1;v-cargo–PY-NLS proteins,mRNA-binding proteins,histones,ribosomal proteinsKap108/Sxm1Importin8Imports sc-cargo–Lhp1,ribosomal proteins;v-cargo–SRP19,SmadKap109/Cse1CAS Imports sc-cargo–Kap60/Srp1;v-cargo–importin a s Kap111/Mtr10Transportin SR1or SR2Imports sc-cargo–Npl3,tRNAs;v-cargo–SR proteins,HuRKap114Importin9Imports sc-cargo–TBP,histones,Nap1,Sua7;v-cargo–histones,ribosomal proteinsKap119/Nmd5Importin7Imports sc-cargo–Hog1,Crz1,Dst1,ribosomal proteins,histones;v-cargo–Smad,ERK,GR,ribosomalproteinsKap120HsRanBP11Imports sc-cargo–Rpf1Kap121/Pse1Importin5/Importinb3/RanBP5Imports sc-cargo–Yra1,Spo12,Ste12,Y ap1,Pho4, histones,ribosomal proteins;v-cargo–histones, ribosomal proteinsKap122/Pdr6-Imports sc-cargo–T oa1and T oa2,TFIIAKap123Importin4Imports sc-cargo–SRP proteins,histones,ribosomalproteins;v-cargo-Transition Protein2,histones,ribosomal protein S3aKap127/Los1Exportin-t Exports tRNAsKap142/Msn5Exportin5sc-cargo–imports replication protein A;exports Pho4,Crz1,Cdh1;v-cargo-exports pre-miRNAImportin13v-cargo–imports UBC9,Y14;exports eIF1ACrm1/Xpo1CRM1/Exportin1Exports proteins with leucine-rich NES,60S ribosomalsubunits(via NMD3adaptor),40S ribosomalsubunits—Exportin4v-cargo–imports SOX2,SRY;exports Smad3,eIF5A —Exportin6Exports profilin,actin—Exportin7/RanBP16Exports p50-RhoGAPÃBased on references cited within and adapted from Tran et al.2007a and DeGrasse et al.2009.S.R.Wente and M.P.Rout6Cite this article as Cold Spring Harb Perspect Biol2010;2:a000562karyopherin to recognize signals.Not only do karyopherins have a cargo-binding domain,they also have an NPC-binding domain(s)as well as a binding domain at the amino-terminus for the small Ras-like GTPase Ran (see the fol-lowing paragraph)(reviewed in Macara 2001;Harel and Forbes 2004).Overall,karyopherin family members share only modest sequence homology,with the greatest similarity being withintheirRan-bindingdomains(Gorlichetal.1997).However,a hallmark architecture within the karyopherins,as determined by recent high-resolution structural studies,is the tandem HEA T-repeat fold formed by antiparallel heli-ces connected by a short turn (reviewed in Conti and Izaurralde 2001).The HEA T-repeats ar-range to form a superhelical structure,similar to a snail’s shell.This folding is reminiscent of the helix-turn-helix repeats found in the NPC’s core scaffold proteins.This similarity raises the intriguing possibility that karyopherins di-verged from a common structure involved in both stationary and soluble phases of transport.The association and dissociation of a karyo-pherin-cargo complex is regulated by direct binding of the small GTPase Ran (Fig.2)(reviewed in Fried and Kutay 2003;Madrid and W eis 2006;Cook et al.2007).In vitro bind-ing studies show that import complexes are dissociated by RanGTP binding.Conversely,export complexes are formed via RanGTP association (Rexach and Blobel 1995;Floer and Blobel 1996;Chi and Adam 1997;Floer et al.1997;Kutay et al.1997a;Kutay et al.1997b;Nakielny et al.1999).Based on the localizations of the Ran GTPase activating protein (RanGAP)in the cytoplasm and the Ran guanine nucleo-tide exchange factor (RanGEF)in the nucleo-plasm,cytoplasmic Ran is primarily in the GDP-bound state whereas nucleoplasmic Ran is kept primarily in the GTP-bound state (Fig.2).The gradient formed from these localizations has been elegantly demonstrated by imaging fluorescence resonance energy transfer-based biosensors (Kalab et al.2002).The RanGTP gra-dient across the two faces of the NPC is essentialExport cycleImport cycle Figure 2.The nuclear transport cycle for karyopherins and their cargos.See main text for details.Nuclear Pore Complexes and Nuclear TransportCite this article as Cold Spring Harb Perspect Biol 2010;2:a0005627for establishing the directionality of karyophe-rin-mediated transport.The pathway for karyopherin-mediated tra-nslocation is well described(reviewed in W eis 2003;T erry et al.2007)(Fig.2).For import, a specific karyopherin recognizes its cognate cargo in the cytoplasm where RanGTP levels are low.The karyopherin mediates the binding of the import complex to the NPC and facili-tates translocation through the NPC.Once the complex moves through the NPC,release and dissociation of the karyopherin-cargo complex are stimulated by RanGTP in the nucleus.The karyopherin bound to RanGTP is then recycled back to the cytoplasm.Finally,GTP hydrolysis of Ran on the cytoplasmic side frees the karyo-pherin to interact with a second cargo molecule for further cycles of transport.Overall,Ran decreases the affinity of the karyopherin for its cargo(reviewed in Macara2001;Cook et al. 2007).For export complexes,an analogous pro-cess occurs,but in this case,RanGTP binding increases the affinity of the karyopherin b for the export cargo.For example,for the exporting karyopherin Crm1and an export cargo SPN1 (snurportin1adaptor for UsnRNPs),the Crm1affinities for RanGTP and SPN1in the ternary RanGTP-Crm1-SPN1complex are increased 1000-fold(Paraskeva et al.1999; Monecke et al.2009).Actual movement thr-ough the NPC does not require energy input. The Ran affinity switches provide the energy for efficient cargo delivery and release.The only possible exception to this rule involves the import of large cargoes,where the presence of Ran and hydrolyzable GTP may be required for the import of cargoes.500kDa in vitro (Lyman et al.2002).It was also originally thought that an individual karyopherin was adapted for either import or export,but not both.However,there are now documented ex-amples of karyopherins functioning in both im-port and export,although with different cargoes in each direction(Y oshida and Blobel2001).In addition to protein import and export, karyopherins can also transport RNAs.For ex-ample,the karyopherins Crm1and exportin-t mediate the export of uridine-rich small nu-clear RNAs(U snRNAs)and tRNAs,respectively (Simos et al.2002;Rodriguez2004).Transport is accomplished via direct binding of karyo-pherins to RNA or to signal sequences within the protein components of the RNP complexes. For example,Crm1does not bind UsnRNAs directly and requires the adaptor PHAX that binds the cap-complex on the RNA(Ohno et al.2000).However,exportin-t directly inter-acts with tRNAs(Arts et al.1998;Hellmuth et al.1998;Kutay et al.1998;Lipowsky et al. 1999).Karyopherins are also involved in the export of some viral RNAs,including their mRNAs (Carmody and W ente2009).However,the primary mRNP export transport receptor is a nonkaryopherin designated Mex67in yeast and NXF1in metazoans,which heterodimer-izes with a protein termed respectively Mtr2 or p15/Nxt1(Erkmann and Kutay2004). Even though Mex67is unrelated in sequence and structure to the karyopherin family,it has all the requirements of a transport receptor: cargo binding,nucleocytoplasmic shuttling, and NPC-binding.The stoichiometry of the Mex67-Mtr2heterodimer per transported mRNP is unknown;Mex67-Mtr2either is re-cruited directly to the mRNA or interacts cotranscriptionally with proteins of the mRNP assembly(Erkmann and Kutay2004;Carmody and W ente2009).Like karyopherins,Mex67-Mtr2heterodimers bind directly to FG Nups, although it seems they prefer different subsets of FG Nups to their karyopherin counterparts, which might reflect how the karyopherin medi-ated transport pathways and mRNPexport path-ways are kept apart at the NPC(Strawn et al. 2001;T erry et al.2007;T erry and W ente2007).As mRNP is a major source of traffic across the NPC,it is interesting that most of this tran-sit is facilitated by non-karyopherin carriers. Because Ran is not utilized to establish a gra-dient,directionality in the mRNA export path-way is conferred by proteins that modify the mRNPs as they cross the NPC.Chief among these is the protein Dbp5.Dbp5is a member of the SF2helicase superfamily of RNA-dependent A TPases(Snay-Hodge et al.1998; Tseng et al.1998)and carries a DEAD/H-box sequence motif.Such DEAD-box proteins areS.R.Wente and M.P.Rout8Cite this article as Cold Spring Harb Perspect Biol2010;2:a000562。

Page 1©2007 Whelen Engineering Company Inc.Form No.14105A (020911)For warranty information regarding this product, visit /warrantyA u t o m o t i v e : Installation Guide:Lightbar Roof Rack Mount 2007 - 2011 Ford Expedition2011 Ford Explorer®ENGINEERING COMPANY INC.Internet: Sales e-mail:*******************Customer Service e-mail:*******************51 Winthrop Road,Chester, Connecticut 06412-0684Phone: (860) 526-9504Set Screw (QTY 4)Stop Nut (Qty 6)Installation:IMPORTANT! The lightbar should be a minimum of 16" from any radio antennas!1.Remove the cover from roof rack (see photos), insert the 3 slide mounts into the track in the roof rack and replace the cover (Fig. 1).2.Snap the nylon retaining washers onto the slide mounts then secure the mounting bracket to the slide mounts with the supplied hardware.3.Insert the 3 slide mounts into the tracks on the vehicle roof rack then snap the “nylon retaining washers” onto the slide locks. This will hold them in place while you are doing the installation.4.Slide both mounting brackets into their track in the bottom of the lightbar base (Fig. 2) and secure them there with the supplied set screws.5.When you have positioned the mounting slides where you wish to mount the lightbar, secure the bracket to the slides using the supplied 14-20elastic stop nuts and 1/4” flat washers (Fig. 3) and installation is complete.©2007 Whelen Engineering Company Inc.Form No.14105A (020911)For warranty information regarding this product, visit /warrantyA u t o m o t i v e : Installation Guide:Lightbar Roof Rack Mount 2007 - 2011 Ford Expedition2011 Ford Explorer®ENGINEERING COMPANY INC.Internet: Sales e-mail:*******************Customer Service e-mail:*******************51 Winthrop Road,Chester, Connecticut 06412-0684Phone: (860) 526-9504Set Screw (QTY 4)Stop Nut (Qty 6)Installation:IMPORTANT! The lightbar should be a minimum of 16" from any radio antennas!1.Remove the cover from roof rack (see photos), insert the 3 slide mounts into the track in the roof rack and replace the cover (Fig. 1).2.Snap the nylon retaining washers onto the slide mounts then secure the mounting bracket to the slide mounts with the supplied hardware.3.Insert the 3 slide mounts into the tracks on the vehicle roof rack then snap the “nylon retaining washers” onto the slide locks. This will hold them in place while you are doing the installation.4.Slide both mounting brackets into their track in the bottom of the lightbar base (Fig. 2) and secure them there with the supplied set screws.5.When you have positioned the mounting slides where you wish to mount the lightbar, secure the bracket to the slides using the supplied 14-20elastic stop nuts and 1/4” flat washers (Fig. 3) and installation is complete.。

Preparation of nanosized Mn 3O 4/SBA-15catalyst for complete oxidation of low concentration EtOH in aqueous solution with H 2O 2Yi-Fan Han *,Fengxi Chen,Kanaparthi Ramesh,Ziyi Zhong,Effendi Widjaja,Luwei ChenInstitute of Chemical and Engineering Sciences,1Pesek Road,Jurong Island 627833,Singapore Received 11May 2006;received in revised form 18December 2006;accepted 29May 2007Available online 2June 2007AbstractA new heterogeneous Fenton-like system consisting of nano-composite Mn 3O 4/SBA-15catalyst has been developed for the complete oxidation of low concentration ethanol (100ppm)by H 2O 2in aqueous solution.A novel preparation method has been developed to synthesize nanoparticles of Mn 3O 4by thermolysis of manganese (II)acetylacetonate on SBA-15.Mn 3O 4/SBA-15was characterized by various techniques like TEM,XRD,Raman spectroscopy and N 2adsorption isotherms.TEM images demonstrate that Mn 3O 4nanocrystals located mainly inside the SBA-15pores.The reaction rate for ethanol oxidation can be strongly affected by several factors,including reaction temperature,pH value,catalyst/solution ratio and concentration of ethanol.A plausible reaction mechanism has been proposed in order to explain the kinetic data.The rate for the reaction is supposed to associate with the concentration of intermediates (radicals: OH,O 2Àand HO 2)that are derived from the decomposition of H 2O 2during reaction.The complete oxidation of ethanol can be remarkably improved only under the circumstances:(i)the intermediates are stabilized,such as stronger acidic conditions and high temperature or (ii)scavenging those radicals is reduced,such as less amount of catalyst and high concentration of reactant.Nevertheless,the reactivity of the presented catalytic system is still lower comparing to the conventional homogenous Fenton process,Fe 2+/H 2O 2.A possible reason is that the concentration of intermediates in the latter is relatively high.#2007Elsevier B.V .All rights reserved.Keywords:Hydrogen peroxide;Fenton catalyst;Complete oxidation of ethanol;Mn 3O 4/SBA-151.IntroductionRemediation of wastewater containing organic constitutes is of great importance because organic substances,such as benzene,phenol and other alcohols may impose toxic effects on human and animal anic effluents from pharmaceu-tical,chemical and petrochemical industry usually contaminate water system by dissolving into groundwater.Up to date,several processes have been developed for treating wastewater that contains toxic organic compounds,such as wet oxidation with or without solid catalysts [1–4],biological oxidation,supercritical oxidation and adsorption [5,6],etc.Among them,catalytic oxidation is a promising alternative,since it avoids the problem of the adsorbent regeneration in the adsorption process,decreases significantly the temperature and pressure in non-catalytic oxidation techniques [7].Generally,the disposalof wastewater containing low concentration organic pollutants (e.g.<100ppm)can be more costly through all aforementioned processes.Thus,catalytic oxidation found to be the most economical way for this purpose with considering its low cost and high efficiency.Currently,a Fenton reagent that consists of homogenous iron ions (Fe 2+)and hydrogen peroxide (H 2O 2)is an effective oxidant and widely applied for treating industrial effluents,especially at low concentrations in the range of 10À2to 10À3M organic compounds [8].However,several problems raised by the homogenous Fenton system are still unsolved,e.g.disposing the iron-containing waste sludge,limiting the pH range (2.0–5.0)of the aqueous solution,and importantly irreversible loss of activity of the reagent.To overcome these drawbacks raised from the homogenous Fenton system,since 1995,a heterogeneous Fenton reagent using metal ions exchanged zeolites,i.e.Fe/ZSM-5has proved to be an interesting alternative catalytic system for treating wastewater,and showed a comparable activity with the homogenous Fenton system [9].However,most reported heterogeneous Fenton reagents still need UV radiation during/locate/apcatbApplied Catalysis B:Environmental 76(2007)227–234*Corresponding author.Tel.:+6567963806.E-mail address:han_yi_fan@.sg (Y .-F.Han).0926-3373/$–see front matter #2007Elsevier B.V .All rights reserved.doi:10.1016/j.apcatb.2007.05.031oxidation of organic compounds.This might limit the application of homogeneous Fenton system.Exploring other heterogeneous catalytic system considering the above disadvantages,is still desirable for this purpose.Here,we present an alternative catalytic system for the complete oxidation of organic com-pounds in aqueous solution using supported manganese oxide as catalyst under mild conditions,which has rarely been addressed.Mn-containing oxide catalysts have been found to be very active for the catalytic wet oxidation of organic effluents (CWO)[10–14],which is operated at high air pressures(1–22MPa)and at high temperatures(423–643K)[15].On the other hand,manganese oxide,e.g.MnO2[16],is well known to be active for the decomposition of H2O2in aqueous solution to produce hydroxyl radical( OH),which is considered to be the most robust oxidant so far.The organic constitutes can be deeply oxidized by those radicals rapidly[17].The only by-product is H2O from decomposing H2O2.Therefore,H2O2is a suitable oxidant for treating the wastewater containing organic compounds.Due to the recent progress in the synthesis of H2O2 directly from H2and O2[18,19],H2O2is believed to be produced through more economical process in the coming future.So,the heterogeneous Fenton system is economically acceptable.In this study,nano-crystalline Mn3O4highly dispersed inside the mesoporous silica,SBA-15,has been prepared by thermolysis of organic manganese(II)acetylacetonate in air. We expect the unique mesoporous structure may provide add-itional function(confinement effect)to the catalytic reaction, i.e.occluding/entrapping large organic molecules inside pores. The catalyst as prepared has been examined for the complete oxidation of ethanol in aqueous solution with H2O2,or to say, wet peroxide oxidation.Ethanol was selected as a model organic compound because(i)it is one of the simplest organic compounds and can be easily analyzed,(ii)it has high solu-bility in water due to its strong hydrogen bond with water molecule and(iii)the structure of ethanol is quite stable and only changed through catalytic reaction.Presently,for thefirst time by using the Mn3O4/SBA-15catalyst,we investigated the peroxide ethanol oxidation affected by factors such as temperature,pH value,ratio of catalyst(g)and volume of solution(L),and concentration of ethanol in aqueous solution. In addition,plausible reaction mechanisms are established to explain the peroxidation of ethanol determined by the H2O2 decomposition.2.Experimental2.1.Preparation and characterization of Mn3O4/SBA-15 catalystSynthesis of SBA-15is similar to the previous reported method[20]by using Pluronic P123(BASF)surfactant as template and tetraethyl orthosilicate(TEOS,98%)as silica source.Manganese(II)acetylacetonate([CH3COCH C(O)CH3]2Mn,Aldrich)by a ratio of2.5mmol/gram(SBA-15)werefirst dissolved in acetone(C.P.)at room temperature, corresponding to ca.13wt.%of Mn3O4with respect to SBA-15.The preparation method in detail can be seen in our recent publications[21,22].X-ray diffraction profiles were obtained with a Bruker D8 diffractometer using Cu K a radiation(l=1.540589A˚).The diffraction pattern was taken in the Bragg angle(2u)range at low angles from0.68to58and at high angles from308to608at room temperature.The XRD patterns were obtained by scanning overnight with a step size:0.028per step,8s per step.The dispersive Raman microscope employed in this study was a JY Horiba LabRAM HR equipped with three laser sources(UV,visible and NIR),a confocal microscope,and a liquid nitrogen cooled charge-coupled device(CCD)multi-channel detector(256pixelsÂ1024pixels).The visible 514.5nm argon ion laser was selected to excite the Raman scattering.The laser power from the source is around20MW, but when it reached the samples,the laser output was reduced to around6–7MW after passing throughfiltering optics and microscope objective.A100Âobjective lens was used and the acquisition time for each Raman spectrum was approximately 60–120s depending on the sample.The Raman shift range acquired was in the range of50–1200cmÀ1with spectral resolution1.7–2cmÀ1.Adsorption and desorption isotherms were collected on Autosorb-6at77K.Prior to the measurement,all samples were degassed at573K until a stable vacuum of ca.5m Torr was reached.The pore size distribution curves were calculated from the adsorption branch using Barrett–Joyner–Halenda(BJH) method.The specific surface area was assessed using the BET method from adsorption data in a relative pressure range from 0.06to0.10.The total pore volume,V t,was assessed from the adsorbed amount of nitrogen at a relative pressure of0.99by converting it to the corresponding volume of liquid adsorbate. The conversion factor between the volume of gas and liquid adsorbate is0.0,015,468for N2at77K when they are expressed in cm3/g and cm3STP/g,respectively.The measurements of transmission electron microscopy (TEM)were performed at Tecnai TF20S-twin with Lorentz Lens.The samples were ultrasonically dispersed in ethanol solvent,and then dried over a carbon grid.2.2.Kinetic measurement and analysisThe experiment for the wet peroxide oxidation of ethanol was carried out in a glass batch reactor connected to a condenser with continuous stirring(400rpm).Typically,20ml of aqueous ethanol solution(initial concentration of ethanol: 100ppm)wasfirst taken in the round bottomflask(reactor) together with5mg of catalyst,corresponding to ca.1(g Mn)/30 (L)ratio of catalyst/solution.Then,1ml of30%H2O2solution was introduced into the reactor at different time intervals (0.5ml at$0min,0.25ml at32min and0.25ml at62min). The total molar ratio of H2O2/ethanol is about400/1. Hydrochloric acid(HCl,0.01M)was used to acidify the solution if necessary.NH4OH(0.1M)solution was used to adjust pH to9.0when investigating the effect of pH.The pH for the deionized water is ca.7.0(Oakton pH meter)and decreased to 6.7after adding ethanol.All the measurements wereY.-F.Han et al./Applied Catalysis B:Environmental76(2007)227–234 228performed under the similar conditions described above if without any special mention.For comparison,the reaction was also carried out with a typical homogenous Fenton reagent[17], FeSO4(5ppm)–H2O2,under the similar reaction conditions.The conversion of ethanol during reaction was detected using gas chromatography(GC:Agilent Technologies,6890N), equipped with HP-5capillary column connecting to a thermal conductive detector(TCD).There is no other species but ethanol determined in the reaction system as evidenced by the GC–MS. Ethanol is supposed to be completely oxidized into CO2and H2O.The variation of H2O2concentration during reaction was analyzed colorimetrically using a UV–vis spectrophotometer (Epp2000,StellarNet Inc.)after complexation with a TiOSO4/ H2SO4reagent[18].Note that there was almost no measurable leaching of Mn ion during reaction analyzed by ICP(Vista-Mpx, Varian).3.Results and discussion3.1.Characterization of Mn3O4/SBA-15catalystThe structure of as-synthesized Mn3O4inside SBA-15has beenfirst investigated with powder XRD(PXRD),and the profiles are shown in Fig.1.The profile at low angles(Fig.1a) suggests that SBA-15still has a high degree of hexagonal mesoscopic organization even after forming Mn3O4nanocrys-tals[23].Several peaks at high angles of XRD(Fig.1b)indicate the formation of a well-crystallized Mn3O4.All the major diffraction peaks can be assigned to hausmannite Mn3O4 structure(JCPDS80-0382).By N2adsorption measurements shown in Fig.2,the pore volume and specific surface areas(S BET)decrease from 1.27cm3/g and937m2/g for bare SBA-15to0.49cm3/g and 299m2/g for the Mn3O4/SBA-15,respectively.About7.7nm of mesoporous diameter for SBA-15decreases to ca.6.3nm for Mn3O4/SBA-15.The decrease of the mesopore dimension suggests the uniform coating of Mn3O4on the inner walls of SBA-15.This nano-composite was further characterized by TEM. Obviously,the SBA-15employed has typical p6mm hex-agonal morphology with the well-ordered1D array(Fig.3a). The average pore size of SBA-15is ca.8.0nm,which is very close to the value(ca.7.7nm)determined by N2adsorption. Along[001]orientation,Fig.3b shows that the some pores arefilled with Mn3O4nanocrystals.From the pore A to D marked in Fig.3b correspond to the pores from empty to partially and fullyfilled;while the features for the SBA-15 nanostructure remains even after forming Mn3O4nanocrys-tals.Nevertheless,further evidences for the location of Mn3O4inside the SBA-15channels are still undergoing in our group.Raman spectra obtained for Mn3O4/SBA-15is presented in Fig.4a.For comparison the Raman spectrum was also recorded for the bulk Mn3O4(97.0%,Aldrich)under the similar conditions(Fig.4b).For the bulk Mn3O4,the bands at310,365, 472and655cmÀ1correspond to the bending modes of Mn3O4, asymmetric stretch of Mn–O–Mn,symmetric stretch of Mn3O4Fig.1.XRD patterns of the bare SBA-15and the Mn3O4/SBA-15nano-composite catalyst.(a)At low angles:(A)Mn3O4/SBA-15,(B)SBA-15;and (b)at high angles of Mn3O4/SBA-15.Fig.2.N2adsorption–desorption isotherms:(!)SBA-15,(~)Mn3O4/SBA-15.Y.-F.Han et al./Applied Catalysis B:Environmental76(2007)227–234229groups,respectively [24–26].However,a downward shift ($D n 7cm À1)of the peaks accompanying with a broadening of the bands was observed for Mn 3O 4/SBA-15.For instance,the distinct feature at 655cm À1for the bulk Mn 3O 4shifted to 648cm À1for the nanocrystals.The Raman bands broadened and shifted were observed for the nanocrystals due to the effect of phonon confinement as suggested previously in the literature [27,28].Furthermore,a weak band at 940cm À1,which should associate with the stretch of terminal Mn O,is an indicative of the existence of the isolated Mn 3O 4group [26].The assignment of this unique band has been discussed in our previous publication [22].3.2.Kinetic study3.2.1.Blank testsUnder a typical reaction conditions,that is,20ml of 100ppm ethanol aqueous solution (pH 6.7)mixed with 1ml of 30%H 2O 2,at 343K,there is no conversion of ethanol was observed after running for 120min in the absence of catalyst or in the presence of bare SBA-15(5mg).Also,under the similar conditions in H 2O 2-free solution,ethanol was not converted for all blank tests even with Mn 3O 4/SBA-15catalyst (5mg)in the reactor.It suggests that a trace amount of oxygen dissolved in water or potential dissociation of adsorbed ethanol does not have any contribution to the conversion of ethanol under reaction conditions.To study the effect of low temperature evaporation of ethanol during reaction,we further examined the concentration of ethanol (100ppm)versus time at different temperatures in the absence of catalyst and H 2O 2.Loss of ca.5%ethanol was observed only at 363K after running for 120min.Hence,to avoid the loss of ethanol through evaporation at high temperatures,which may lead to a higher conversion of ethanol than the real value,the kinetic experiments in this study were performed at or below 343K.The results from blank tests confirm clearly that ethanol can be transformed only by catalytic oxidation during reaction.3.2.2.Effect of amount of catalystThe effect of amount of catalyst on ethanol oxidation is presented in Fig.5.Different amounts of catalyst ranging from 2to 10mg were taken for the same concentration of ethanol (100ppm)in aqueous solution under the standard conditions.It can be observed that the conversion of ethanol increases monotonically within 120min,reaching 15,20and 12%for 2,5and 10mg catalysts,respectively.On the other hand,Fig.5shows that the relative reaction rates (30min)decreased from 0.7to ca 0.1mmol/g Mn min with the rise of catalyst amount from 2to 10mg.Apparently,more catalyst in the system may decrease the rate for ethanol peroxidation,and a proper ratio of catalyst (g)/solution (L)is required for acquiring a balance between the overall conversion of ethanol and reaction rate.In order to investigate the effects from other factors,5mg (catalyst)/20ml (solution),corresponding to 1(g Mn )/30(L)ratio of catalyst/solution,has been selected for the followedexperiments.Fig.4.Raman spectroscopy of the Mn 3O 4/SBA-15(a)and bulk Mn 3O 4(b).Fig.3.TEM images recorded along the [001]of SBA-15(a),Mn 3O 4/SBA-15(b):pore A unfilled with hexagonal structure,pores B and C partially filled and pore D completely filled.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–2342303.2.3.Effect of temperatureAs shown in Fig.6,the reaction rate increases with increasing the reaction temperature.After 120min,the conversion of ethanol increases from 12.5to 20%when varying the temp-erature from 298to 343K.Further increasing the temperature was not performed in order to avoid the loss of ethanol by evaporation.Interestingly,the relative reaction rate increased with time within initial 60min at 298and 313K,but upward tendency was observed above 333K.3.2.4.Effect of pHIn the pH range from 2.0to 9.0,as illustrated in Fig.7,the reaction rate drops down with the rise of pH.It indicates that acidic environment,or to say,proton concentration ([H +])in the solution is essential for this reaction.With considering our target for this study:purifying water,pH approaching to 7.0in the reaction system is preferred.Because acidifying the solution with organic/inorganic acids may potentially causea second time pollution and result in surplus cost.Actually,there is almost no effect on ethanol conversion with changing pH from 5.5to 6.7in this system.It is really a merit comparing with the conventional homogenous Fenton system,by which the catalyst works only in the pH range of 2.0–5.0.3.2.5.Effect of ethanol concentrationThe investigation of the effect of ethanol concentration on the reaction rate was carried out in the ethanol ranging from 50to 500ppm.The results in Fig.8show that the relative reaction rate increased from 0.07to 2.37mmol/g Mn min after 120min with increasing the concentration of ethanol from 50to 500ppm.It is worth to note that the pH value of the solution slightly decreased from 6.7to 6.5when raising the ethanol concentration from 100to 500ppm.paring to a typical homogenous Fenton reagent For comparison,under the similar reaction conditions ethanol oxidation was performed using aconventionalFig.5.The ethanol oxidation as a function of time with different amount of catalyst.Conversion of ethanol vs.time (solid line)on 2mg (&),5mg (*)and 10mg (~)Mn 3O 4/SBA-15catalyst,the relative reaction rate vs.time (dash line)on 2mg (&),5mg (*)and 10mg (~)Mn 3O 4/SBA-15catalyst.Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,708C and pH of6.7.Fig.6.The ethanol oxidation as a function of temperature.Conversion of ethanol vs.time (solid line)at 258C (&),408C (*),608C (~)and 708C (!),the relative reaction rate vs.time (dash line)at 258C (&),408C (*),608C (~)and 708C (5).Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,pH of 6.7,5mg ofcatalyst.Fig.7.The ethanol oxidation as a function of pH value.Conversion of ethanol vs.time (solid line)at pH value of 2.0(&),3.5(*),4.5(~),5.5(!),6.7(^)and 9.0("),the relative reaction rate vs.time (dash line)at pH value of 2.0(&),3.5(*),4.5(~),5.5(5),6.7(^)and 9.0(").Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,708C,5mg ofcatalyst.Fig.8.The ethanol oxidation as a function of ethanol concentration.Conver-sion of ethanol vs.time (solid line)for ethanol concentration (ppm)of 50(&),100(*),300(~),500(!),the relative reaction rate vs.time (dash line)for ethanol concentration (ppm)of 50(&),100(*),300(~),500(5).Condi-tions:20ml of ethanol,pH of 6.7,1ml of 30%H 2O 2,708C,5mg of catalyst.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234231homogenous reagent,Fe 2+(5ppm)–H 2O 2(1ml)at pH of 5.0.It has been reported to be an optimum condition for this system [17].As shown in Fig.9,the reaction in both catalytic systems exhibits a similar behavior,that is,the conversion of ethanol increases with extending the reaction time.Varying reaction temperature from 298to 343K seems not to impact the conversion of ethanol when using the homogenous Fenton reagent.Furthermore,the conversion of ethanol (defining at 120min)in the system of Mn 3O 4/SBA-15–H 2O 2is about 60%of that obtained from the conventional Fenton reagent.There are no other organic compounds observed in the reaction mixture other than ethanol suggesting that ethanol directly decomposing to CO 2and H 2O.3.2.7.Decomposition of H 2O 2In the aqueous solution,the capability of metal ions such as Fe 2+and Mn 2+has long been evidenced to be effective on the decomposition of H 2O 2to produce the hydroxyl radical ( OH),which is oxidant for the complete oxidation/degrading of organic compounds [9,17].Therefore,ethanol oxidation is supposed to be associated with H 2O 2decomposition.The investigation of H 2O 2decomposition has been performed under the reaction conditions (in an ethanol-free solution)with different amounts of catalyst.H 2O 2was introduced into the reaction system by three steps,initially 0.5ml followed by twice 0.25ml at 32and 62min,the pH of 6.7is set for all experiments except pH of 5.0for Fe 2+.As shown in Fig.10,H 2O 2was not converted in the absence of catalyst or presence of bare SBA-15(5mg);in contrast,by using the Mn 3O 4/SBA-15catalyst we observed that ca.Ninety percent of total H 2O 2was decomposed in the whole experiment.It can be concluded that that dissociation of H 2O 2is mainly caused by Mn 3O paratively,the rate of H 2O 2decomposition is relatively low with the homogenous Fenton reagent,total conversion of H 2O 2,was ca.50%after runningfor 120min.Considering the fact that H 2O 2decomposition can be significantly enhanced with the rise of Fe 2+concentration,however,it seems not to have the influence on the reaction rate for ethanol oxidation simultaneously.The similar behavior of H 2O 2decomposition was also observed during ethanol oxidation.The rate for ethanol oxidation is lower for Mn 3O 4/SBA-15comparing to the conventional Fenton reagent.The possible reasons will be discussed in the proceeding section.3.3.Plausible reaction mechanism for ethanol oxidation with H 2O 2In general,the wet peroxide oxidation of organic constitutes has been suggested to proceed via four steps [15]:activation of H 2O 2to produce OH,oxidation of organic compounds withOH,recombination of OH to form O 2and wet oxidation of organic compounds with O 2.It can be further described by Eqs.(1)–(4):H 2O 2À!Catalyst =temperture 2OH(1)OH þorganic compoundsÀ!Temperatureproduct(2)2 OHÀ!Temperature 12O 2þH 2O(3)O 2þorganic compoundsÀ!Temperature =pressureproduct(4)The reactive intermediates produced from step 1(Eq.(1))participate in the oxidation through step 2(Eq.(2)).In fact,several kinds of radical including OH,perhydroxyl radicals ( HO 2)and superoxide anions (O 2À)may be created during reaction.Previous studies [29–33]suggested that the process for producing radicals could be expressed by Eqs.(5)–(7)when H 2O 2was catalytically decomposed by metal ions,such asFeparison of ethanol oxidation in systems of typical homogenous Fenton catalyst (5ppm of Fe 2+,20ml of ethanol (100ppm),1ml of 30%H 2O 2,pH of 5.0acidified with HCl)at room temperature (~)and 708C (!),and Mn 3O 4/SBA-15catalyst (&)under conditions of 20ml of ethanol (100ppm),pH of 6.7,1ml of 30%H 2O 2,708C,5mg ofcatalyst.Fig.10.An investigation of H 2O 2decomposition under different conditions.One milliliter of 30%H 2O 2was dropped into the 20ml deionized water by three intervals,initial 0.5ml followed by twice 0.25ml at 32and 62min.H 2O 2concentration vs.time:by calculation (&),without catalyst (*),SBA-15(~),5ppm of Fe 2+(!)and Mn 3O 4/SBA-15(^).Rest conditions:5mg of solid catalyst,pH of 7.0(5.0for Fe 2+),708C.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234232and Mn,S þH 2O 2!S þþOH Àþ OH (5)S þþH 2O 2!S þ HO 2þH þ(6)H 2O $H þþO 2À(7)where S and S +represent reduced and oxidized metal ions,both the HO 2and O 2Àare not stable and react further with H 2O 2to form OH through Eqs.(8)and (9):HO 2þH 2O 2! OH þH 2O þO 2(8)O 2ÀþH 2O 2! OH þOH ÀþO 2(9)Presently, OH radical has been suggested to be the main intermediate responsible for oxidation/degradation of organic compounds.Therefore,the rate for ethanol oxidation in the studied system is supposed to be dependent on the concentra-tion of OH.Note that the oxidation may proceed via step four (Eq.(4))in the presence of high pressure O 2,which is so-called ‘‘wet oxidation’’and usually occurs at air pressures (1–22MPa)and at high temperatures (423–643K)[15].However,it is unlikely to happen in the present reaction conditions.According to Wolfenden’s study [34],we envisaged that the complete oxidation of ethanol may proceed through a route like Eq.(10):C 2H 5OH þ OH À!ÀH 2OC 2H 4O À! OHCO 2þH 2O(10)Whereby,it is believed that organic radicals containing hydroxy-groups a and b to carbon radicals centre can eliminate water to form oxidizing species.With the degrading of organic intermediates step by step as the way described in Eq.(10),the final products should be CO 2and H 2O.However,no other species but ethanol was detected by GC and GC–MS in the present study possibly due to the rapid of the reaction that leads to unstable intermediate.Fig.5indicates that a proper ratio of catalyst/solution is a necessary factor to attain the high conversion of ethanol.It can be understood that over exposure of H 2O 2to catalyst will increase the rate of H 2O 2decomposition;but on the other hand,more OH radical produced may be scavenged by catalyst with increasing the amount of catalyst and transformed into O 2and H 2O as expressed in Eq.(3),instead of participating the oxidation reaction.In terms of Eq.(10),stoichiometric ethanol/H 2O 2should be 1/6for the complete oxidation of ethanol;however,in the present system the total molar ratio is 1/400.In other words,most intermediates were extinguished through scavenging during reaction.This may explain well that the decrease of reaction rate with the rise of ratio of catalyst/solution in the system.The same reason may also explain the decrease of reaction rate with prolonging the time.Actually,H 2O 2decomposition (ca.90%)may be completed within a few minutes over the Mn 3O 4/SBA-15catalyst as illustrated in Fig.10,irrespective of amount of catalyst (not shown for the sake of brevity);in contrast,the rate for H 2O 2decomposition became dawdling for Fe 2+catalyst.As a result,presumably,the homogenous system has relatively high concentration ofradicals.It may explain the superior reactivity of the conventional Fenton reagent to the presented system as depicted in Fig.9.Therefore,how to reduce scavenging,especially in the heterogeneous Fenton system [29],is crucial for enhancing the reaction rate.C 2H 5OH þ6H 2O 2!2CO 2þ9H 2O(11)On the other hand,as illustrated by Eqs.(1)–(4),all steps in the oxidation process are affected by the reaction temperature.Fig.6demonstrates that increasing temperature remarkably boosts the reactivity of ethanol oxidation in the system of Mn 3O 4/SBA-15–H 2O 2possibly,due to the improvement of the reactions in Eqs.(2)and (4)at elevated temperatures.In terms of Eqs.(6)and (7),acidic conditions may delay the H 2O 2decomposition but enhance the formation of OH (Eqs.(5),(8)and (9)).This ‘‘delay’’is supposed to reduce the chance of the scavenging of radicals and improve the efficiency of H 2O 2in the reaction.The protons are believed to have capability for stabilizing H 2O 2,which has been elucidated well previously [18,19].Consequently,it is understandable that the reaction is favored in the strong acidic environment.Fig.7shows a maximum reactivity at pH of 2.0and the lowest at pH of 9.0.As depicted in Fig.8,the reaction rate for ethanol oxidation is proportional to the concentration of ethanol in the range of 50–500ppm.It suggests that at low concentration of ethanol (100ppm)most of the radicals might not take part in the reaction before scavenged by catalyst.With increasing the ethanol concentration,the possibility of the collision between ethanol and radicals can be increased significantly.As a result,the rate of scavenging radicals is reduced relatively.Thus,it is reasonable for the faster rate observed at higher concentration of ethanol.Finally,it is noteworthy that as compared to the bulk Mn 3O 4(Aldrich,98.0%of purity),the reactivity of the nano-crystalline Mn 3O 4on SBA-15is increased by factor of 20under the same typical reaction conditions.Obviously,Mn 3O 4nanocrystal is an effective alternative for this catalytic system.The present study has evidenced that the unique structure of SBA-15can act as a special ‘‘nanoreactor’’for synthesizing Mn 3O 4nanocrystals.Interestingly,a latest study has revealed that iron oxide nanoparticles could be immobilized on alumina coated SBA-15,which also showed excellent performance as a Fenton catalyst [35].However,the role of the pore structure of SBA-15in this reaction is still unclear.We do expect that during reaction SBA-15may have additional function to trap larger organic molecules by adsorption.Thus,it may broaden its application in this field.So,relevant study on the structure of nano-composites of various MnO x and its role in the Fenton-like reaction for remediation of organic compounds in aqueous solution is undergoing in our group.4.ConclusionsIn the present study,we have addressed a new catalytic system suitable for remediation of trivial organic compound from contaminated water through a Fenton-like reaction withY.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234233。

专利名称：METHOD AND PLANT FOR RECOVERY OF THE HEAVIER HYDROCARBONS OF A GASMIXTURE发明人：GAUTHIER, PIERRE申请号：AU3787285申请日：19841226公开号：AU3787285A公开日：19850730专利内容由知识产权出版社提供摘要：PCT No. PCT/FR84/00303 Sec. 371 Date Aug. 14, 1985 Sec. 102(e) Date Aug. 14, 1985 PCT Filed Dec. 26, 1984 PCT Pub. No. WO85/03072 PCT Pub. Date Jul. 18, 1985.The gas to be treated is cooled and partly condensed in two steps (3-4, 5-6), and the condensed fractions are distilled in a column (9). A part of the condensed liquid at the top of the column is subcooled in the second exchanger 5, expanded, and vaporized in counter-current to the gas to be treated. This permits the obtainment of cooling temperatures on the order of -80 DEG C. by means of a refrigerating unit (13, 20) operating at a temperature on the order of -30 DEG to -40 DEG C. Application to the recovery of a mixture of propane, butane and pentane from a petroleum refinery residual gas.申请人：L'AIR LIQUIDE, SOCIETE ANONYME POUR L'ETUDE ET L'EXPLOITATION,DES PROCEDES GEORGES CLAUDE更多信息请下载全文后查看。

Package‘perfectphyloR’October14,2022Type PackageTitle Reconstruct Perfect Phylogenies from DNA Sequence DataVersion0.2.1Date2021-02-28Author Charith Karunarathna and Jinko GrahamMaintainer Charith Karunarathna<***************>Description Reconstructs perfect phylogeny at a user-given focal point and to depict and test associa-tion in a genomic region based on the reconstructed parti-tions.Charith B Karunarathna and Jinko Graham(2019)<bioRxiv:10.1101/674523>. Depends R(>=3.4.0)License GNU General Public LicenseImports ape,phytools,Rcpp(>=0.12.16)LinkingTo Rcpp,RcppArmadilloRoxygenNote7.1.1Suggests HHG,dendextend,vcfR,R.rspVignetteBuilder R.rspNeedsCompilation yesRepository CRANDate/Publication2021-03-0805:30:02UTCR topics documented:perfectphyloR-package (2)createHapMat (2)dCorTest (3)ex_hapMatSmall_data (4)ex_hapMat_data (5)HHGtest (5)MantelTest (6)phenoDist (7)12createHapMat plotDend (7)RandIndexTest (8)rdistMatrix (9)reconstructPP (10)reconstructPPregion (12)RVtest (13)tdend (14)testAssoDist (14)testDendAssoRI (15)vcftohapMat (16)Index18 perfectphyloR-package Reconstruct perfect phylogenies from DNA sequence dataDescriptionFunctions to reconstruct perfect phylogeny underlying a sample of DNA sequences,at a focal single-nucleotide variant(SNV)and to depict and test association in a genomic region based on the reconstructed partitions.Author(s)Charith Karunarathna and Jinko GrahamcreateHapMat Create an object of class hapMatDescriptionThis function creates a hapMat data object,a required input for reconstructPP.UsagecreateHapMat(hapmat,snvNames,hapNames,posns)Argumentshapmat A matrix of0’s and1’s,with rows representing haplotypes and columns repre-senting single-nucleotide variants(SNVs).snvNames A vector of names of SNVs for the columns of hapmat.hapNames A vector of names of haplotypes for the rows of hapmat.posns A numeric vector specifying the genomic positions(e.g.in base pairs)of SNVs in the columns of hapmat.dCorTest3ValueAn object of class hapMat.Exampleshapmat=matrix(c(1,1,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,0,0,1,0,0,1,1,0,0,1),byrow=TRUE,ncol=4)snvnames=c(paste("SNV",1:4,sep=""))allhaps=c("h1","h2","h3","h4","h5","h6","h7")#Physical positionsposns=c(1000,2000,3000,4000)#Create hapMat data objectex_hapMat<-createHapMat(hapmat=hapmat,snvNames=snvnames,hapNames=allhaps,posns=posns)dCorTest dCor test for similarity of two matricesDescriptionThis function performs dCor test for association between two distance matrices and computes per-mutation P value.Permutation P value is computed by randomly permuting rows and columns of the second distance matrix.UsagedCorTest(Dx,Dy,nperm)ArgumentsDx A numeric matrix of pairwise distances.Dy A second numeric matrix of pairwise distances.nperm The number of times to permute the rows and columns of Dy.ValueA list contains RV coefficient and permutation P value.4ex_hapMatSmall_data ReferencesG.J.Szekely,M.L.Rizzo,and N.K.Bakirov.(2007).Measuring and testing dependence bycorrelation of distances.The Annals of Statistics,35(6):2769-2794.Examplesx<-runif(8)y<-runif(8)#Distance matricesdistX=as.matrix(dist(x,upper=TRUE,diag=TRUE))distY=as.matrix(dist(y,upper=TRUE,diag=TRUE))dCorTest(Dx=distX,Dy=distY,nperm=1000)ex_hapMatSmall_data Example small datasetDescriptionA subset of ex_hapMat_data,containing10sequences(haplotypes)with20SNVs.Usagedata(ex_hapMatSmall_data)FormatA list of ten haplotypes with the physical positions of each SNV.hapmat A matrix of0’s and1’s,with rows representing haplotypes and columns representing SNVs.snvNames A vector of names of SNVs for the columns of hapmat.hapNames A vector of names of haplotypes for the rows of hapmat.posns a numeric vector specifying the genomic positions(e.g.in base pairs)of SNVs in the columns of hapmat.ex_hapMat_data5 ex_hapMat_data Example datasetDescriptionA hapMat data object containing200sequences(haplotypes)with2747SNVs.Usagedata(ex_hapMat_data)FormatA list of200haplotypes with the physical positions of each SNV.hapmat A matrix of0’s and1’s,with rows representing haplotypes and columns representing SNVs.snvNames A vector of names of SNVs for the columns of hapmat.hapNames A vector of names of haplotypes for the rows of hapmat.posns A numeric vector specifying the genomic positions(e.g.in base pairs)of SNVs in the columns of hapmat.HHGtest HHG test for association of two distance matricesDescriptionThis function performs HHG test tofind the association between two distance matrices.It permutes rows and columns of the second matrix randomly to calculate P value.UsageHHGtest(Dx,Dy,nperm)ArgumentsDx A numeric matrix of pairwise distances.Dy A second numeric matrix of pairwise distances.nperm The number of times to permute the rows and columns of Dy.ValueA list contains HHG coefficient and permutation P value.6MantelTest ReferencesBarak,B.,and Shachar,K.,based in part on an earlier implementation by Ruth Heller and Yair Heller.(2017).HHG:Heller-Heller-Gorfine Tests of Independence and Equality of Distributions.R package version2.2.https:///package=HHGExamplesx<-runif(8)y<-runif(8)#Distance matricesdistX=as.matrix(dist(x,upper=TRUE,diag=TRUE))distY=as.matrix(dist(y,upper=TRUE,diag=TRUE))HHGtest(Dx=distX,Dy=distY,nperm=1000)MantelTest Mantel test for association of two distance matircesDescriptionThis function performs Mantel test for correlation between two distance matrices.It computes P value by randomly permuting rows and columns of the second matrix.UsageMantelTest(Dx,Dy,nperm)ArgumentsDx A numeric matrix of pairwise distances.Dy A second numeric matrix of pairwise distances.nperm The number of times to permute the rows and columns of Dy.ValueA list contains Mantel statistic and permutation P value.ReferencesMantel,N.(1967)The detection of disease clustering and a generalized regression approach.Can-cer Research,27,209-220.phenoDist7 Examplesx<-runif(8)y<-runif(8)#Distance matricesdistX=as.matrix(dist(x,upper=TRUE,diag=TRUE))distY=as.matrix(dist(y,upper=TRUE,diag=TRUE))MantelTest(Dx=distX,Dy=distY,nperm=1000)phenoDist Phenotypic distancesDescriptionThis is the pairwise phenotypic distances described in Karunarathna and Graham(2018).Usagedata(phenoDist)FormatAn object of class matrix.ReferencesKarunarathna,C.B.,and Graham,J.(2018)Using gene genealogies to localize rare variants asso-ciated with complex traits in diploid populations.Human heredity,83(1),30-39.plotDend Plot reconstructed dendrogramDescriptionThis function plots reconstructed dendrogram in a genomic region.UsageplotDend(dend,direction="downwards")8RandIndexTestArgumentsdend An object of class phylo or of class multiPhylo returned from reconstructPP or reconstructPPregion.direction A character string specifying the direction of the dendrogram.Four values are possible:"downwards"(the default),"upwards","leftwards"and"rightwards". Examplesdata(ex_hapMat_data)ex_dend<-reconstructPP(hapMat=ex_hapMat_data,focalSNV=3,minWindow=1,sep="-")plotDend(dend=ex_dend,direction="downwards")RandIndexTest Rand Index TestDescriptionThis function performs Rand index test for association between two phylo objects.UsageRandIndexTest(dend1,dend2,k=2,nperm)Argumentsdend1An object of type phylo.dend2A second object of type phylo.k An integer that specifies the number of clusters that the dendrogram should be cut into.The default is k=2.Clusters are defined by starting from the root ofthe dendrogram and cutting across.nperm The number of times to permute tips of the dend2.ValueA numeric value between0and1and permutation P value.ReferencesRand,W.M.(1971)Objective criteria for the evaluation of clustering methods.Journal of the Amer-ican Statistical Association66:846-850.rdistMatrix9 Examplesdata(ex_hapMat_data)d1<-reconstructPP(ex_hapMat_data,focalSNV=1,minWindow=1)d2<-reconstructPP(ex_hapMat_data,focalSNV=5,minWindow=1)RandIndexTest(dend1=d1,dend2=d2,k=5,nperm=100)rdistMatrix Rank-based distances between haplotypes in a given partitionDescriptionThis function computes the pairwise distances between haplotypes(tips)of the dendrogram based on the ranking of the nested partitions in the dendrogram.See the details.UsagerdistMatrix(dend,sep="-")Argumentsdend A list of nodes that represents the nested partition of haplotypes.sep A character string separator for concatenating haplotype labels in the dendro-gram if they are undistingushable in the window around the focal SNV.See thearguments in reconstructPP.DetailsWe code the distance between two haplotypes of a dendrogram as the number of inner nodes that seperate the haplotypes plus one.That is,we assign the distance between two internal neighbouring nodes as one,and the distance between an internal node and its neighbouring tip as one.To illustrate, consider the followingfigure of a dendrogram.In thefigure,the distance between the haplotypes 2931and454is3;the distance between other haplotypes are given in the table below.10reconstructPPValueA matrix of pairwise distances between haplotypes.Examplesdata(ex_hapMat_data)rdend<-reconstructPP(hapMat=ex_hapMat_data,focalSNV=2,minWindow=1,sep="-") rdistMatrix(rdend)reconstructPP Reconstruct the perfect phylogeny at a given focal SNVDescriptionThis function reconstructs the perfect phylogeny at a given focal SNV using the recursive parti-tioning algorithm of Gusfield(1991)on compatible SNVs,and the modification of Mailund et al.(2006)to include incompatible SNVs that are nearby.UsagereconstructPP(hapMat,focalSNV,minWindow=1,sep="-")ArgumentshapMat A data structure of class hapMat.Eg:created by the createHapMat function.focalSNV The column number of the focal SNV at which to reconstruct the reconstructed partitions.reconstructPP11 minWindow Minimum number of SNVs around the focal SNV in the window of SNVs usedto reconstruct the partitions(default is the maximum of one and2%of the totalnumber of the SNVs).sep Character string separator to separate haplotype names for haplotypes that cannot be distingushed in the window around the focal point.For example,if a tipis comprised of haplotypes"h1"and"h3",and sep="-",then the tip label willbe"h1-h3".The default value is"-".See details.DetailsTo reconstruct the perfect phylogeny from sequence data,these two steps are followed:(1)Selecta window of SNVs at a given focal SNV.(2)Build the perfect phylogey for the window of SNVs.More details can be found in the references.The followingfigure shows the reconstructed partitions at the tenth SNV position of ex_hapMatSmall_data.ValueAn object of class phylo with indices of the column boundaries of the hapMat object that were usedto reconstruct the partition in the window of SNVs.ReferencesGusfield,D.(1991)Efficient algorithms for inferring evolutionary works,21(1),19-28.Mailund,T.,Besenbacher,S.,and Schierup,M.H.(2006)Whole genome association mapping byincompatibilities and local perfect phylogenies.BMC Bioinformatics,7(1),454.Examplesdata(ex_hapMatSmall_data)rdend<-reconstructPP(hapMat=ex_hapMatSmall_data,focalSNV=10,minWindow=1,sep="-")12reconstructPPregion#Plot the reconstructed perfect phylogeney.plotDend(rdend,direction="down")#Extract the positions of the lower and upper limits of a window of SNVs in hapMat object #to reconstruct the partition,rdend.ex_hapMatSmall_data$posns[rdend$snvWinIndices]reconstructPPregion Reconstruct perfect phylogeny sequencce across a regionDescriptionThis function reconstructs perfect phylogenies on each possible focal SNV across a genomic region. UsagereconstructPPregion(hapMat,minWindow,posn.lb=NULL,posn.ub=NULL) ArgumentshapMat A data structure of class hapMat.See the arguments in reconstructPP.minWindow Minimum number of SNVs around the focal SNV in the window of SNVs used to reconstruct the perfect phylogeny.posn.lb Lower bound of the subregion of hapMat(in base pairs)within which to consider SNVs.posn.ub Upper bound of the subregion of hapMat(in base pairs)within which to consider SNVs.ValueAn object of class multiPhylo that contains multiple phylo objects.Examplesdata(ex_hapMatSmall_data)#Reconstruct partitions across the region of ex_hapMatSmall_data.rdends<-reconstructPPregion(hapMat=ex_hapMatSmall_data,minWindow=1)#Reconstruct partitions between a given range SNV positions.rdends_range<-reconstructPPregion(hapMat=ex_hapMatSmall_data,minWindow=1,posn.lb=2000,posn.ub=7000)RVtest13 RVtest RV test for association of two distance matricesDescriptionThis function performs RV test for similarity of two distance matrices.It permutes rows and columns of the second matrix randomly to calculate P value.UsageRVtest(Dx,Dy,nperm)ArgumentsDx A numeric matrix of pairwise distances.Dy A second numeric matrix of pairwise distances.nperm The number of times to permute the rows and columns of Dy.ValueA list contains RV coefficient and permutation P value.ReferencesRobert,P.and Escoufier,Y.(1976)A Unifying tool for linear multivariate statistical methods:the RV-coefficient.Applied Statistics,V ol.25,No.3,p.257-265.Examplesx<-runif(8)y<-runif(8)#Distance matricesdistX=as.matrix(dist(x,upper=TRUE,diag=TRUE))distY=as.matrix(dist(y,upper=TRUE,diag=TRUE))RVtest(Dx=distX,Dy=distY,nperm=1000)14testAssoDist tdend True dendrogram objectDescriptionA phylo object containing attributes of the comparator true dendrogram for the example data atSNV position975kilo base pairs.Usagedata(tdend)FormatA phylo object from the ape package containing four attributes:edge A matrix containing the node labels and their child nodes.Nnode The number of nodes.bel A character vector containing the haplotype labels of the true dendrogram.edge.length A numeric vector giving the lengths of the branches given by edge.testAssoDist Test the association between a comparator distance matrix,and thereconstructed dendrograms across a genomic regionDescriptionThis function calculates and tests the association between a comparator distance matrix,based on any pairwise distance measure,and the reconstructed dendrograms across a genomic region of interest usingassociation measures such as the dCor statistic,HHG statistic,Mantel statistic,and RV coefficient.See the section Applications in vignette("perfectphyloR")for the detailed example.UsagetestAssoDist(rdend,cdmat,method,hapMat,nperm=0,xlab="",ylab="",main="")testDendAssoRI15Argumentsrdend A multiPhylo object of reconstructed dendrograms at each focal SNV.cdmat A comparator matrix of pairwise distances(e.g.pairwise distances between haplotypes of a comparator dendrogram).method Association e"dCor"for dCor test,"HHG"for HHG test,"Mantel"for mantel test,and"RV"for RV test.hapMat An object of class hapMat containing SNV haplotypes.nperm Number of permutations for the test of any association across the genomic re-gion of interest.The default is nperm=0;i.e.,association will not be tested.xlab An optional character string for the label on the x-axis in the plot that is returned (none by default).ylab An optional character string for the label on the y-axis in the plot that is returned (none by default).main An optional character string for title in the plot that is returned(none by default). ValueA list with the following components:Stats A vector of observed statistics computed from the user-provided distance asso-ciation method.OmPval A permutation-based omnibus P value for the test of any association across the genomic region using the maximum statistic over the genomic region as the teststatistic.mPval A vector of marginal P values at each SNV position.plt A plot of the association profile over SNV locations in the region of interest. See AlsoHHGtest,dCorTest,RVtest,MantelTesttestDendAssoRI Tests Rand Index between a comparator dendrogram and recon-structed dendrogramsDescriptionThis function performs the Rand Index between a user-supplied comparator dendrogram and the reconstruced dendrograms at each focal SNV position in a genomic region.See the section Appli-cations in vignette("perfectphyloR")for the detailed example.UsagetestDendAssoRI(rdend,cdend,hapMat,k=2,nperm=0,xlab="",ylab="",main="")Argumentsrdend A multiPhylo object of reconstructed dendrograms at each focal SNV.cdend A phylo object of the comparator dendrogram.hapMat An object of class‘hapMat‘containing SNV haplotypes.k An integer that specifies the number of clusters that the dendrogram should be cut into.The default is k=2.Clusters are defined by starting from the root of thedendrogram and moving towards the tips,cutting horizontally at any given pointin the dendrogram.nperm Number of permutations for the test of any association across the genomic re-gion of interest.The default is’nperm=0’;i.e.,association will not be tested.xlab An optional character string for the label on the x-axis in the plot that is returned (none by default).ylab An optional character string for the label on the y-axis in the plot that is returned (none by default).main An optional character string for title in the plot that is returned(none by default).ValueA list with the following components:Stats A vector of observed Rand indices.OmPval A permutation-based omnibus P value for the test of any association across the genomic region using the maximum Rand index over the genomic region as thetest statistics.mPval A vector of marginal P values at each SNV position.plt A plot of the association profile of Rand indices over SNV locations in the region of interest.vcftohapMat Create a hapMat object from variant call format(vcf)file.DescriptionThis function creates a hapMat object from variant call format(vcf)file.UsagevcftohapMat(vcf_file_path)Argumentsvcf_file_path File path to the vcffile.ValueAn object of class hapMat.Examples##Not run:#Specify the file path.vcf_file_path<-"C:/vcfData/vcfData.vcf.gz"#Create a hapMat object from the vcf file.ex_vcf_hapMat<-vcftohapMat(vcf_file_path)##End(Not run)Index∗datasetsex_hapMat_data,5ex_hapMatSmall_data,4phenoDist,7tdend,14createHapMat,2,10dCorTest,3,15ex_hapMat_data,5ex_hapMatSmall_data,4HHGtest,5,15MantelTest,6,15perfectphyloR(perfectphyloR-package),2 perfectphyloR-package,2phenoDist,7plotDend,7RandIndexTest,8rdistMatrix,9reconstructPP,2,9,10,12 reconstructPPregion,12RVtest,13,15tdend,14testAssoDist,14testDendAssoRI,15vcftohapMat,1618。

python的海豹运算符
海豹运算符是Python 3.8版本引入的一种新的语法结构，它使
用符号"，="表示。

海豹运算符的作用是在表达式中给变量赋值，并
且同时返回赋的值。

这个特性在一些特定的情况下可以使代码更加
简洁和易读。

海豹运算符的语法是这样的，变量名 := 表达式。

它的作用相
当于先计算表达式的值，然后将这个值赋给变量。

这在一些需要重
复使用同一个表达式的情况下非常有用，因为它可以避免重复计算
同一个表达式，提高了代码的执行效率。

另外，海豹运算符还可以在一些特定的场景下提高代码的可读性。

比如在使用while循环时，可以使用海豹运算符来初始化一个
变量，使得这个变量的作用范围局限在while循环内部，不会影响
到外部的同名变量。

这样可以减少变量污染和提高代码的可维护性。

需要注意的是，海豹运算符并不是Python中的新的赋值方式，
它只是在某些情况下可以替代常规的赋值语句，使得代码更加简洁
和易读。

在使用海豹运算符时，需要注意避免滥用，以免降低代码
的可读性。

总的来说，海豹运算符是Python语言的一个有趣的特性，可以在一定程度上提高代码的简洁性和可读性。

opt forOpt for: Making Smart Choices for a Better FutureIntroductionIn today's fast-paced world, we are constantly bombarded with an overwhelming number of options in every aspect of our lives. From the products we buy to the decisions we make, our choices have a significant impact on our present and future. That is why it is crucial to opt for smart choices that align with our values, goals, and long-term sustainability. This article explores the importance of making informed decisions and provides practical tips on how to opt for a better future.Section 1: Understanding the Impact of Our ChoicesThe choices we make on a daily basis have far-reaching consequences, not only for ourselves but also for the environment and society as a whole. It is important to recognize that our decisions can either contribute to the problems we face or offer solutions. By opting for smart choices, we can positively influence our lives and make a meaningful difference.Section 2: Opting for Sustainable ConsumptionSustainable consumption is a concept that encourages individuals to consider the environmental and social impact of their purchasing decisions. Opting for sustainable products and services means favoring those that are ethically sourced, produced, and distributed. This can involve choosing organic and locally grown food, supporting fair-trade practices, and purchasing eco-friendly products.Section 3: Opting for Renewable EnergyOne of the most significant steps we can take towards a better future is to opt for renewable energy sources. By choosing to power our homes and businesses with clean energy, such as solar or wind power, we can reduce our carbon footprint and contribute to the fight against climate change. Additionally, opting for renewable energy can save money in the long run and increase energy independence.Section 4: Opting for Sustainable TransportationTransportation is a major source of greenhouse gas emissions and air pollution. To mitigate these impacts, it is crucial to opt for sustainable transportation options. This could include using public transportation, carpooling, cycling, or walking whenever possible. For longer distances, opting for electric vehicles or hybrid cars can significantly reduce carbon emissions.Section 5: Opting for Responsible InvestingInvestment choices can have a significant impact on the world around us. By opting for responsible investing, individuals can encourage companies to prioritize environmental and social responsibility. This can involve investing in companies that promote renewable energy, practice fair labor standards, and prioritize sustainability. By aligning our investments with our values, we can actively support positive change.Section 6: Opting for Education and AwarenessKnowledge and awareness are essential tools for making informed choices. By investing in education and staying informed about the issues that matter to us, we can make more conscious decisions in all aspects of our lives. This mayinvolve attending workshops or conferences, reading books and articles, or engaging in discussions with like-minded individuals. By opting for education and awareness, we empower ourselves to be agents of change.ConclusionIn conclusion, the choices we make today have a profound impact on our future and the world around us. By opting for smart choices that promote sustainability, we can create a better world for ourselves and future generations. Whether it's opting for sustainable consumption, renewable energy, responsible investing, or education and awareness, each choice matters. Let us all opt for a better future by making conscious decisions and reshaping our world for the better.。