Zero-curvature condition in two dimensions. Relativistic particle models and finite W-trans

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Collagen-structure, function, and biosynthesis

Collagen-structure, function, and biosynthesis

Collagens—structure,function,and biosynthesisK.Gelse a ,E.Po ¨schl b ,T.Aigner a,*aCartilage Research,Department of Pathology,University of Erlangen-Nu ¨rnberg,Krankenhausstr.8-10,D-91054Erlangen,GermanybDepartment of Experimental Medicine I,University of Erlangen-Nu ¨rnberg,91054Erlangen,GermanyReceived 20January 2003;accepted 26August 2003AbstractTherepresents a complex alloy of variable members of diverse protein families defining structuralintegrity and various physiological functions.The most abundant family is the collagens with more than 20different collagen types identified so far.Collagens are centrally involved in the formation of fibrillar and microfibrillar networks of the extracellular matrix,basement membranes as well as other structures of the extracellular matrix.This review focuses on the distribution and function of various collagen types in different tissues.It introduces their basic structural subunits and points out major steps in the biosynthesis and supramolecular processing of fibrillar collagens as prototypical members of this protein family.A final outlook indicates the importance of different collagen types not only for the understanding of collagen-related diseases,but also as a basis for the therapeutical use of members of this protein family discussed in other chapters of this issue.D 2003Elsevier B.V .All rights reserved.Keywords:Collagen;Extracellular matrix;Fibrillogenesis;Connective tissueContents1.Collagens—general introduction .............................................15322.Collagens—the basic structural module..........................................15323.Distribution,structure,and function of different collagen types...............................15353.1.Collagen types I,II,III,V and XI—the fibril-forming collagens ...........................15353.2.Collagen types IX,XII,and XIV—The FACIT collagens...............................15373.3.Collagen type VI—a microfibrillar collagen.....................................15383.4.Collagen types X and VIII—short chain collagens..................................15383.5.Collagen type IV—the collagen of basement membranes...............................15384.Biosynthesis of collagens ................................................15404.1.Transcription and translation ............................................15404.2.Posttranslational modifications of collagen .....................................15404.3.Secretion of collagens...............................................15414.4.Extracellular processing and modification ......................................15410169-409X/$-see front matter D 2003Elsevier B.V .All rights reserved.doi:10.1016/j.addr.2003.08.002*Corresponding author.Tel.:+49-9131-8522857;fax:+49-9131-8524745.E-mail address:thomas.aigner@patho.imed.uni-erlangen.de (T.Aigner)/locate/addrAdvanced Drug Delivery Reviews 55(2003)1531–15465.Functions of collagens beyond biomechanics ......................................15426.Perspectives .....................................................1542Acknowledgements....................................................1543References........................................................1543gens were once considered to be a group of proteinswith a characteristic molecular structure with theirfibrillar structures contributing to the extracellularscaffolding.Thus,collagens are the major structuralelement of all connective tissues and are also foundin the interstitial tissue of virtually all parenchymal 2.Collagens—the basic structural module The name ‘‘collagen’’is used as a generic term for proteins forming a characteristic triple helix of three polypeptide chains and all members of the collagen family form these the K.Gelse et al./Advanced Drug Delivery Reviews 55(2003)1531–15461532Table 1Table showing the various collagen types as they belong to the major collagen families TypeMolecular compositionGenes (genomic localization)Tissue distribution Fibril-forming collagens I [a 1(I)]2a 2(I)COL1A1(17q21.31–q22)bone,COL1A2(7q22.1)II[a 1(II)]3COL2A1(12q13.11–q13.2)III [a 1(III)]3COL3A1(2q31)Va 1(V),a 2(V),a 3(V)COL5A1(9q34.2–q34.3)COL5A2(2q31)COL5A3(19p13.2)XI a 1(XI)a 2(XI)a 3(XI)COL11A1(1p21)COL11A2(6p21.3)COL11A3=COL2A1Basement membrane collagens IV [a 1(IV)]2a 2(IV);a 1–a 6COL4A1(13q34)basement membranesCOL4A2(13q34)COL4A3(2q36–q37)COL4A4(2q36–q37)COL4A5(Xq22.3)COL4A6(Xp22.3)Microfibrillar collagen VI a 1(VI),a 2(VI),a 3(VI)COL6A1(21q22.3)widespread:dermis,cartilage,placenta,lungs,vessel wall,COL6A2(21q22.3)intervertebral discCOL6A3(2q37)Anchoring fibrils VII [a 1(VII)]3COL7A1(3p21.3)skin,dermal–epidermal junctions;oral mucosa,cervix,Hexagonal network-forming collagens VIII [a 1(VIII)]2a 2(VIII)COL8A1(3q12–q13.1)endothelial cells,Descemet’s membrane COL8A2(1p34.3–p32.3)X [a 3(X)]3COL10A1(6q21–q22.3)hypertrophic cartilageF ACIT collagens IX a 1(IX)a 2(IX)a 3(IX)COL9A1(6q13)cartilage,vitreous humor,corneaCOL9A2(1p33–p32.2)XII [a 1(XII)]3COL12A1(6q12–q13)perichondrium,ligaments,tendonXIV [a 1(XIV)]3COL9A1(8q23)dermis,tendon,vessel wall,placenta,lungs,liver XIX [a 1(XIX)]3COL19A1(6q12–q14)human rhabdomyosarcomaXX [a 1(XX)]3corneal epithelium,embryonic skin,sternal cartilage,tendon XXI[a 1(XXI)]3COL21A1(6p12.3–11.2)blood vessel wallTransmembrane collagens XIII [a 1(XIII)]3COL13A1(10q22)epidermis,hair follicle,endomysium,intestine,chondrocytes,lungs,liver XVII [a 1(XVII)]3COL17A1(10q24.3)dermal–epidermal junctionsMultiplexins XV [a 1(XV)]3COL15A1(9q21–q22)fibroblasts,smooth muscle cells,kidney,pancreas,XVI [a 1(XVI)]3COL16A1(1p34)fibroblasts,amnion,keratinocytes XVIII [a 1(XVIII)]3COL18A1(21q22.3)lungs,liverGiven are the molecular composition,the genomic localization of the different chains as well as the basic tissue distribution.K.Gelse et al./Advanced Drug Delivery Reviews 55(2003)1531–15461533extracellular matrix although their size,function and tissue distribution vary considerably.So far,26ge-netically distinct collagen types have been described [4,7–11].Based on their structure and supramolecular orga-nization,they can be grouped into fibril-forming collagens,fibril-associated collagens (FACIT),net-work-forming collagens,anchoring fibrils,transmem-brane collagens,basement membrane collagens and others with unique functions (see Table 1).The different collagen types are characterized by considerable complexity and diversity in their struc-ture,their splice variants,the presence of additional,non-helical domains,their assembly and their func-tion.The most abundant and widespread family of collagens with about 90%of the total collagen is represented by the fibril-forming collagens.Types Itissues [4,12,13].Type IV collagens with a more flexible triple helix assemble into meshworks restrict-ed to basement membranes.The microfibrillar type VI collagen is highly disulfide cross-linked and contrib-utes to a network of beaded filaments interwoven with other collagen fibrils [14].F ibril-a ssociated c ollagens with i nterrupted t riplehelices (FACIT)such as types IX,XII,and XIV collagens associate as single mol-ecules with large collagen fibrils and presumably play a role in regulating the diameter of collagen fibrils [9].Types VIII and X collagens form hexagonal networks while others (XIII and XVII)even span cell membranes [15].Despite the rather high structural diversity among the different collagen types,all members of the collagen family have one characteristic feature:a right-handed triple helix composed of three a -chains (Fig.1)[7,16].These might be formed by three identical chains (homotrimers)as in collagens II,III,VII,VIII,X,and others or by two or more different chains (heterotrimers)as in collagen types I,IV ,V ,VI,IX,and XI.Each of the three a -chains within the molecule forms an extended left-handed helix with a pitch of 18amino acids per turn [17].The three chains,staggered by one residue relative to each other,are supercoiled around a central axis in a right-handed manner to form the triple helix [18].A structural prerequisite for the assembly into a triple helix is a glycine residue,the smallest amino acid,in every third position of the polypeptide chains resulting in a (Gly-X-Y)n repeat structure which characterizes the ‘‘col-lagenous’’domains of all collagens.The a -chains assemble around a central axis in a way that all glycine residues are positioned in the center of the triple helix,while the more bulky side chains of the other amino acids occupy the outer positions.This allows a close packaging along the central axis of the molecule.The X and Y position is often occupied by proline and hydroxyproline.Depending on the colla-gen type,specific proline and lysine residues areFig.1.Molecular structure of fibrillar collagens with the various subdomains as well as the cleavage sites for N-and C-procollagenases (shown is the type I collagen molecule).Whereas they are arranged in tendon in a parallel manner they show a rather network-like supramolecular arrangement in articular cartilage.K.Gelse et al./Advanced Drug Delivery Reviews 55(2003)1531–15461534modified by post-translational enzymatic hydroxyl-ation.The content of4-hydroxyproline is essential for the formation of intramolecular hydrogen bonds and contributes to the stability of the triple helical conformation.Some of the hydroxylysines are further modified by glycosylation.The length of the triple helical part varies considerably between different collagen types.The helix-forming(Gly-X-Y)repeat is the predominating motif in fibril-forming collagens (I,II,III)resulting in triple helical domains of300nm in length which corresponds to about1000amino acids[3,4].In other collagen types,these collagenous domains are much shorter or contain non-triple helical interruptions.Thus,collagen VI or X contains triple helices with about200or460amino acids,respec-tively[4].Although the triple helix is a key feature of all collagens and represents the major part in fibril-forming collagens,non-collagenous domains flanking the central helical part are also important structural components(Fig.1).Thus,the C-propeptide is thought to play a fundamental role in the initiation of triple helix formation,whereas the N-propeptide is thought to be involved in the regulation of primary fibril diameters[3].The short non-helical telopeptides of the processed collagen monomers(see Fig.1)are involved in the covalent cross-linking of the collagen molecules as well as linking to other molecular structures of the surrounding matrix[38].FACIT collagens are characterized by several non-collagenous domains interrupting the triple he-lices,which may function as hinge regions[19].In other collagens like collagens IV,VI,VII,VIII or X,non-collagenous domains are involved in net-work formation and aggregation.In contrast to the highly conserved structure of the triple helix,non-collagenous domains are characterized by a more structural and functional diversity among different collagen families and types.Interruptions of the triple helical structure may cause intramolecular flexibility and allow specific proteolytic cleavage. Native triple helices are characterized by their resistance to proteases such as pepsin,trypsin or chymotrypsin[20]and can only be degraded by different types of specific collagenases.Collagenase A(MMP-1)[21],the interstitial collagenase,is expressed by a large variety of cells and is thought to be centrally involved in tissue remodeling, e.g. during wound healing.MMP-8(collagenase B)is largely specific for neutrophil granulocytes[22]and, thus,thought to be mainly involved in tissue destruction during acute inflammatory processes. MMP-13(collagenase C)[23]is expressed by hypertrophic chondrocytes as well as osteoblasts and osteoclasts[24]and therefore most likely plays an important role in cartilage and bone remodeling. Many other matrix metalloproteinases are able to cleave the denatured collagen(‘‘gelatin’’).The de-tailed analysis of the interplay of MMPs as well as specific inhibitors will describe the reactivities in vivo as well as potential pharmaceutical options for intervention[25–27].3.Distribution,structure,and function of different collagen types3.1.Collagen types I,II,III,V and XI—the fibril-forming collagensThe classical fibril-forming collagens include col-lagen types I,II,III,V,and XI.These collagens are characterized by their ability to assemble into highly orientated supramolecular aggregates with a charac-teristic suprastructure,the typical quarter-staggered fibril-array with diameters between25and400nm (Fig.2).In the electron microscope,the fibrils are defined by a characteristic banding pattern with a periodicity of about70nm(called the D-period)based on a staggered arrangement of individual collagen monomers[28].Type I collagen is the most abundant and best studied collagen.It forms more than90%of the organic mass of bone and is the major collagen of tendons,skin,ligaments,cornea,and many intersti-tial connective tissues with the exception of very few tissues such as hyaline cartilage,brain,and vitreous body.The collagen type I triple helix is usually formed as a heterotrimer by two identical a1(I)-chains and one a2(I)-chain.The triple helical fibres are,in vivo,mostly incorporated into composite containing either type III collagen(in skin and reticular fibres)[29]or type V collagen(in bone, tendon,cornea)[30].In most organs and notably in tendons and fascia,type I collagen provides tensile stiffness and in bone,it defines considerable biome-chanical properties concerning load bearing,tensileK.Gelse et al./Advanced Drug Delivery Reviews55(2003)1531–15461535strength,and torsional stiffness in particular after calcification.The fibril-forming type II collagen is the charac-teristic and predominant component of hyaline carti-lage.It is,however,not specifically restricted to cartilage where it accounts for about80%of the total collagen content since it is also found in the vitreous body,the corneal epithelium,the notochord, the nucleus pulposus of intervertebral discs,and embryonic epithelial–mesenchymal transitions[4]. The triple helix of type II collagen is composed of three a1(II)-chains forming a homotrimeric molecule similar in size and biomechanical properties to that of type I collagen[31].Collagen fibrils in cartilage represent heterofibrils containing in addition to the dominant collagen II,also types XI and IX collagens which are supposed to limit the fibril diameter to about15–50nm[32]as well as other non-collage-nous pared to type I collagen,type II collagen chains show a higher content of hydroxy-lysine as well as glucosyl and galactosyl residuesmatrix of hyaline cartilageof the type II collagen pre-mRNA results in twoforms of the a1(II)-chains.In the splice variant IIB, Fig.2.(A)Schematic representation of the supramolecular assembly of the collagen fibrils in the characteristic quarter-staggered form.The monomers are300-nm long and40-nm gaps separate consecutive monomers causing the characteristic appearance of the collagen type I fibrils on the ultrastructural level.(B+C)Collagen type I(B)and II(C)fibrils as they are arranged in normal tendon(B)and articular cartilage(C). Whereas they are arranged in tendon in a parallel manner,they show a rather network-like supramolecular arrangement in articular cartilage.K.Gelse et al./Advanced Drug Delivery Reviews55(2003)1531–15461536theexonN-terminal propeptide is excluded,whereas it is retained in the IIA variant,the embryonic form found in prechondrogenic mesenchyme [33,34],osteo-phytes [35,36],perichondrium,vertebrae [33]and chondrogenic tumors [37].The switch from IIA to IIB suggests a role during developmental processes chains and is widely distributed in collagen I contain-ing tissues with the exception of bone [38].It is an important component of reticular fibres in the inter-stitial tissue of the lungs,liver,dermis,spleen,and vessels.This homotrimeric molecule also often con-tributes to mixed fibrils with type I collagen and is also abundant in elastic tissues [39].Types V and XI collagens are formed as hetero-trimers of three different a -chains (a 1,a 2,a 3).It is remarkable that the a 3-chain of type XI collagen is encoded by the same gene as the a 1-chain of type II collagen and only the extent of glycosylation and hydroxylation differs from a 1(II)[4].Although it is finally not sorted out,a combination between differ-ent types V and XI chains appears to exist in various tissues [40–43].Thus,types V and XI collagens form a subfamily within fibril-forming collagens,though they share similar biochemical properties and func-tions with other members of this family.As men-tioned before,type V collagen typically forms heterofibrils with types I and III collagens and contributes to the organic bone matrix,corneal stro-ma and the interstitial matrix of muscles,liver,lungs,and placenta [12].Type XI collagen codistributes largely in articular cartilage with type II collagen [4,13].The large amino-terminal non-collagenous domains of types V and XI collagens are processed only partially after secretion and their incorporation into the heterofibrils is thought to control their assembly,growth,and diameter [44].Since their triple helical domains are immunologically masked in tissues,they are thought to be located central in the fibrils rather than on their surface [12,45].Thus,type V collagen may function as a core structure of the fibrils with types I and III collagens polymerizing around this central axis.Analogous to this model,type XI collagen is supposed to form the core ofII heterofibrils [3].A high content of in the N-terminal domains of a 1(V)-and a 2(V)-chains,with 40%of the residues being O-sulfated,supports a strong interaction with the more basic triple helical part and is likely to stabilize the fibrillar complex [46].3.2.Collagen types IX,XII,and XIV—The F ACIT collagensThe collagen types IX,XII,XIV,XVI,XIX,and XX belong to the so-called F ibril-A ssociated C olla-gens with I nterrupted T riple helices (FACIT colla-gens).The structures of these collagens are characterized by ‘‘collagenous domains’’interrupted by short non-helical domains and the trimeric mole-cules are associated with the surfaces of various fibrils.Collagen type IX codistributes with type II colla-gen in cartilage and the vitreous body [4].The heterotrimeric molecule consists of three different a -chains (a 1(IX),a 2(IX),and a 3(IX))forming three triple helical segments flanked by four globular domains (NC1–NC4)[47].Type IX collagen mole-cules are located periodically along the surface of type II collagen fibrils in antiparallel direction [48].This interaction is stabilized by covalent lysine-derived cross-links to the N-telopeptide of type II collagen.A hinge region in the NC3domain provides flexibility in the molecule and allows the large and highly cationic globular N-terminal domain to reach out from the fibril where it presumably interacts with proteo-glycans or other matrix components [13,49].A chon-droitin-sulfate side chain is covalently linked to a serine residue of the a 2(IX)-chain in the NC3domain and the size may vary between tissues [50].It might be involved in the linkage of various collagen fibres as well as their interaction with molecules of the extracellular matrix.Additionally,collagen type XVI is found in hyaline cartilage and skin [51]and is associated with a subset of the collagen ‘‘type II fibers’’(Graessel,personal communication).Types XII and type XIV collagens are similar in structure and share sequence homologies to type IX collagen.Both molecules associate or colocalize with type I collagen in skin,perichondrium,periosteum,tendons,lung,liver,placenta,and vessel walls [4].The function of these collagens,as well as of collagen55(2003)1531–15461537types XIX [52]and XX [53],within the tissue is still poorly understood.3.3.Collagen type VI—a microfibrillar collagen Type VI collagen is an heterotrimer of three differ-ent a -chains (a 1,a 2,a 3)with short triple helical domains and rather extended globular termini [54,55].This is in particular true for the a 3-chain which is nearly as twice as long as the other chains due to a large N-and C-terminal globular domains.However,these extended domains are subject not only to alternative splicing,but also to extensive posttrans-lational processing,both within and outside the cell [56,57].The primary fibrils assemble already inside the cell to antiparallel,overlapping dimers,which then align in a parallel manner to form tetramers.Following secretion into the extracellular matrix,type VI collagen tetramers aggregate to filaments and form an indepen-dent microfibrillar network in virtually all connective tissues,except bone [14,57,58].Type VI collagen fibrils appear on the ultrastructural level as fine fila-ments,microfibrils or segments with faint crossband-ing of 110-nm periodicity [58–63],although not all fine filaments represent type VI collagen [64–68].3.4.Collagen types X and VIII—short chain collagens Types X and VIII collagens are structurally related short-chain collagens.Type X collagen is a charac-teristic component of hypertrophic cartilage in the fetal and juvenile growth plate,in ribs and vertebrae [7].It is a homotrimeric collagen with a large C-terminal and a short N-terminal domain and experi-ments in vitro are indicative for its assembly to hexagonal networks [69].The function of type X collagen is not completely resolved.A role in endo-chondral ossification and matrix calcification is dis-cussed.Thus,type X collagen is thought to be involved in the calcification process in the lower hypertrophic zone [69–72],a possibility supported by the restricted expression of type X collagen in thecalcified zone of adult articular cartilage [73,74]and its prevalence in the calcified chick egg shell [75].In fetal cartilage,type X collagen has been localized in fine filaments as well as associated with type II fibrils.[76].Mutations of the COL10A1gene are causative for the disease Schmid type metaphyseal chondrodysplasia (SMCD)impeding endochondral ossification in the metaphyseal growth plate.This leads to growth deficiency and skeletal deformities with short limbs [77].Type VIII collagen is very homologous to type X collagen in structure but shows a distinct distribution and may therefore have different functions [78].This network-forming collagen is produced by endothelial cells and assembles in hexagonal lattices,e.g.in the Descemet’s membrane in the cornea [79].3.5.Collagen type IV—the collagen of basement membranesType IV collagen is the most important structural component of basement membranes integrating lam-inins,nidogens and other components into the visible two-dimensional stable supramolecular ag-gregate.The structure of type IV collagen is characterized by three domains:the N-terminal 7S domain,a C-terminal globular domain (NC1),and the central triple helical part with short interruptions of the Gly-X-Y repeats resulting in a flexible triple helix.Six subunit chains have been identified yet,a 1(IV)–a 6(IV),associating into three distinct heterotrimeric molecules.The predominant form is represented by a 1(IV)2a 2(IV)heterotrimers forming the essential network in most embryonic and adult basement membranes.Specific dimeric interactions of the C-terminal NC1domains,cross-linking of four 7S domains as well as interactions of the triple helical domains,are fundamental for the stable network of collagen IV [80].The isoforms a 3(IV)–a 6(IV)show restricted,tissue-specific ex-pression patterns and are forming either an inde-pendent homotypic network of a 3(IV)a 4(IV)a 6(IV)Fig.3.Schematic representation of collagen synthesis starting form the nuclear transcription of the collagen genes,mRNA processing,ribosomal protein synthesis (translation)and post-translational modifications,secretion and the final steps of fibril formation.(SP:signal peptidase;GT:hydroxylysyl galactosyltransferase and galactosylhydroxylysyl glucosyltransferase;LH:lysyl hydroxylase;PH:prolyl hydroxylase;OTC:oligosaccharyl transferase complex;PDI:protein disulphide isomerase;PPI:peptidyl-prolyl cis -trans -isomerase;NP:procollagen N -proteinase;CP:procollagen C-proteinase;LO:lysyl oxidase;HSP47:heat shock protein 47,colligin1).K.Gelse et al./Advanced Drug Delivery Reviews 55(2003)1531–15461538K.Gelse et al./Advanced Drug Delivery Reviews55(2003)1531–15461539heterotrimers(kidney,lung)or a composite network of a5(IV)2a6(IV)/a1(IV)2a2(IV)molecules[81]. Mutations of the major isoform a1(IV)2a2(IV)are assumed to be embryonic lethal,but defects of the a5(IV),as well as a3(IV)or a4(IV)-chains are causative for various forms of Alport syndrome due to the importance of the a3a4a6heterotrimer for stability and function of glomerular and alveolar basement membranes[3].4.Biosynthesis of collagensThe biosynthesis of collagens starting with gene transcription of the genes within the nucleus to the aggregation of collagen heterotrimers into large fibrils is a complex multistep process(Fig.3).Since most of our knowledge of these mechanisms is based on fibril-forming collagens,this discussion will mostly focus on type I collagen.It is likely that the basic mecha-nisms of triple helix formation and processing will also apply for other collagen types.4.1.Transcription and translationThe regulation of the transcriptional activities of collagens depends largely on the cell type,but may also be controlled by numerous growth factors and cytokines(for review,see Ref.[38]).Thus,bone formation is stimulated,at least in the adult,by members of the TGF-h-superfamily as well as the insulin-like-growth factors.In other tissues,fibro-blast-growth-factors and many other agents are even more important.To discuss this in more detail is beyond the scope of this review and needs to be evaluated for all collagens and tissues separately.Most collagen genes revealed a complex exon–intron pattern,ranging from3to117exons,with the mRNAs of fibrillar collagens encoded by more than 50exons.Therefore,in many cases,different mRNA species could be detected,caused by multiple tran-scription initiation sites,alternative splicing of exons or combination of both.For example,in the cornea and the vitreous body,a shorter form of type IX collagen mRNA is generated by an additional start site between exons6and7[4].Alternative splicing has been reported for many collagen types and was first described for type II collagen.A longer form of type II collagen(COL2A)is expressed by chondro-progenitor cells and varies from a shorter form (COL2B)where exon2is excluded[33]and which is the main gene product of mature articular chon-drocytes.More recently,more than17different tran-scripts have been reported for type XIII collagen[82] and alternative splicing also generates heterogeneous transcription products for collagens VI,XI,XII[82–85].In addition to splicing,the pre-mRNA undergoes capping at the5V end and polyadenylation at the3V end and the mature mRNA is transported to the cytoplasm and translated at the rough endoplasmatic reticulum.Ribosome-bound mRNA is translated into prepro-collagen molecules which protrude into the lumen of the rough endoplasmatic reticulum with the help of a signal recognition domain recognized by the cor-responding receptors.4.2.Posttranslational modifications of collagenAfter removal of the signal peptide by a signal peptidase(Fig.3),the procollagen molecules undergo multiple steps of post-translational modifications.Hy-droxylation of proline and lysine residues catalyzed by prolyl3-hydroxylase,prolyl4-hydroxylase,and lysyl hydroxylase,respectively.All three enzymes require ferrous ions,2-oxoglutarate,molecular oxy-gen,and ascorbate as cofactors.In fibril-forming collagens,approximately50%of the proline residues contain a hydroxylgroup at position4and the extent of prolyl-hydroxylation is species-dependent.The organisms living at lower environmental temperatures show a lower extent of hydroxylation[86].The presence of4-hydroxyproline is essential for intramo-lecular hydrogen bonds and thus contributes to the thermal stability of the triple helical domain,and therefore also to the integrity of the monomer and collagen fibril.The function of3-hydroxyproline is not known[3].The extent of lysine hydroxylation also varies between tissues and collagen types[87]. Hydroxylysine residues are able to form stable inter-molecular cross-linking of collagen molecules in fibrils and additionally represent sites for the attach-ment of carbohydrates.Glucosyl-and galactosyl-residues are transferred to the hydroxyl groups of hydroxylysine;this is catalyzed by the enzymes hydroxylysyl galactosyltransferase and galactosylhy-droxylysyl-glucosyltransferase,respectively(Fig.3).K.Gelse et al./Advanced Drug Delivery Reviews55(2003)1531–1546 1540。

two-stage least squares analysis

two-stage least squares analysis

two-stage least squares analysis [twostage least squares analysis]Introduction to Two-stage Least Squares Analysis:Two-stage least squares (2SLS) analysis is a statistical technique that is often used in econometrics and social science research to deal with endogeneity issues. Endogeneity occurs when there is a two-way causal relationship between the independent and dependent variables, leading to biased and inconsistent estimates. 2SLS analysis provides a solution to this problem by using instrumental variables to estimate the causal relationship between the variables of interest.Step 1: Identification of Endogeneity Problem:The first step in conducting a two-stage least squares analysis is to identify the potential endogeneity problem. This can be done by examining the theoretical underpinnings of the relationship between the variables and looking for sources of omitted variable bias or reverse causality. For example, in a study investigating the effect of education on income, endogeneity may arise if individuals with higher initial income levels are more likely to invest in education.Step 2: Selection of Instrumental Variables:Once the endogeneity problem is identified, the next step is to select appropriate instrumental variables. Instrumental variables are variables that are correlated with the endogenous variable but are not directly related to the dependent variable. The instrumental variables should satisfy the conditions of relevance and exogeneity. Relevance means that the instrumental variables have a significant impact on the endogenous variable, while exogeneity implies that the instrumental variables are not affected by the outcome variable.Step 3: Estimation of First Stage Regressions:In the first stage of two-stage least squares analysis, the instrumental variables are used to estimate the relationship between the endogenous variable and the instrumental variables. This estimation is done through a regression analysis, where the endogenous variable is regressed on the instrumental variables. The estimated coefficients from this regression represent the predicted values of the endogenous variable, which are then used in the second stage of the analysis.Step 4: Check for Strong Instruments:After estimating the first stage regressions, it is important to evaluate the strength of the instrumental variables. Weak instruments can lead to biased estimates and reduced precision. Several statistical tests, such as the F-statistic and the Kleibergen-Paap Wald rk F statistic, can be used to assess the strength of the instruments. If the instruments are weak, alternative identification strategies or different instrumental variables should be considered.Step 5: Estimation of Second Stage Regressions:In the second stage of two-stage least squares analysis, the predicted values of the endogenous variable obtained from the first stage regression are used as an instrumental variable in the regression of the dependent variable on the endogenous variable and other control variables. This two-stage regression effectively eliminates the endogeneity problem and provides consistent and unbiased estimates of the causal relationship of interest.Step 6: Interpretation of Results:After conducting the two-stage least squares analysis, the estimated coefficients from the second stage regression can beinterpreted as the causal effects of the independent variables on the dependent variable, accounting for the endogeneity problem. These estimates should be evaluated in conjunction with their statistical significance and the goodness of fit of the regression model.Conclusion:Two-stage least squares analysis is a valuable technique for addressing endogeneity issues in econometric and social science research. By using instrumental variables and conductingtwo-stage regressions, this method provides unbiased and consistent estimates of causal relationships. However, it is important to carefully select appropriate instruments and assess their strength to ensure the validity of the analysis.。

俱乐部趋同

俱乐部趋同

Applied Economics Letters,2006,13,569–574Club convergence inEuropean regionsRita De Siano a and Marcella D’Uva b,*a Department of Economic Studies,University of Naples‘Parthenope’,Via Medina40,80133Naples,Italyb Department of Social Sciences,University of Naples L’Orientale,Largo S.Giovanni Maggiore30,80134Naples,ItalyThis study investigates the‘club convergence’hypothesis applying the stochastic notion of convergence to groups of European regions.In order to avoid the group selection bias problem,the innovative regression tree technique was applied to select endogenously the most important variables in achieving the best identification of groups on the base of per capita income and productive specialization.Tests on stochastic convergence in each group evidences a strong convergence among the wealthiest regions of the European Union and a trend of weak convergence among the remaining groups,confirming Baumol’s hypothesis of convergence.I.IntroductionOver the past decade many authors have explored the evolution of output discrepancies,at both national and regional levels.In particular,starting with Baumol(1986)it has been widely hypothesized that convergence may hold not for all economies but within groups of them showing similar characteristics (Azariadis and Drazen,1990).This evidence is referred to as the‘club convergence’hypothesis which implies that a set of economies may converge with each other,in the sense that in the long run they tend towards a common steady state position, but there is no convergence across different sets. In seeking to test the club convergence hypothesis (Qing Li,1999;Feve and Le Pen,2000;Su,2003,for example)two main questions arise:(a)which frame-work of convergence to use,and(b)how to identify the economies belonging to each club.Initially,a cross-section notion of convergence was used in order to verify the existence of a negative relationship between initial per capita income and its growth rate. In contrast with this notion a stochastic definition of convergence(Carlino and Mills,1993)was proposed and explored by using time series analyses. According to this framework there is stochastic convergence if per capita income disparities between economies follow a stationary process.Bernard and Durlauf(1996)found that when economies show multiple long run equilibria,cross-sectional tests tend to spuriously reject the null hypothesis of no convergence and,as a consequence,represent a weaker notion of convergence than that of the time series.As regards the second point,two methods can be used in order to create different groups of economies.The first sorts of economies follows some a priori criteria(initial level of GDP,education, technology,capital accumulation,etc.)while the second follows an endogenous selection method (Durlauf and Johnson,1995).Finally,the switching regression with the contribution of additional infor-mation on the sample separation followed by Feve and Le Pen(2000)can be mentioned as an intermediate method in modelling convergence clubs. This study investigates the‘club convergence’hypothesis applying the stochastic notion of conver-gence to groups of European regions sorted accord-ing to their initial levels of per capita income and*Corresponding author.E-mail:mduva@unior.itApplied Economics Letters ISSN1350–4851print/ISSN1466–4291onlineß2006Taylor&Francis569/journalsDOI:10.1080/13504850600733473productive specialization(De Siano and D’Uva, 2004,2005)through the application of an innovative methodology known as Classification and Regression Tree Analysis(CART).Unlike other partitioning methods,CART allows a regression to be performed together with a classification analysis on the same ‘learning’dataset,without requiring particular speci-fication of the functional form for the predictor variables which are selected endogenously.The importance of similarities in the initial productive specialization has been highlighted by several theore-tical contributions(Jacobs,1969;Marshall,1980; Romer,1986;Lucas,1988;Helg et al.,1995;Bru lhart, 1998;Ottaviano and Puga,1998)which found that it can be crucial in determining both the nature and size of responses to external shocks.The paper is organized as follows:Section II introduces the methodology of the empirical analysis, Section III displays the dataset,Section IV shows the results of econometric analysis and Section V concludes.II.MethodologyThe empirical analysis is carried out in two parts:first regions are grouped through the classification and regression tree analyses(CART),then convergence is tested within‘clubs’using the time series analysis. CART methodology(Breiman et al.,1984)provides binary recursive partitioning using non-parametric approaches in order to construct homogeneous groups of regions using splitting variables which minimize the intra-group‘impurity’as predictors. The final outcome is a tree with branches and ‘terminal nodes’,as homogeneous as possible,where the average value of the node represents the predicted value of the dependent variable.In this analysis the regression is carried out through the least squares method using the regional GDP growth rate as dependent variable and initial GDP and specializa-tion indexes as explicative variables.In the second part of the study Carlino and Mills(1993)notion of stochastic convergence is applied in each group identified by CART methodology.It follows that if the logarithm of a region’s per capita income relative to the group’s average does not contain a unit root,the region converges.The model(Ben-David, 1994;Qing Li,1999)is the following:y j i,t ¼ iþ i tþ’y i,tÀ1þ"i,tð1Þwhere y j i,t is the log of region i per capita income inyear t,j is the region’s group and"is white noise errorwith0mean.Summing Equation1over j for eachgroup and dividing the outcome by the number ofregions within the group,the following equation isobtained:"y t¼" þ" tþ’"y tÀ1þ"tð2Þwhere"y t is the group’s average per capita incomein year t(the group superscript is suppressed).Subtracting Equation2from Equation1one has:RI i,t¼AþBtþ’RI i,tÀ1þ"tð3Þwhere RI i,t is the logarithm of region i per capitaincome relative to the group’s average at time t(y j i,tÀ"y t).For each region of the sample we apply theAugmented Dickey–Fuller(ADF)test(Dickey andFuller,1979)using the ADF regression ofEquation3:ÁRI t¼ þ tþ RI tÀ1þX kj¼1c jÁRI tÀjþ"tð4ÞAt this point,considering the low power of the ADFtest in the case of short time series,we run alsothe Kwiatkowski et al.(1992)test(KPSS)for trendstationarity.The null hypothesis of the KPSS test isthe trend stationarity against the unit root alter-native.If the KPSS statistic is larger than the criticalvalues the null hypothesis is rejected.The combinedanalysis of KPSS and ADF tests results leads on thefollowing possibilities(Qing Li,1999):.rejection by ADF tests and failure to reject byKPSS!strong convergence;.failure to reject by both ADF and KPSS!weakconvergence;.rejection by KPSS test and failure to rejectADF!no convergence;.rejection by both ADF and KPSS tests invitesto perform further analyses.III.Data DescriptionThis section presents the dataset used both to groupthe sample regions and to run the econometricanalysis.Data for GDP and employment are fromthe Eurostat New Cronos Regio database at NUTS2level.1Annual values for GDP per inhabitant in termsof Purchasing Power Parity(PPP)and the number of1According to EC Regulation No.1059/2003.570R.De Siano and M.D’Uvaemployees in the NACE92productive branches from1981to 2000are used.The sample consists of 123regions belonging to nine countries:11Belgian,8Dutch,29German,222French,20Italian,18Spanish,5Portuguese,2Greek,38British.4For each region (i )the following initial productivespecialization indexes (SP)were built for all theconsidered branches 5(j ):SP ij ¼E ij P n j ¼1ij P m i ¼1E ij P n j ¼1P mi ¼1ijð5Þwhere E indicates the number of employees.IV.Empirical ResultsThe main purpose of the study is to test the ‘clubconvergence’hypothesis across the European regions.In particular,the study aims to investigate whethera region’s per capita income converges to the averageof the group to which it belongs.In order to avoidthe group selection bias problem,the regressiontree technique was applied to select endogenouslythe most important variables in achieving thebest identification of groups (De Siano and D’Uva,2005).If the majority of regions in a groupconverges,the group may be considered a conver-gence ‘club’.The CART method allowed a tree to be built withfour terminal nodes including regions showing a morehomogeneous behaviour of per capita GDP growthrate and productive specialization.Results of CARTanalysis together with the stochastic convergence tests for each group are presented in what follows.The first group consists of 11regions (from Spain,Greece and Portugal)characterized by:the highest estimated mean value of GDP growth rate (126.08%)despite the lowest initial income level (average equal to 4144.3);strong specialization in the agriculture sector (the highest and equal to 3.75),construction branch (2.09)and food and beverages compartment (1.93);the minimum specialization in chemical,energy,and machinery branches and the highest in food-beverages-tobacco,mineral and construction.More than 80%of these regions display ‘weak’convergence while remaining regions show ‘strong’convergence (Table 1).The second group includes 23regions (mainly from Belgium,Spain,Italy and the United Kingdom)characterized by:an average GDP growth rate equal to 111.36%and the second highest initial income level (5788.78);strong specialization in agriculture (2.68)sector,food and beverage (1.26),construction (1.52)and energy (1.20)compartments;the highest specialization in chemical products (0.98);the second highest level of specialization in agricul-ture construction and energy.Almost all these regions present ‘weak’convergence (Table 2).The third group is formed by 21regions from Belgium,France,Germany,the Netherlands,Spain,the UK and Italy (only Abruzzo)characterized by:an estimate for the GDP growth rate of 106%and an average initial level of income equal to 6920.6;main specializations in manufacturing (1.03),mineral products (1.13),construction (1.22),food and beverage (1.45)and energy (1.21);the highest 2The analysis starts from 1984due to the lack of data in the respective regional labour statistics.3During the period 1983–1987there has been a different aggregation of Greek regions at NUTS2level.Kriti and Thessalia are the only regions which presents data for the period 1984–2000.4The geographic units for UK are at NUTS1level of Eurostat classification because of the lack of data for NUTS2units.5Agricultural-forestry and fishery,manufacturing,fuel and power products,non-metallic minerals and minerals,food-beverages-tobacco,textiles-clothing-leather and footwear,chemical products,metal products,machinery-equipment and electrical goods,various industries,building and construction,transport and communication,credit and insurance services.Table 1.Convergence test results of group 1Regions group 1ADF statistics KPSS statistics l ¼4Regions group 1ADF statistics KPSS statistics l ¼4Castilla-la ManchaÀ2.9780.099gr 43Kriti À4.05ÃÃ0.080ExtremaduraÀ3.320.097Pt11Norte À4.03ÃÃ0.126AndaluciaÀ2.630.094Pt12Centro (P)À2.290.123Ceuta y MelillaÀ1.770.123Pt14Alentejo À2.770.104CanariasÀ1.940.121Pt15Algarve À2.010.086ThessaliaÀ1.760.137Notes :ÃÃdenote statistical significance using unit root critical values at the 5%(À3.645).Club convergence in European regions571specialization in energy and manufacturing branches.Except for Abruzzo and Noord Brabant,which donot converge,all the other regions ‘weakly’convergeto the group’s average (Table 3).The fourth group contains 68regions (almost allGerman,French and Italian (North-Centre)andsome Belgian and Dutch)characterized by thelowest estimation of the GDP growth rate (97.8%),despite their highest initial GDP level (8893.9);thehighest specialization in the branches of the servicessector (1.16and 1.07,respectively)and in machinery(1.01);the lowest specialization in agriculture,foodand beverages,textile and construction activities.These regions present the highest percentage of‘strong’convergence to the group’s average (morethan 60%,Table 4).Table 5presents the summary of convergence testsresults (percentage are in parentheses).The main outcome of this study is the evidence of strong convergence among the wealthiest regions of the European Union.Besides,it appears that there is a trend of weak convergence also among the remaining groups (percentages are considerably over 80%).Therefore,Baumol’s hypothesis of conver-gence within clubs showing similar characteristics is confirmed.V.Conclusion This study tests the ‘club convergence’hypothesis applying the stochastic notion of convergence to groups of European regions.In order to avoid the group selection bias problem,the innovative regression tree technique was applied to selectTable 3.Convergence test results of group 3Regions group 3ADF statistics KPSS statistics l ¼4Regions group 3ADF statistics KPSS statistics l ¼4LimburgÀ1.680.116Abruzzo 2.600.153ÃÃHainautÀ0.800.091Friesland À3.620.142NamurÀ1.840.094Noord-Brabant À2.590.148ÃÃNiederbayernÀ1.270.104Limburg (NL)À2.980.128OberpfalzÀ1.400.097Yorkshire and The Humber À1.610.085TrierÀ1.430.119East Midlands À2.190.091Comunidad Foral de NavarraÀ2.750.071West Midlands À1.920.080La RiojaÀ1.770.119East Anglia À2.150.134BalearesÀ2.960.108South West À1.950.091LimousinÀ2.410.083Scotland 2.220.093Languedoc-RoussillonÀ3.390.105Notes :ÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146).Table 2.Convergence test results of group 2Regions group 2ADF statistics KPSS statistics l ¼4Regions group 2ADF statistics KPSS statistics l ¼4Vlaams BrabantÀ1.220.100Murcia À1.530.124Brabant WallonÀ1.600.111Molise À2.170.078Luxembourg1.190.122Campania À3.220.078Lu neburgÀ0.280.114Puglia À2.820.115GaliciaÀ1.690.140Basilicata À2.100.140Principado de AsturiasÀ1.550.146ÃÃCalabria À5.07ÃÃÃ0.106CantabriaÀ1.080.133Sicilia À2.980.142Aragon À1.580.142Sardegna À2.210.141Comunidad de MadridÀ1.380.091Lisboa e Vale do Tejo À2.620.141Castilla y Leon À2.580.138Wales À2.120.098Cataluna À1.550.097Northern Ireland À1.790.120Comunidad Valenciana À1.420.105Notes :ÃÃand ÃÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146)and 1%level (0.216)respectively,using unit root critical values at the 5%(À3.645)and 1%(À4.469).572R.De Siano and M.D’Uvaendogenously the most important variables inachieving the best identification of groups.Testson stochastic convergence in each group identifiedby CART evidence strong convergence among thewealthiest regions of the European Union and atrend of weak convergence among the remaininggroups.References Azariadis,C.and Drazen,A.(1990)Threshold externalities in economic development,Quarterly Journal of Economics ,105,501–26.Baumol,W.J.(1986)Productivity growth,convergence and welfare:what the long run data show,AmericanEconomic Review ,76,1072–85.Table 5.Convergence test resultsGroupsNo.of regions Strong convergence Weak convergence No convergence 1112(18,19)9(81,81)2231(4.35)21(91.3)1(4.35)32119(90.48)2(9.52)46843(63.23)20(29.41)4(5.88)Table 4.Convergence test results of group 4Regions group 4ADF statistics KPSS statistics l ¼4Regions group 4ADF statistics KPSS statistics l ¼4RegionBruxelles capitale À2.650.112Haute-Normandie À4.11ÃÃ0.102AntwerpenÀ2.770.102Centre (FR)À5.13ÃÃÃ0.099Oost-VlaanderenÀ3.150.078Basse-Normandie À3.86ÃÃ0.101West-VlaanderenÀ3.030.097Bourgogne À5.03ÃÃÃ0.113Licge À3.060.089Nord-Pas-de-Calais À4.37ÃÃ0.130StuttgartÀ4.22ÃÃ0.123Lorraine À4.41ÃÃ0.139KarlsruheÀ4.51ÃÃÃ0.088Alsace À4.13ÃÃ0.094FreiburgÀ5.11ÃÃÃ0.092Franche-Comte À5.20ÃÃÃ0.145Tu bingenÀ4.94ÃÃÃ0.104Pays de la Loire À4.34ÃÃ0.116OberbayernÀ4.17ÃÃ0.094Bretagne À4.41ÃÃ0.124MittelfrankenÀ3.79ÃÃ0.089Poitou-Charentes À4.74ÃÃÃ0.102UnterfrankenÀ0.420.140Aquitaine À3.290.104SchwabenÀ4.11ÃÃ0.084Midi-Pyre ne es À5.48ÃÃÃ0.103BremenÀ3.76ÃÃ0.121Rho ne-Alpes À4.93ÃÃÃ0.104HamburgÀ3.350.097Auvergne À4.43ÃÃ0.135DarmstadtÀ3.150.125Provence-Alpes-Co te d’Azur À5.10ÃÃÃ0.109GießenÀ3.020.088Corse À2.560.166ÃÃKasselÀ3.0120.094Piemonte À3.460.112BraunschweigÀ3.82ÃÃ0.116Valle d’Aosta À4.36ÃÃ0.080HannoverÀ3.96ÃÃ0.083Liguria À4.26ÃÃ0.117Weser-EmsÀ3.400.084Lombardia À4.04ÃÃ0.101Du sseldorfÀ3.94ÃÃ0.097Trentino-Alto Adige À3.84ÃÃ0.109Ko lnÀ3.96ÃÃ0.084Veneto À3.68ÃÃ0.106Mu nsterÀ4.04ÃÃ0.087Friuli-Venezia Giulia À4.20ÃÃ0.116DetmoldÀ4.06ÃÃ0.099Emilia-Romagna À3.120.136ArnsbergÀ3.98ÃÃ0.096Toscana À3.190.121KoblenzÀ3.88ÃÃ0.113Umbria À3.560.146ÃÃRheinhessen-PfalzÀ4.18ÃÃ0.107Marche À3.250.136SaarlandÀ4.35ÃÃ0.090Lazio À3.96ÃÃ0.098Schleswig-HolsteinÀ3.360.089Drenthe À1.850.134Pais VascoÀ3.630.159ÃÃUtrecht À2.400.155ÃÃI le de FranceÀ4.61ÃÃÃ0.110Noord-Holland À1.990.137Champagne ArdenneÀ3.79ÃÃ0.157ÃÃZuid-Holland À2.200.138Picardie À4.44ÃÃ0.142Zeeland À3.78ÃÃ0.093Notes :ÃÃand ÃÃÃdenote statistical significance using KPSS stationary critical values at the 5%level (0.146)and 1%level (0.216)respectively,using unit root critical values at the 5%(À3.645)and 1%(‘4.469).Club convergence in European regions573Ben-David, D.(1994)Convergence clubs and diverging economies,unpublished manuscript,University of Houston,Ben-Gurion University and CEPR. Bernard, A. B.and Durlauf,S.N.(1996)Interpreting tests of the convergence hypothesis,Journal of Econometrics,71,161–73.Breiman,L.,Friedman,J.L.,Olshen,R.A.and Stone,C.J.,(1984)Classification and Regression Trees,Wadsworth,Belmont,CA.Bru lhart,M.(1998)Economic geography,industrial location and trade:the evidence,World Economy,21, 775–801.Carlino,G.A.and Mills,L.O.(1993)Are US regional incomes converging?A time series analysis,Journal of Monetary Economics,32,335–46.De Siano,R.and D’Uva,M.(2004)Specializzazione e crescita:un’applicazione alle regioni dell’Unione Monetaria Europea,Rivista Internazionale di Scienze Sociali,4,381–98.De Siano,R.and D’Uva,M.(2005)Regional growth in Europe:an analysis through CART methodology, Studi Economici,87,115–28.Dickey,D.A.and Fuller,W.A.(1979)Distribution of the estimators for autoregressive time series with a unit root,Journal of The American Statistical Association, 74,427–31.Durlauf,S.N.and Johnson,P.A.(1995)Multiple regimes and cross-country growth behaviour,Journal of Applied Econometrics,10,365–84.Feve,P.and Le Pen,Y.(2000)On modelling convergence clubs,Applied Economic Letters,7,311–14.Helg,R.,Manasse,P.,Monacelli,T.and Rovelli,R.(1995) How much(a)symmetry in Europe?Evidence from industrial sectors,European Economic Review,39, 1017–41.Jacobs,J.(1969)The Economy of Cities,Jonathen Cape, London.Kwiatkowski, D.,Phillips,P. C. B.,Schmidt,P.and Shin,Y.(1992)Testing the null hypothesis of stationarity against the alternative of a unit root:how sure are we that economic time series have a unit root?,Journal of Econometrics,54, 159–78.Lucas,R. E.(1988)On the mechanics of economic development,Journal of Monetary Economics,22, 3–42.Marshall,A.(1980)Principles of Economics,Macmillan, London.Ottaviano,I.and Puga,D.(1998)Agglomeration in the global economy:a survey of the‘new economic geography’,World Economy,21,707–31.Qing,L.(1999)Convergence clubs:some further evidence, Review of International Economics,7,59–67. Romer,P.M.(1986)Increasing returns and long run growth,Journal of Political Economy,94, 1002–37.Su,J.J.(2003)Convergence clubs among15OECD countries,Applied Economic Letters,10,113–18.574R.De Siano and M.D’Uva。

斜率之和为0二级结论

斜率之和为0二级结论

斜率之和为0二级结论Question: Sum of Slopes is Zero Corollary Requirement: 1、The article needs to be answered in two languages. The whole article should be answered in English first. Afterall the answers are in English, then answer in Chinese. Do not answer one paragraph in Chinese and one paragraph in English. Before answering, mark it in advance. 2、The article should not be less than 800 words and should not contain my prompt.English Answer:The Sum of Slopes is Zero Corollary states that if two lines are perpendicular, then the sum of their slopes is zero. This corollary is a direct consequence of the definition of perpendicular lines.Two lines are perpendicular if and only if their slopes are negative reciprocals of each other. In other words, if the slope of one line is m, then the slope of theperpendicular line is -1/m.The Sum of Slopes is Zero Corollary can be used to solve a variety of problems involving perpendicular lines. For example, it can be used to find the equation of a line that is perpendicular to a given line, or to find the distance between two parallel lines.中文回答。

T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma

T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma

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Rough Approximation of a perference Relation by Dominance Relations

Rough Approximation of a perference Relation by Dominance Relations
This explains our interest in the rough sets theory (Pawlak, 1982, 1991), which proved to be a useful tool for analysis of vague description of decision situations (Pawlak and Slowinski, 1994). We remember that the rough set concept is founded on the assumption that with every object of the universe of discourse there is associated some information (data, knowledge). For example, if objects are potential projects, their technical and economic characteristics form information (description) about the projects. Objects characterized by the same information are indiscernible (similar) in view of available information about them. The indiscernibility relation generated in this way is the mathematical basis of the rough sets theory. Any set of indiscernible objects is called elementary set. Any subset of the universe can either be expressed precisely in terms of elementary sets or roughly only. In the latter case, this subset can be characterized by two ordinary sets, called lower and upper approximations. The lower approximation contains objects surely belonging to the subset considered; the upper approximation contains objects possibly belonging to the subset considered.

SPSS词汇中英文对照

SPSS词汇中英文对照

SPSS词汇(中英文对照) Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。

决奈达隆片说明书

决奈达隆片说明书
HIGHLIGHTS OF PRESCRIBING INFORMATION These highlights do not include all the information needed to use MULTAQ safely and effectively. See full prescribing information for MULTAQ. MULTAQ (dronedarone) Tablets Initial U.S. Approval: 2009 WARNING: HEART FAILURE MULTAQ is contraindicated in patients with NYHA Class IV heart failure or NYHA Class II - III heart failure with a recent decompensation requiring hospitalization or referral to a specialized heart failure clinic (4). In a placebo-controlled study in patients with severe heart failure requiring recent hospitalization or referral to a specialized heart failure clinic for worsening symptoms (the ANDROMEDA Study), patients given dronedarone had a greater than two-fold increase in mortality. Such patients should not be given dronedarone (14.3). ----------------------------INDICATIONS AND USAGE--------------------------MULTAQ is an antiarrhythmic drug indicated to reduce the risk of cardiovascular hospitalization in patients with paroxysmal or persistent atrial fibrillation (AF) or atrial flutter (AFL), with a recent episode of AF/AFL and associated cardiovascular risk factors (i.e., age >70, hypertension, diabetes, prior cerebrovascular accident, left atrial diameter ≥50 mm or left ventricular ejection fraction [LVEF] <40%), who are in sinus rhythm or who will be cardioverted (1, 14). ----------------------DOSAGE AND ADMINISTRATION----------------------One tablet of 400 mg twice a day with morning and evening meals (2) ---------------------DOSAGE FORMS AND STRENGTHS---------------------400 mg film-coated tablets (3) -------------------------------CONTRAINDICATIONS-----------------------------• Class IV heart failure or symptomatic heart failure with a recent decompensation (Boxed Warning, 4) • Second- or third- degree atrioventicular (AV) block or sick sinus syndrome (except when used in conjunction with a functioning pacemaker) (4) • Bradycardia <50 bpm (4) • Concomitant use of a strong CYP3A inhibitor (4) • Concomitant use of drugs or herbal products that prolong the QT interval and may induce Torsade de Pointes (4) • QTc Bazett interval ≥500 ms (4) • Severe hepatic impairment (4)

Parallel and Distributed Computing and Systems

Parallel and Distributed Computing and Systems

Proceedings of the IASTED International ConferenceParallel and Distributed Computing and SystemsNovember3-6,1999,MIT,Boston,USAParallel Refinement of Unstructured MeshesJos´e G.Casta˜n os and John E.SavageDepartment of Computer ScienceBrown UniversityE-mail:jgc,jes@AbstractIn this paper we describe a parallel-refinement al-gorithm for unstructuredfinite element meshes based on the longest-edge bisection of triangles and tetrahedrons. This algorithm is implemented in P ARED,a system that supports the parallel adaptive solution of PDEs.We dis-cuss the design of such an algorithm for distributed mem-ory machines including the problem of propagating refine-ment across processor boundaries to obtain meshes that are conforming and non-degenerate.We also demonstrate that the meshes obtained by this algorithm are equivalent to the ones obtained using the serial longest-edge refine-ment method.Wefinally report on the performance of this refinement algorithm on a network of workstations.Keywords:mesh refinement,unstructured meshes,finite element methods,adaptation.1.IntroductionThefinite element method(FEM)is a powerful and successful technique for the numerical solution of partial differential equations.When applied to problems that ex-hibit highly localized or moving physical phenomena,such as occurs on the study of turbulence influidflows,it is de-sirable to compute their solutions adaptively.In such cases, adaptive computation has the potential to significantly im-prove the quality of the numerical simulations by focusing the available computational resources on regions of high relative error.Unfortunately,the complexity of algorithms and soft-ware for mesh adaptation in a parallel or distributed en-vironment is significantly greater than that it is for non-adaptive computations.Because a portion of the given mesh and its corresponding equations and unknowns is as-signed to each processor,the refinement(coarsening)of a mesh element might cause the refinement(coarsening)of adjacent elements some of which might be in neighboring processors.To maintain approximately the same number of elements and vertices on every processor a mesh must be dynamically repartitioned after it is refined and portions of the mesh migrated between processors to balance the work.In this paper we discuss a method for the paral-lel refinement of two-and three-dimensional unstructured meshes.Our refinement method is based on Rivara’s serial bisection algorithm[1,2,3]in which a triangle or tetrahe-dron is bisected by its longest edge.Alternative efforts to parallelize this algorithm for two-dimensional meshes by Jones and Plassman[4]use randomized heuristics to refine adjacent elements located in different processors.The parallel mesh refinement algorithm discussed in this paper has been implemented as part of P ARED[5,6,7], an object oriented system for the parallel adaptive solu-tion of partial differential equations that we have devel-oped.P ARED provides a variety of solvers,handles selec-tive mesh refinement and coarsening,mesh repartitioning for load balancing,and interprocessor mesh migration.2.Adaptive Mesh RefinementIn thefinite element method a given domain is di-vided into a set of non-overlapping elements such as tri-angles or quadrilaterals in2D and tetrahedrons or hexahe-drons in3D.The set of elements and its as-sociated vertices form a mesh.With theaddition of boundary conditions,a set of linear equations is then constructed and solved.In this paper we concentrate on the refinement of conforming unstructured meshes com-posed of triangles or tetrahedrons.On unstructured meshes, a vertex can have a varying number of elements adjacent to it.Unstructured meshes are well suited to modeling do-mains that have complex geometry.A mesh is said to be conforming if the triangles and tetrahedrons intersect only at their shared vertices,edges or faces.The FEM can also be applied to non-conforming meshes,but conformality is a property that greatly simplifies the method.It is also as-sumed to be a requirement in this paper.The rate of convergence and quality of the solutions provided by the FEM depends heavily on the number,size and shape of the mesh elements.The condition number(a)(b)(c)Figure1:The refinement of the mesh in using a nested refinement algorithm creates a forest of trees as shown in and.The dotted lines identify the leaf triangles.of the matrices used in the FEM and the approximation error are related to the minimum and maximum angle of all the elements in the mesh[8].In three dimensions,the solid angle of all tetrahedrons and their ratio of the radius of the circumsphere to the inscribed sphere(which implies a bounded minimum angle)are usually used as measures of the quality of the mesh[9,10].A mesh is non-degenerate if its interior angles are never too small or too large.For a given shape,the approximation error increases with ele-ment size(),which is usually measured by the length of the longest edge of an element.The goal of adaptive computation is to optimize the computational resources used in the simulation.This goal can be achieved by refining a mesh to increase its resolution on regions of high relative error in static problems or by re-fining and coarsening the mesh to follow physical anoma-lies in transient problems[11].The adaptation of the mesh can be performed by changing the order of the polynomi-als used in the approximation(-refinement),by modifying the structure of the mesh(-refinement),or a combination of both(-refinement).Although it is possible to replace an old mesh with a new one with smaller elements,most -refinement algorithms divide each element in a selected set of elements from the current mesh into two or more nested subelements.In P ARED,when an element is refined,it does not get destroyed.Instead,the refined element inserts itself into a tree,where the root of each tree is an element in the initial mesh and the leaves of the trees are the unrefined elements as illustrated in Figure1.Therefore,the refined mesh forms a forest of refinement trees.These trees are used in many of our algorithms.Error estimates are used to determine regions where adaptation is necessary.These estimates are obtained from previously computed solutions of the system of equations. After adaptation imbalances may result in the work as-signed to processors in a parallel or distributed environ-ment.Efficient use of resources may require that elements and vertices be reassigned to processors at runtime.There-fore,any such system for the parallel adaptive solution of PDEs must integrate subsystems for solving equations,adapting a mesh,finding a good assignment of work to processors,migrating portions of a mesh according to anew assignment,and handling interprocessor communica-tion efficiently.3.P ARED:An OverviewP ARED is a system of the kind described in the lastparagraph.It provides a number of standard iterativesolvers such as Conjugate Gradient and GMRES and pre-conditioned versions thereof.It also provides both-and -refinement of meshes,algorithms for adaptation,graph repartitioning using standard techniques[12]and our ownParallel Nested Repartitioning(PNR)[7,13],and work mi-gration.P ARED runs on distributed memory parallel comput-ers such as the IBM SP-2and networks of workstations.These machines consist of coarse-grained nodes connectedthrough a high to moderate latency network.Each nodecannot directly address a memory location in another node. In P ARED nodes exchange messages using MPI(Message Passing Interface)[14,15,16].Because each message has a high startup cost,efficient message passing algorithms must minimize the number of messages delivered.Thus, it is better to send a few large messages rather than many small ones.This is a very important constraint and has a significant impact on the design of message passing algo-rithms.P ARED can be run interactively(so that the user canvisualize the changes in the mesh that results from meshadaptation,partitioning and migration)or without directintervention from the user.The user controls the systemthrough a GUI in a distinguished node called the coordina-tor,.This node collects information from all the other processors(such as its elements and vertices).This tool uses OpenGL[17]to permit the user to view3D meshes from different angles.Through the coordinator,the user can also give instructions to all processors such as specify-ing when and how to adapt the mesh or which strategy to use when repartitioning the mesh.In our computation,we assume that an initial coarse mesh is given and that it is loaded into the coordinator.The initial mesh can then be partitioned using one of a num-ber of serial graph partitioning algorithms and distributed between the processors.P ARED then starts the simulation. Based on some adaptation criterion[18],P ARED adapts the mesh using the algorithms explained in Section5.Af-ter the adaptation phase,P ARED determines if a workload imbalance exists due to increases and decreases in the num-ber of mesh elements on individual processors.If so,it invokes a procedure to decide how to repartition mesh el-ements between processors;and then moves the elements and vertices.We have found that PNR gives partitions with a quality comparable to those provided by standard meth-ods such as Recursive Spectral Bisection[19]but which(b)(a)Figure2:Mesh representation in a distributed memory ma-chine using remote references.handles much larger problems than can be handled by stan-dard methods.3.1.Object-Oriented Mesh RepresentationsIn P ARED every element of the mesh is assigned to a unique processor.V ertices are shared between two or more processors if they lie on a boundary between parti-tions.Each of these processors has a copy of the shared vertices and vertices refer to each other using remote ref-erences,a concept used in object-oriented programming. This is illustrated in Figure2on which the remote refer-ences(marked with dashed arrows)are used to maintain the consistency of multiple copies of the same vertex in differ-ent processors.Remote references are functionally similar to standard C pointers but they address objects in a different address space.A processor can use remote references to invoke meth-ods on objects located in a different processor.In this case, the method invocations and arguments destined to remote processors are marshalled into messages that contain the memory addresses of the remote objects.In the destina-tion processors these addresses are converted to pointers to objects of the corresponding type through which the meth-ods are invoked.Because the different nodes are inher-ently trusted and MPI guarantees reliable communication, P ARED does not incur the overhead traditionally associated with distributed object systems.Another idea commonly found in object oriented pro-gramming and which is used in P ARED is that of smart pointers.An object can be destroyed when there are no more references to it.In P ARED vertices are shared be-tween several elements and each vertex counts the number of elements referring to it.When an element is created, the reference count of its vertices is incremented.Simi-larly,when the element is destroyed,the reference count of its vertices is decremented.When the reference count of a vertex reaches zero,the vertex is no longer attached to any element located in the processor and can be destroyed.If a vertex is shared,then some other processor might have a re-mote reference to it.In that case,before a copy of a shared vertex is destroyed,it informs the copies in other processors to delete their references to itself.This procedure insures that the shared vertex can then be safely destroyed without leaving dangerous dangling pointers referring to it in other processors.Smart pointers and remote references provide a simple replication mechanism that is tightly integrated with our mesh data structures.In adaptive computation,the struc-ture of the mesh evolves during the computation.During the adaptation phase,elements and vertices are created and destroyed.They may also be assigned to a different pro-cessor to rebalance the work.As explained above,remote references and smart pointers greatly simplify the task of creating dynamic meshes.4.Adaptation Using the Longest Edge Bisec-tion AlgorithmMany-refinement techniques[20,21,22]have been proposed to serially refine triangular and tetrahedral meshes.One widely used method is the longest-edge bisec-tion algorithm proposed by Rivara[1,2].This is a recursive procedure(see Figure3)that in two dimensions splits each triangle from a selected set of triangles by adding an edge between the midpoint of its longest side to the opposite vertex.In the case that makes a neighboring triangle,,non-conforming,then is refined using the same algorithm.This may cause the refinement to prop-agate throughout the mesh.Nevertheless,this procedure is guaranteed to terminate because the edges it bisects in-crease in length.Building on the work of Rosenberg and Stenger[23]on bisection of triangles,Rivara[1,2]shows that this refinement procedure provably produces two di-mensional meshes in which the smallest angle of the re-fined mesh is no less than half of the smallest angle of the original mesh.The longest-edge bisection algorithm can be general-ized to three dimensions[3]where a tetrahedron is bisected into two tetrahedrons by inserting a triangle between the midpoint of its longest edge and the two vertices not in-cluded in this edge.The refinement propagates to neigh-boring tetrahedrons in a similar way.This procedure is also guaranteed to terminate,but unlike the two dimensional case,there is no known bound on the size of the small-est angle.Nevertheless,experiments conducted by Rivara [3]suggest that this method does not produce degenerate meshes.In two dimensions there are several variations on the algorithm.For example a triangle can initially be bisected by the longest edge,but then its children are bisected by the non-conforming edge,even if it is that is not their longest edge[1].In three dimensions,the bisection is always per-formed by the longest edge so that matching faces in neigh-boring tetrahedrons are always bisected by the same com-mon edge.Bisect()let,and be vertices of the trianglelet be the longest side of and let be the midpoint ofbisect by the edge,generating two new triangles andwhile is a non-conforming vertex dofind the non-conforming triangle adjacent to the edgeBisect()end whileFigure3:Longest edge(Rivara)bisection algorithm for triangular meshes.Because in P ARED refined elements are not destroyed in the refinement tree,the mesh can be coarsened by replac-ing all the children of an element by their parent.If a parent element is selected for coarsening,it is important that all the elements that are adjacent to the longest edge of are also selected for coarsening.If neighbors are located in different processors then only a simple message exchange is necessary.This algorithm generates conforming meshes: a vertex is removed only if all the elements that contain that vertex are all coarsened.It does not propagate like the re-finement algorithm and it is much simpler to implement in parallel.For this reason,in the rest of the paper we will focus on the refinement of meshes.5.Parallel Longest-Edge RefinementThe longest-edge bisection algorithm and many other mesh refinement algorithms that propagate the refinement to guarantee conformality of the mesh are not local.The refinement of one particular triangle or tetrahedron can propagate through the mesh and potentially cause changes in regions far removed from.If neighboring elements are located in different processors,it is necessary to prop-agate this refinement across processor boundaries to main-tain the conformality of the mesh.In our parallel longest edge bisection algorithm each processor iterates between a serial phase,in which there is no communication,and a parallel phase,in which each processor sends and receives messages from other proces-sors.In the serial phase,processor selects a setof its elements for refinement and refines them using the serial longest edge bisection algorithms outlined earlier. The refinement often creates shared vertices in the bound-ary between adjacent processors.To minimize the number of messages exchanged between and,delays the propagation of refinement to until has refined all the elements in.The serial phase terminates when has no more elements to refine.A processor informs an adjacent processor that some of its elements need to be refined by sending a mes-sage from to containing the non-conforming edges and the vertices to be inserted at their midpoint.Each edge is identified by its endpoints and and its remote ref-erences(see Figure4).If and are sharedvertices,(a)(c)(b)Figure4:In the parallel longest edge bisection algo-rithm some elements(shaded)are initially selected for re-finement.If the refinement creates a new(black)ver-tex on a processor boundary,the refinement propagates to neighbors.Finally the references are updated accord-ingly.then has a remote reference to copies of and lo-cated in processor.These references are included in the message,so that can identify the non-conforming edge and insert the new vertex.A similar strategy can be used when the edge is refined several times during the re-finement phase,but in this case,the vertex is not located at the midpoint of.Different processors can be in different phases during the refinement.For example,at any given time a processor can be refining some of its elements(serial phase)while neighboring processors have refined all their elements and are waiting for propagation messages(parallel phase)from adjacent processors.waits until it has no elements to refine before receiving a message from.For every non-conforming edge included in a message to,creates its shared copy of the midpoint(unless it already exists) and inserts the new non-conforming elements adjacent to into a new set of elements to be refined.The copy of in must also have a remote reference to the copy of in.For this reason,when propagates the refine-ment to it also includes in the message a reference to its copies of shared vertices.These steps are illustrated in Figure4.then enters the serial phase again,where the elements in are refined.(c)(b)(a)Figure5:Both processors select(shaded)mesh el-ements for refinement.The refinement propagates to a neighboring processor resulting in more elements be-ing refined.5.1.The Challenge of Refining in ParallelThe description of the parallel refinement algorithm is not complete because refinement propagation across pro-cessor boundaries can create two synchronization prob-lems.Thefirst problem,adaptation collision,occurs when two(or more)processors decide to refine adjacent elements (one in each processor)during the serial phase,creating two(or more)vertex copies over a shared edge,one in each processor.It is important that all copies refer to the same logical vertex because in a numerical simulation each ver-tex must include the contribution of all the elements around it(see Figure5).The second problem that arises,termination detection, is the determination that a refinement phase is complete. The serial refinement algorithm terminates when the pro-cessor has no more elements to refine.In the parallel ver-sion termination is a global decision that cannot be deter-mined by an individual processor and requires a collabora-tive effort of all the processors involved in the refinement. Although a processor may have adapted all of its mesh elements in,it cannot determine whether this condition holds for all other processors.For example,at any given time,no processor might have any more elements to re-fine.Nevertheless,the refinement cannot terminate because there might be some propagation messages in transit.The algorithm for detecting the termination of parallel refinement is based on Dijkstra’s general distributed termi-nation algorithm[24,25].A global termination condition is reached when no element is selected for refinement.Hence if is the set of all elements in the mesh currently marked for refinement,then the algorithmfinishes when.The termination detection procedure uses message ac-knowledgments.For every propagation message that receives,it maintains the identity of its source()and to which processors it propagated refinements.Each prop-agation message is acknowledged.acknowledges to after it has refined all the non-conforming elements created by’s message and has also received acknowledgments from all the processors to which it propagated refinements.A processor can be in two states:an inactive state is one in which has no elements to refine(it cannot send new propagation messages to other processors)but can re-ceive messages.If receives a propagation message from a neighboring processor,it moves from an inactive state to an active state,selects the elements for refinement as spec-ified in the message and proceeds to refine them.Let be the set of elements in needing refinement.A processor becomes inactive when:has received an acknowledgment for every propa-gation message it has sent.has acknowledged every propagation message it has received..Using this definition,a processor might have no more elements to refine()but it might still be in an active state waiting for acknowledgments from adjacent processors.When a processor becomes inactive,sends an acknowledgment to the processors whose propagation message caused to move from an inactive state to an active state.We assume that the refinement is started by the coordi-nator processor,.At this stage,is in the active state while all the processors are in the inactive state.ini-tiates the refinement by sending the appropriate messages to other processors.This message also specifies the adapta-tion criterion to use to select the elements for refinement in.When a processor receives a message from,it changes to an active state,selects some elements for refine-ment either explicitly or by using the specified adaptation criterion,and then refines them using the serial bisection algorithm,keeping track of the vertices created over shared edges as described earlier.When itfinishes refining its ele-ments,sends a message to each processor on whose shared edges created a shared vertex.then listens for messages.Only when has refined all the elements specified by and is not waiting for any acknowledgment message from other processors does it sends an acknowledgment to .Global termination is detected when the coordinator becomes inactive.When receives an acknowledgment from every processor this implies that no processor is re-fining an element and that no processor is waiting for an acknowledgment.Hence it is safe to terminate the refine-ment.then broadcasts this fact to all the other proces-sors.6.Properties of Meshes Refined in ParallelOur parallel refinement algorithm is guaranteed to ter-minate.In every serial phase the longest edge bisectionLet be a set of elements to be refinedwhile there is an element dobisect by its longest edgeinsert any non-conforming element intoend whileFigure6:General longest-edge bisection(GLB)algorithm.algorithm is used.In this algorithm the refinement prop-agates towards progressively longer edges and will even-tually reach the longest edge in each processor.Between processors the refinement also propagates towards longer edges.Global termination is detected by using the global termination detection procedure described in the previous section.The resulting mesh is conforming.Every time a new vertex is created over a shared edge,the refinement propagates to adjacent processors.Because every element is always bisected by its longest edge,for triangular meshes the results by Rosenberg and Stenger on the size of the min-imum angle of two-dimensional meshes also hold.It is not immediately obvious if the resulting meshes obtained by the serial and parallel longest edge bisection al-gorithms are the same or if different partitions of the mesh generate the same refined mesh.As we mentioned earlier, messages can arrive from different sources in different or-ders and elements may be selected for refinement in differ-ent sequences.We now show that the meshes that result from refining a set of elements from a given mesh using the serial and parallel algorithms described in Sections4and5,re-spectively,are the same.In this proof we use the general longest-edge bisection(GLB)algorithm outlined in Figure 6where the order in which elements are refined is not spec-ified.In a parallel environment,this order depends on the partition of the mesh between processors.After showing that the resulting refined mesh is independent of the order in which the elements are refined using the serial GLB al-gorithm,we show that every possible distribution of ele-ments between processors and every order of parallel re-finement yields the same mesh as would be produced by the serial algorithm.Theorem6.1The mesh that results from the refinement of a selected set of elements of a given mesh using the GLB algorithm is independent of the order in which the elements are refined.Proof:An element is refined using the GLBalgorithm if it is in the initial set or refinementpropagates to it.An element is refinedif one of its neighbors creates a non-conformingvertex at the midpoint of one of its edges.Therefinement of by its longest edge divides theelement into two nested subelements andcalled the children of.These children are inturn refined by their longest edge if one of their edges is non-conforming.The refinement proce-dure creates a forest of trees of nested elements where the root of each tree is an element in theinitial mesh and the leaves are unrefined ele-ments.For every element,let be the refinement tree of nested elements rooted atwhen the refinement procedure terminates. Using the GLB procedure elements can be se-lected for refinement in different orders,creating possible different refinement histories.To show that this cannot happen we assume the converse, namely,that two refinement histories and generate different refined meshes,and establish a contradiction.Thus,assume that there is an ele-ment such that the refinement trees and,associated with the refinement histories and of respectively,are different.Be-cause the root of and is the same in both refinement histories,there is a place where both treesfirst differ.That is,starting at the root,there is an element that is common to both trees but for some reason,its children are different.Be-cause is always bisected by the longest edge, the children of are different only when is refined in one refinement history and it is not re-fined in the other.In other words,in only one of the histories does have children.Because is refined in only one refinement his-tory,then,the initial set of elements to refine.This implies that must have been refined because one of its edges became non-conforming during one of the refinement histo-ries.Let be the set of elements that are present in both refinement histories,but are re-fined in and not in.We define in a similar way.For each refinement history,every time an ele-ment is refined,it is assigned an increasing num-ber.Select an element from either or that has the lowest number.Assume that we choose from so that is refined in but not in.In,is refined because a neigh-boring element created a non-conforming ver-tex at the midpoint of their shared edge.There-fore is refined in but not in because otherwise it would cause to be refined in both sequences.This implies that is also in and has a lower refinement number than con-。

2005-A global Malmquist productivity index

2005-A global Malmquist productivity index

A global Malmquist productivity indexJesu ´s T.Pastor a ,C.A.Knox Lovell b ,TaCentro de Investigacio ´n Operativa,Universidad Miguel Herna ´ndez,03206Elche (Alicante),SpainbDepartment of Economics,University of Georgia,Athens,GA 30602,USA Received 2June 2004;received in revised form 24January 2005;accepted 16February 2005Available online 23May 2005AbstractThe geometric mean Malmquist productivity index is not circular,and its adjacent period components can provide different measures of productivity change.We propose a global Malmquist productivity index that is circular,and that gives a single measure of productivity change.D 2005Elsevier B.V .All rights reserved.Keywords:Malmquist productivity index;Circularity JEL classification:C43;D24;O471.IntroductionThe geometric mean form of the contemporaneous Malmquist productivity index,introduced by Caves et al.(1982),is not circular.Whether this is a serious problem depends on the powers of persuasion of Fisher (1922),who dismissed the test,and Frisch (1936),who endorsed it.The index averages two possibly disparate measures of productivity change.Fa ¨re and Grosskopf (1996)state sufficient conditions on the adjacent period technologies for the index to satisfy circularity,and to average the same measures of productivity change.When linear programming techniques are used to compute and decompose the index,infeasibility can occur.Whether this is a serious problem depends on0165-1765/$-see front matter D 2005Elsevier B.V .All rights reserved.doi:10.1016/j.econlet.2005.02.013T Corresponding author.Tel.:+17065423689;fax:+17065423376.E-mail address:knox@ (C.A.K.Lovell).Economics Letters 88(2005)266–271/locate/econbasethe structure of the data.Xue and Harker(2002)provide necessary and sufficient conditions on the datafor LP infeasibility not to occur.We demonstrate that the source of all three problems is the specification of adjacent periodtechnologies in the construction of the index.We show that it is possible to specify a base periodtechnology in a way that solves all three problems,without having to impose restrictive conditions oneither the technologies or the data.Berg et al.(1992)proposed an index that compares adjacent period data using technology from a baseperiod.This index satisfies circularity and generates a single measure of productivity change,but it paysfor circularity with base period dependence,and it remains susceptible to LP infeasibility.Shestalova(2003)proposed an index having as its base a sequential technology formed from data ofall producers in all periods up to and including the two periods being compared.This index is immune toLP infeasibility,and it generates a single measure of productivity change,but it fails circularity and itprecludes technical regress.Thus no currently available Malmquist productivity index solves all three problems.We propose anew global index with technology formed from data of all producers in all periods.This index satisfiescircularity,it generates a single measure of productivity change,it allows technical regress,and it isimmune to LP infeasibility.In Section2we introduce and decompose the circular global index.Its efficiency change componentis the same as that of the contemporaneous index,but its technical change component is new.In Section3we relate it to the contemporaneous index.In Section4we provide an empirical illustration.Section5concludes.2.The global Malmquist productivity indexConsider a panel of i=1,...,I producers and t=1,...,T time periods.Producers use inputs x a R N+toproduce outputs y a R P+.We define two technologies.A contemporaneous benchmark technology isdefined as T c t={(x t,y t)|x t can produce y t}with k T c t=T c t,t=1,...,T,k N0.A global benchmarktechnology is defined as T c G=conv{T c1v...v T c T}.The subscript b c Q indicates that both benchmark technologies satisfy constant returns to scale.A contemporaneous Malmquist productivity index is defined on T c s asM scx t;y t;x tþ1;y tþ1ÀÁ¼D scx tþ1;y tþ1ðÞD scx t;y tðÞ;ð1Þwhere the output distance functions D c s(x,y)=min{/N0|(x,y//)a T c s},s=t,t+1.Since M c t(x t,y t,x t+1, y t+1)p M c t+1(x t,y t,x t+1,y t+1)without restrictions on the two technologies,the contemporaneous index is typically defined in geometric mean form as M c(x t,y t,x t+1,y t+1)=[M c t(x t,y t,x t+1,y t+1)ÂM c t+1(x t,y t,x t+1, y t+1)]1/2.A global Malmquist productivity index is defined on T c G asM Gcx t;y t;x tþ1;y tþ1ÀÁ¼D Gcx tþ1;y tþ1ðÞD Gcx t;y tðÞ;ð2Þwhere the output distance functions D c G(x,y)=min{/N0|(x,y//)a T c G}.J.T.Pastor,C.A.K.Lovell/Economics Letters88(2005)266–271267Both indexes compare (x t +1,y t +1)to (x t ,y t ),but they use different benchmarks.Since there is only one global benchmark technology,there is no need to resort to the geometric mean convention when defining the global index.M cGdecomposes as M G c x t ;y t ;x t þ1;f y t þ1ÀÁ¼D t þ1c x t þ1;y t þ1ðÞD t c x t ;y t ðÞÂD G c x t þ1;y t þ1ðÞD t þ1c x t þ1;y t þ1ðÞÂD t cx t ;y t ðÞD Gc x t ;y t ðÞ&'¼TE t þ1c x t þ1;y t þ1ðÞTE t c x t ;y t ðÞÂD G c Àx t þ1;y t þ1=D t þ1c x t þ1;y t þ1ðÞÁD G c x t ;y t =D t cx t ;y t ðÞÀÁ()¼EC c ÂBPG G ;t þ1cx t þ1;y t þ1ðÞBPG cx t ;y tðÞ()¼EC c ÂBPC c ;ð3Þwhere EC c is the usual efficiency change indicator and BPG c G,s V 1is a best practice gap between T c Gand T c s measured along rays (x s ,y s),s =t ,t +1.BPC c is the change in BPG c ,and provides a new measure of technical change.BPC c f 1indicates whether the benchmark technology in period t +1in the region[(x t +1,y t +1/D ct +1(x t +1,y t +1))]is closer to or farther away from the global benchmark technology than is the benchmark technology in period t in the region [(x t ,y t /D ct (x t ,y t ))].M c G has four virtues.First,like any fixed base index,M cGis circular,and since EC c is circular,so is BPC c .Second,each provides a single measure,with no need to take the geometric mean of disparate adjacent period measures.Third,but not shown here,the decomposition in (3)can be extended to generate a three-way decomposition that is structurally identical to the Ray and Desli (1997)decomposition of the contemporaneous index.M cGand M c share a common efficiency change component,but they have different technical change and scale components,and so M c Gp M c without restrictions on the technologies.Finally,the technical change and scale components of M c Gare immune to the LP infeasibility problem that plagues these components of M c .paring the global and contemporaneous indexes The ratioM G c =M c¼M G c =M t þ1cÀÁÂM G c =M t cÀÁÂÃ1=2¼D G cx t þ1;y t þ1=D t þ1c x t þ1;y t þ1ðÞÀÁD G c x t ;y t =D t þ1c x t ;y t ðÞÀÁ"#ÂD G c x t þ1;y t þ1=D t c x t þ1;y t þ1ðÞÀÁD G c x t ;y t =D t c x t ;y t ðÞÀÁ"#()1=2¼BPG G ;t þ1cx t þ1;y t þ1ðÞBPG G ;t þ1cx t ;y tðÞ"#ÂBPG G ;t c xt þ1;y t þ1ðÞBPG G ;t c x t ;y tðÞ"#()1=2ð4Þis the geometric mean of two terms,each being a ratio of benchmark technology gaps along differentrays.M c G /M c f 1as projections onto T c t and T c t +1of period t +1data are closer to,equidistant from,orfarther away from T c G than projections onto T c t and T ct +1of period t data are.J.T.Pastor,C.A.K.Lovell /Economics Letters 88(2005)266–271268J.T.Pastor,C.A.K.Lovell/Economics Letters88(2005)266–271269 Table1Electricity generation data,annual means1977198219871992 Output(000MW h)13,70013,86016,18017,270 Labor(#FTE)1373179719952021 Fuel(billion BTU)1288144116671824 Capital(To¨rnqvist)44,756211,622371,041396,386 M c G=M c if BPG c G,s(x t+1,y t+1)=BPG c G,s(x t,y t),s=t,t+1.From the first equality in(4),this condition is equivalent to the condition M c G=M c s,s=t,t+1.If this condition holds for all s,it is equivalent to the condition M c t=M c1for all t.Althin(2001)has shown that a sufficient condition for base period independence is that technical change be Hicks output-neutral(HON).Hence HON is also sufficient for M c G=M c.4.An empirical illustrationWe summarize an application intended to illustrate the behavior of M c G,and to compare its performance with that of M c.We analyze a panel of93US electricity generating firms in four years (1977,1982,1997,1992).The firms use labor(FTE employees),fuel(BTUs of energy)and capital(a multilateral To¨rnqvist index)to generate electricity(net generation in MW h).The data are summarized in Table1.Electricity generation increased by proportionately less than each input did.The main cause of the rapid increase in the capital input was the enactment of environmental regulations mandating the installation of pollution abatement equipment.We are unable to disaggregate the capital input into its productive and abatement components.Empirical findings are summarized in Table2.The first three rows report decomposition(3)of M c G, and the final three rows report M c and its two adjacent period components.Columns correspond to time periods.M c G shows a large productivity decline from1977to1982,followed by weak productivity growth. Cumulative productivity in1992was25%lower than in1977.M c G calculated using1992and1977data generates the same value,verifying that it is circular.The efficiency change component EC c of M c G(and M c)is also circular,and cumulates to an18% improvement.Best practice change,BPC c,is also circular,and declined by35%.Capital investment in Table2Global and contemporaneous Malmquist productivity indexes1977–19821982–19871987–1992Cumulative productivity1977–1992 M c G0.685 1.064 1.0390.7570.757EC c 1.163 1.0890.929 1.176 1.176 BPC c0.5890.977 1.1180.6440.644M c0.4310.895 1.0390.4000.592M c t0.7130.902 1.0530.678 1.333M c t+10.2600.887 1.0240.2360.263pollution abatement equipment generated cleaner air but not more electricity.Consequently catching up with deteriorating best practice was relatively easy.Turning to the contemporaneous index M c reported in the final three rows,the story is not so clear.Cumulative productivity in 1992was 60%lower than in 1977.However calculating M c using 1992and 1977data generates a smaller 40%decline,verifying that M c is not circular.Neither figure is close to the25%decline reported by M cG,verifying that technical change was not HON,but (pollution abatement)capital-using.The lack of circularity is reflected in the frequently large differences between M ct and M c t +1,which give conflicting signals when computed using 1992and 1977data,with M c tsignaling productivitygrowth and M ct +1signaling productivity decline.Although not reported in Table 2,we have calculated three-way decompositions of M cG and M c .All three components of M c G are circular,and LP infeasibility does not occur.In contrast,the technical change and scale components of M c are not circular,and infeasibility occurs for 13observations.The circular global index M cGtells a single story about productivity change,and its decomposition is intuitively appealing in light of what we know about the industry during the cking circularity,M c and its two adjacent period components tell different stories that are often contradictory.Thedifferences between M cGand M c are a consequence of the capital-using bias of technical change,which was regressive due to the mandated installation of pollution abatement equipment,augmented perhaps by the rate base padding that was prevalent during the period.5.ConclusionsThe contemporaneous Malmquist productivity index is not circular,its adjacent period components can give conflicting signals,and it is susceptible to LP infeasibility.The global Malmquist productivity index and each of its components is circular,it provides single measures of productivity change and its components,and it is immune to LP infeasibility.The global index decomposes into the same sources of productivity change as the contemporaneous index does.A sufficient condition for equality of the two indexes,and their respective components,is Hicks output neutrality of technical change.The global index must be recomputed when a new time period is incorporated.Diewert’s (1987)assertion that b ...economic history has to be rewritten ...Q when new data are incorporated is the base period dependency problem revisited.The problem can be serious when using base periods t =1and t =T ,but it is likely to be benign when using global base periods {1,...,T }and {1,...,T +1}.While new data may change the global frontier,the rewriting of history is likely to be quantitative rather than qualitative.ReferencesAlthin,R.,2001.Measurement of productivity changes:two Malmquist index approaches.Journal of Productivity Analysis 16,107–128.Berg,S.A.,Førsund,F.R.,Jansen,E.S.,1992.Malmquist indices of productivity growth during the deregulation of Norwegian banking,1980–89.Scandinavian Journal of Economics 94,211–228(Supplement).Caves,D.W.,Christensen,L.R.,Diewert,W.E.,1982.The economic theory of index numbers and the measurement of input output,and productivity.Econometrica 50,1393–1414.J.T.Pastor,C.A.K.Lovell /Economics Letters 88(2005)266–271270J.T.Pastor,C.A.K.Lovell/Economics Letters88(2005)266–271271 Diewert,W.E.,1987.Index numbers.In:Eatwell,J.,Milgate,M.,Newman,P.(Eds.),The New Palgrave:A Dictionary of Economics,vol.2.The Macmillan Press,New York.Fa¨re,R.,Grosskopf,S.,1996.Intertemporal Production Frontiers:With Dynamic DEA.Kluwer Academic Publishers,Boston. Fisher,I.,1922.The Making of Index Numbers.Houghton Mifflin,Boston.Frisch,R.,1936.Annual survey of general economic theory:the problem of index numbers.Econometrica4,1–38.Ray,S.C.,Desli,E.,1997.Productivity growth,technical progress,and efficiency change in industrialized countries:comment.American Economic Review87,1033–1039.Shestalova,V.,2003.Sequential Malmquist indices of productivity growth:an application to OECD industrial activities.Journal of Productivity Analysis19,211–226.Xue,M.,Harker,P.T.,2002.Note:ranking DMUs with infeasible super-efficiency in DEA models.Management Science48, 705–710.。

计量经济学中英文词汇对照

计量经济学中英文词汇对照

Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covaห้องสมุดไป่ตู้iance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Asymmetric distribution Asymptotic bias Asymptotic efficiency Asymptotic variance Attributable risk Attribute data Attribution Autocorrelation Autocorrelation of residuals Average Average confidence interval length Average growth rate BBB Bar chart Bar graph Base period Bayes' theorem Bell-shaped curve Bernoulli distribution Best-trim estimator Bias Binary logistic regression Binomial distribution Bisquare Bivariate Correlate Bivariate normal distribution Bivariate normal population Biweight interval Biweight M-estimator Block BMDP(Biomedical computer programs) Boxplots Breakdown bound CCC Canonical correlation Caption Case-control study Categorical variable Catenary Cauchy distribution Cause-and-effect relationship Cell Censoring

three factors Linguistic Inquiry, Volume 36, Number 1, Winter 2005

three factors Linguistic Inquiry, Volume 36, Number 1, Winter 2005

Three Factors in LanguageDesignNoam ChomskyThe biolinguistic perspective regards the language faculty as an ‘‘organof the body,’’along with other cognitive systems.Adopting it,weexpect to find three factors that interact to determine (I-)languagesattained:genetic endowment (the topic of Universal Grammar),experi-ence,and principles that are language-or even organism-independent.Research has naturally focused on I-languages and UG,the problemsof descriptive and explanatory adequacy.The Principles-and-Param-eters approach opened the possibility for serious investigation of thethird factor,and the attempt to account for properties of language interms of general considerations of computational efficiency,eliminat-ing some of the technology postulated as specific to language andproviding more principled explanation of linguistic phenomena.Keywords:minimalism,principled explanation,Extended StandardTheory,Principles-and-Parameters,internal/external Merge,single-cycle derivation,phaseThirty years ago,in 1974,an international meeting took place at MIT,in cooperation with the Royaumont Institute in Paris,on the topic of ‘‘biolinguistics,’’a term suggested by the organizer,Massimo Piattelli-Palmarini,and the title of a recent book surveying the field and proposing new directions by Lyle Jenkins (2002).1This was only one of many such interactions in those years,including interdisciplinary seminars and international conferences.The biolinguistic perspective began to take shape over 20years before in discussions among a few graduate students who were much influenced by developments in biology and mathematics in the early postwar years,including work in ethology that was just coming to be known in the United States.One of them was Eric Lenneberg,whose seminal 1967study Biological Foundations of Language remains a basic document of the field.Many of the leading questions discussed at the 1974conference,and in the years leading up to it,remain very much alive today.One of these questions,repeatedly brought up in the conference as ‘‘one of the basic questions to be asked from the biological point of view,’’is the extent to which apparent principles of language,including some that had only recently come to light,are unique to this cognitive system or whether similar ‘‘formal arrangements’’are found in other cognitive domains in humans or This article is expanded from a talk presented at the annual meeting of the Linguistic Society of America,9January 2004.Thanks to Cedric Boeckx,Samuel David Epstein,Robert Freidin,Lyle Jenkins,Howard Lasnik,and Luigi Rizzi,among others,for comments on an earlier draft.1The conference,titled ‘‘A Debate on Bio-Linguistics,’’was held at Endicott House,Dedham,Massachusetts,20–21May 1974,and organized by the Centre Royaumont pour une science de l’homme,Paris.1Linguistic Inquiry,Volume 36,Number 1,Winter 20051–22᭧2005by the Massachusetts Institute of Technology2N O A M C H O M S K Yother organisms.An even more basic question from the biological point of view is how much of language can be given a principled explanation,whether or not homologous elements can be found in other domains or organisms.The effort to sharpen these questions and to investigate them for language has come to be called the‘‘Minimalist Program’’in recent years,but the questions arise for any biological system and are independent of theoretical persuasion,in linguis-tics and elsewhere.Answers to these questions are fundamental not only to understanding the nature and functioning of organisms and their subsystems,but also to investigating their growth and evolution.For any biological system,language included,the only general question that arises about the program is whether it can be productively pursued or is premature.In these remarks,I will try to identify what seem to me some of the significant themes in the past half-century of inquiry into problems of biolinguistics and to consider their current status. Several preliminary qualifications should be obvious.One is that the picture is personal;others would no doubt make different choices.A second is that things often seem clearer in retrospect than at the time,so there is some anachronism in this account,but not I think too much.A third is that I cannot even begin to mention the contributions of a great many people to the collective enterprise,particularly as the related fields have expanded enormously in the years since the1974 conference.The biolinguistic perspective views a person’s language as a state of some component of the mind,understanding‘‘mind’’in the sense of eighteenth-century scientists who recognized that after Newton’s demolition of the only coherent concept of body,we can only regard aspects of the world‘‘termed mental’’as the result of‘‘such an organical structure as that of the brain’’(Joseph Priestley).Among the vast array of phenomena that one might loosely consider language-related,the biolinguistic approach focuses attention on a component of human biology that enters into the use and acquisition of language,however one interprets the term‘‘language.’’Call it the ‘‘faculty of language,’’adapting a traditional term to a new usage.This component is more or less on a par with the systems of mammalian vision,insect navigation,and others.In many of these cases,the best available explanatory theories attribute to the organism computational systems and what is called‘‘rule-following’’in informal usage—for example,when a recent text on vision presents the so-called rigidity principle as it was formulated50years ago:‘‘if possible,and other rules permit,interpret image motions as projections of rigid motions in three dimensions’’(Hoffman1998:169).In this case,later work provided substantial insight into the mental computa-tions that seem to be involved when the visual system follows these rules,but even for very simple organisms,that is typically no slight task,and relating mental computations to analysis at the cellular level is commonly a distant goal.Adopting this conception,a language is a state of the faculty of language,an I-language,in technical usage.The decision to study language as part of the world in this sense was regarded as highly controversial at the time,and still is.A more careful look will show,I think,that the arguments advanced against the legitimacy of the approach have little force(a weak thesis)and that its basic assumptions are tacitly adopted even by those who strenuously reject them,and indeed must be, even for coherence(a much stronger thesis).I will not enter into this interesting chapter ofT H R E E F A C T O R S I N L A N G U A G E D E S I G N3 contemporary intellectual history here,but will simply assume that crucial aspects of language can be studied as part of the natural world,adopting the biolinguistic approach that took shape half a century ago and that has been intensively pursued since,along different paths.The language faculty is one component of what the cofounder of modern evolutionary theory, Alfred Russel Wallace,called‘‘man’s intellectual and moral nature’’:the human capacities for creative imagination,language and symbolism generally,mathematics,interpretation and record-ing of natural phenomena,intricate social practices,and the like,a complex of capacities that seem to have crystallized fairly recently,perhaps a little over50,000years ago,among a small breeding group of which we are all descendants—a complex that sets humans apart rather sharply from other animals,including other hominids,judging by traces they have left in the archaeological record.The nature of the‘‘human capacity,’’as some researchers now call it,remains a considera-ble mystery.It was one element of a famous disagreement between the two founders of the theory of evolution,with Wallace holding,contrary to Darwin,that evolution of these faculties cannot be accounted for in terms of variation and natural selection alone,but requires‘‘some other influence,law,or agency,’’some principle of nature alongside gravitation,cohesion,and other forces without which the material universe could not exist.Although the issues are framed differ-ently today within the core biological sciences,they have not disappeared(see Wallace1889: chap.15,Marshack1985).It is commonly assumed that whatever the human intellectual capacity is,the faculty of language is essential to it.Many scientists agree with paleoanthropologist Ian Tattersall,who writes that he is‘‘almost sure that it was the invention of language’’that was the‘‘sudden and emergent’’event that was the‘‘releasing stimulus’’for the appearance of the human capacity in the evolutionary record—the‘‘great leap forward’’as Jared Diamond called it,the result of some genetic event that rewired the brain,allowing for the origin of modern language with the rich syntax that provides a multitude of modes of expression of thought,a prerequisite for social development and the sharp changes of behavior that are revealed in the archaeological record, also generally assumed to be the trigger for the rapid trek from Africa,where otherwise modern humans had apparently been present for hundreds of thousands of years(Tattersall1998:24–25; see also Wells2002).Tattersall takes language to be‘‘virtually synonymous with symbolic thought.’’Elaborating,one of the initiators of the Royaumont-MIT symposia,Franc¸ois Jacob, observed that‘‘the role of language as a communication system between individuals would have come about only secondarily,as many linguists believe’’(1982:59),perhaps referring to discus-sions at the symposia,where the issue repeatedly arose,among biologists as well.In the1974 conference,his fellow Nobel laureate Salvador Luria was the most forceful advocate of the view that communicative needs would not have provided‘‘any great selective pressure to produce a system such as language,’’with its crucial relation to‘‘development of abstract or productive thinking’’(Luria1974:195).‘‘The quality of language that makes it unique does not seem to be so much its role in communicating directives for action’’or other common features of animal communication,Jacob continued,but rather‘‘its role in symbolizing,in evoking cognitive im-ages,’’in‘‘molding’’our notion of reality and yielding our capacity for thought and planning, through its unique property of allowing‘‘infinite combinations of symbols’’and therefore‘‘mental4N O A M C H O M S K Ycreation of possible worlds,’’ideas that trace back to the seventeenth-century cognitive revolution (1982:59).Jacob also stressed the common understanding that answers to questions about evolu-tion‘‘in most instances...can hardly be more than more or less reasonable guesses’’(1982: 31).We can add another insight of seventeenth-and eighteenth-century philosophy:that even the most elementary concepts of human language do not relate to mind-independent objects by means of some reference-like relation between symbols and identifiable physical features of the external world,as seems to be universal in animal communication systems.Rather,they are creations of the‘‘cognoscitive powers’’that provide us with rich means to refer to the outside world from certain perspectives,but are individuated by mental operations that cannot be reduced to a‘‘peculiar nature belonging’’to the thing we are talking about,as Hume summarized a century of inquiry.Those are critical observations about the elementary semantics of natural language, suggesting that its most primitive elements are related to the mind-independent world much as the internal elements of phonology are,not by a reference-like relation but as part of a considerably more intricate species of conception and action.It is for reasons such as these,though not clearly grasped at the time,that the early work in the1950s adopted a kind of‘‘use theory of meaning,’’pretty much in the sense of John Austin and the later Wittgenstein:language was conceived as an instrument put to use for various human purposes,generating expressions including arrange-ments of the fundamental elements of the language,with no grammatical-ungrammatical divide, each basically a complex of instructions for use(see Chomsky1955,hereafter LSLT).2 If this much is generally on the right track,then at least two basic problems arise when we consider the origins of the faculty of language and its role in the sudden emergence of the human intellectual capacity:first,the core semantics of minimal meaning-bearing elements,including the simplest of them;and second,the principles that allow infinite combinations of symbols, hierarchically organized,which provide the means for use of language in its many aspects.Accord-ingly,the core theory of language—Universal Grammar(UG)—must provide,first,a structured inventory of possible lexical items that are related to or perhaps identical with the concepts that are the elements of the‘‘cognoscitive powers,’’sometimes now regarded as a‘‘language of thought’’along lines developed by Jerry Fodor(1975);and second,means to construct from these lexical items the infinite variety of internal structures that enter into thought,interpretation, planning,and other human mental acts,and that are sometimes put to use in action,including the externalization that is a secondary process if the speculations just reviewed turn out to be correct.On the first problem,the apparently human-specific conceptual-lexical apparatus,there is important work on relational notions linked to syntactic structures and on the partially mind-internal objects that appear to play a critical role(events,propositions,etc.).3But there is little beyond descriptive remarks on the core referential apparatus that is used to talk about the world. The second problem has been central to linguistic research for half a century,with a long history before in different terms.2For later discussion,see among others Chomsky1966,2001b,McGilvray1999,Antony and Hornstein2003.3For insightful review and original analysis,see Borer2004a,b.T H R E E F A C T O R S I N L A N G U A G E D E S I G N5 The biolinguistic approach adopted from the outset the point of view that C.R.Gallistel (1997)calls‘‘the norm these days in neuroscience’’(p.86),the‘‘modular view of learning’’: the conclusion that in all animals,learning is based on specialized mechanisms,‘‘instincts to learn’’(p.82)in specific ways.We can think of these mechanisms as‘‘organs within the brain’’(p.86),achieving states in which they perform specific kinds of computation.Apart from‘‘ex-tremely hostile environments’’(p.88),they change states under the triggering and shaping effect of external factors,more or less reflexively,and in accordance with internal design.That is the ‘‘process of learning’’(Gallistel1997,1999),though‘‘growth’’might be a more appropriate term,avoiding misleading connotations of the term‘‘learning.’’The modular view of learning of course does not entail that the component elements of the module are unique to it:at some level,everyone assumes that they are not—the cellular level,for example—and the question of the level of organization at which unique properties emerge remains a basic one from a biological point of view,as it was at the1974conference.Gallistel’s observations recall the concept of‘‘canalization’’introduced into evolutionary and developmental biology by C.H.Waddington over60years ago,referring to processes‘‘adjusted so as to bring about one definite end result regardless of minor variations in conditions during the course of the reaction,’’thus ensuring‘‘the production of the normal,that is optimal type in the face of the unavoidable hazards of existence’’(Waddington1942).That seems to be a fair description of the growth of language in the individual.A core problem of the study of the faculty of language is to discover the mechanisms that limit outcomes to‘‘optimal types.’’It has been recognized since the origins of modern biology that such constraints enter not only into the growth of organisms but also into their evolution,with roots in the earlier tradition that Stuart Kauffman calls‘‘rational morphology’’(1993:3–5).4In a classic contemporary paper, John Maynard Smith and associates trace the post-Darwinian reformulation back to Thomas Huxley,who was struck by the fact that there appear to be‘‘predetermined lines of modification’’that lead natural selection to‘‘produce varieties of a limited number and kind’’for every species (Maynard Smith et al.1985:266).5They review a variety of such constraints in the organic world and describe how‘‘limitations on phenotypic variability’’are‘‘caused by the structure,character, composition,or dynamics of the developmental system,’’pointing out also that such‘‘develop-mental constraints...undoubtedly play a significant role in evolution’’though there is yet‘‘little agreement on their importance as compared with selection,drift,and other such factors in shaping evolutionary history’’(p.265).At about the same time,Jacob wrote that‘‘the rules controlling embryonic development,’’almost entirely unknown,interact with other constraints imposed by general body plan,mechanical properties of building materials,and other factors in‘‘restricting possible changes of structures and functions’’in evolutionary development(1982:21),providing ‘‘architectural constraints’’that‘‘limit adaptive scope and channel evolutionary patterns’’(Erwin 2003:1683).The best-known of the figures who devoted much of their work to these topics are 4For comment in a linguistic context,see Boeckx and Hornstein2003.For more general discussion,see Jenkins 2000.5For review of some of these topics,see Stewart1998.6N O A M C H O M S K YD’Arcy Thompson and Alan Turing,who took a very strong view on the central role of such factors in biology.In recent years,such considerations have been adduced for a wide range of problems of development and evolution,from cell division in bacteria to optimization of structure and function of cortical networks,even to proposals that organisms have‘‘the best of all possible brains,’’as argued by computational neuroscientist Christopher Cherniak(1995:522).6The prob-lems are at the border of inquiry,but their significance is not controversial.Assuming that the faculty of language has the general properties of other biological systems, we should,therefore,be seeking three factors that enter into the growth of language in the indi-vidual:1.Genetic endowment,apparently nearly uniform for the species,which interprets part ofthe environment as linguistic experience,a nontrivial task that the infant carries out reflexively,and which determines the general course of the development of the language faculty.Among the genetic elements,some may impose computational limitations that disappear in a regular way through genetically timed maturation.Kenneth Wexler and his associates have provided compelling evidence of their existence in the growth of language,thus providing empirical evidence for what Wexler(to appear)calls‘‘Lenne-berg’s dream.’’2.Experience,which leads to variation,within a fairly narrow range,as in the case of othersubsystems of the human capacity and the organism generally.3.Principles not specific to the faculty of language.The third factor falls into several subtypes:(a)principles of data analysis that might be used in language acquisition and other domains;(b)principles of structural architecture and developmental constraints that enter into canalization,organic form,and action over a wide range,including principles of efficient computation,which would be expected to be of particular significance for computational systems such as language.It is the second of these subcategories that should be of particular significance in determining the nature of attainable languages.Those exploring these questions50years ago assumed that the primitive step of analysis of linguistic experience would be feature-based phonetic analysis,along lines described by Roman Jakobson and his associates(see Jakobson,Fant,and Halle1953).We also tried to show that basic prosodic properties reflect syntactic structure that is determined by other principles,including crucially a principle of cyclic computation that was extended much more generally in later years (see Chomsky,Halle,and Lukoff1956).The primitive principles must also provide what George Miller called‘‘chunking,’’identification of phonological words in the string of phonetic units.In LSLT(p.165),I adopted Zellig Harris’s(1955)proposal,in a different framework,for identifying morphemes in terms of transitional probabilities,though morphemes do not have the required beads-on-a-string property.The basic problem,as noted in LSLT,is to show that such statistical 6See also Laughlin and Sejnowski2003,Cherniak et al.2004,and Physics News Update2001reporting Howard, Rutenberg,and de Vet2001.T H R E E F A C T O R S I N L A N G U A G E D E S I G N7 methods of chunking can work with a realistic corpus.That hope turns out to be illusory,as has recently been shown by Thomas Gambell and Charles Yang(2003),who go on to point out that the methods do,however,give reasonable results if applied to material that is preanalyzed in terms of the apparently language-specific principle that each word has a single primary stress.If so,then the early steps of compiling linguistic experience might be accounted for in terms of general principles of data analysis applied to representations preanalyzed in terms of principles specific to the language faculty,the kind of interaction one should expect among the three factors.In LSLT,it was assumed that the next step would be assignment of chunked items to syntactic categories,again by general principles of data analysis.A proposal with an information-theoretic flavor was tried by hand calculations in that precomputer age,with suggestive results,but the matter has never been pursued,to my knowledge.Surely what are called‘‘semantic properties’’are also involved,but these involve nontrivial problems at the most elementary level,as mentioned earlier.The assumption of LSLT was that higher levels of linguistic description,including mor-phemes,are determined by a general format for rule systems provided by UG,with selection among them in terms of a computational procedure that seeks the optimal instantiation,a notion defined in terms of UG principles of significant generalization.Specific proposals were made then and in the years that followed.In principle,they provided a possible answer to what came to be called the‘‘logical problem of language acquisition,’’but they involved astronomical calcula-tion and therefore did not seriously address the issues.The main concerns in those years were quite different,as they still are.It may be hard to believe today,but it was commonly assumed50years ago that the basic technology of linguistic description was available and that language variation was so free that nothing of much generality was likely to be discovered.As soon as efforts were made to provide fairly explicit accounts of the properties of languages,however,it became obvious how little was known,in any domain. Every specific proposal yielded a treasure trove of counterevidence,requiring complex and varied rule-systems even to achieve a very limited approximation to descriptive adequacy.That was highly stimulating for inquiry into language,but it also left a serious quandary,since the most elementary considerations led to the conclusion that UG must impose narrow constraints on possible outcomes—sometimes called‘‘poverty of stimulus’’problems in the study of language, though the term is misleading because this is just a special case of basic issues that arise universally for organic growth.A number of paths were pursued to try to resolve the tension.The most successful turned out to be efforts to formulate general principles,attributed to UG—that is,the genetic endow-ment—leaving a somewhat reduced residue of phenomena that would result,somehow,from experience.Early proposals were the A-over-A Principle,conditions on wh-extraction from wh-phrases(relatives and interrogatives),simplification of T-markers to base recursion(following observations by Charles Fillmore)and cyclicity(an intricate matter,as shown in an important paper of Robert Freidin’s(1978)and insightfully reviewed in a current paper of Howard Lasnik’s (to appear)which shows that many central questions remain unanswered),later John Robert Ross’s(1967)classic study of taxonomy of islands that still remains a rich store of ideas and observations to explore,then attempts to reduce islands to such properties as locality and structure8N O A M C H O M S K Ypreservation,and so on.These approaches had some success,but the basic tensions remained unresolved at the time of the1974conference.Within a few years,the landscape had changed considerably.In part this was because of great progress in areas that had hitherto been explored only in limited ways,including truth-and model-theoretic semantics and prosodic structures.In part it was the result of a vast array of new materials from studies of much greater depth than previously undertaken,and into a much wider variety of languages,much of it traceable to Richard Kayne’s work and his lectures in Europe, which inspired far-reaching inquiry into Romance and Germanic languages,later other languages, also leading to many fruitful ideas about the principles of UG.About25years ago,much of this work crystallized in a radically different approach to UG,the Principles-and-Parameters(P&P) framework,which for the first time offered the hope of overcoming the tension between descriptive and explanatory adequacy.This approach sought to eliminate the format framework entirely,and with it,the traditional conception of rules and constructions that had been pretty much taken over into generative grammar.That much is familiar,as is the fact that the new P&P framework led to an explosion of inquiry into languages of the most varied typology,yielding new problems previously not envisioned,sometimes answers,and the reinvigoration of neighboring disciplines concerned with acquisition and processing,their guiding questions reframed in terms of parameter setting within a fixed system of principles of UG with at least visible contours.Alternative paths, variously interrelated,were leading in much the same direction,including Michael Brody’s highly illuminating work(1995,2003).No one familiar with the field has any illusion today that the horizons of inquiry are even visible,let alone at hand,in any domain.Abandonment of the format framework also had a significant impact on the biolinguistic program.If,as had been assumed,acquisition is a matter of selection among options made available by the format provided by UG,then the format must be rich and highly articulated,allowing relatively few options;otherwise,explanatory adequacy is out of reach.The best theory of lan-guage must be a very unsatisfactory one from other points of view,with a complex array of conditions specific to human language,restricting possible instantiations.The only plausible theories had to impose intricate constraints on the permissible relations between sound and mean-ing,all apparently specific to the faculty of language.The fundamental biological issue of princi-pled explanation could barely be contemplated,and correspondingly,the prospects for serious inquiry into evolution of language were dim;evidently,the more varied and intricate the conditions specific to language,the less hope there is for a reasonable account of the evolutionary origins of UG.These are among the questions that were raised at the1974symposium and others of the period,but they were left as apparently irresoluble problems.The P&P framework offered prospects for resolution of these tensions as well.Insofar as this framework proves valid,acquisition is a matter of parameter setting and is therefore divorced entirely from the remaining format for grammar:the principles of UG.There is no longer a conceptual barrier to the hope that the UG might be reduced to a much simpler form,and that the basic properties of the computational systems of language might have a principled explanation instead of being stipulated in terms of a highly restrictive language-specific format for grammars. Within a P&P framework,what had previously been the worst theory—anything goes—might。

Peripheral Nerve Stimulator说明书

Peripheral Nerve Stimulator说明书

Nursing Service GuidelinesGeneralTitle: U SE OF P ERIPHERAL N ERVE S TIMULATOR TO M ONITOR N EUROMUSCULARB LOCKADE (NMBA).Responsibility: RN's caring for patients receiving neuromuscular blocking agents Equipment: 1.Peripheral Nerve Stimulator2.Two gelled electrode pads (such as those used for external cardiac monitoring)Standard of Care: Use of peripheral nerve stimulator (PNS) by train-of-four (TOF) method to determine depth of paralysis of patients receiving non-depolarizing neuromuscular blocking agents.Procedure Point of Emphasis1.Setting up the Peripheral Nerve Stimulator:Apply the two electrodes either at the ulnar nervearea, the facial nerve area or the posterior tibialnerve area. (Please see pictures below to verifyproper placement.)The optimal placement of the electrodes is the ulnar nerve. However, the conduction of the impulses is affected by wounds, edema and invasive lines, and hair, therefore, if any of these are present the facial nerve or the posterior tibial nerve should be usedinstead.Ulnar nerve area placement of electrodes:Place the distal electrode on the skin at the flexor crease on the ulnar surface of the wrist. Placethe second electrode approximately 1-2 cm. proximalto the first, parallel to the flexor carpi ulnaris tendon.Facial nerve area placement of electrodes:Place one electrode on the face at the outer canthus of the eye and the second electrode approximately 2cm below, parallel with the tragus of the ear.Posterior tibial nerve placement of electrodes: Place one electrode approximately 2 cm from the posterior to the medial malleolus in the foot. Place the second electrode approximately 2 cm above the first electrode.NOTE: It is important to carefully place the electrodes, to avoid direct stimulation of the muscle rather than the nerve. If the electrodes are placed on the muscle, it is impossible to accurately assess the effect of the NMBA.Practice Guidelines Points of EmphasisPlug in the lead wires to the nerve stimulator, attaching the negative (black) and positive (red) leads to the black and red connection sites. Ulnar Nerve Placement: Connect the negative (black) lead to the distal electrode over the crease of the palmer aspect of the wrist and the positive (red) lead to the proximal electrode.Facial Nerve Placement: Connect the negative (black) lead to the distal electrode at the tragus of the ear and the positive (red) lead to the proximal electrode at the outer canthus of the eye.Posterior Tibial Nerve Placement: Connect the negative (black) lead to the distal electrode 2 cm posterior to the medial malleolus in the foot. Connect the positive (red) lead to the proximal electrode 2 cm above the medial malleolus.Turn on the PNS and select a low mA (10 to 20 mA is typical). Excessive amount of mA can lead to over stimulation and repetitive nerve firing.b.Depress the TOF button and observe and countthe number of twitches of the thumb (do notcount finger movements, only the thumb), thenumber of twitches of the muscle above theeyebrow or the number of twitches of the great toe.Finger movements are a result of muscle stimulation, not nerve stimulation. In a person not receiving NMBA therapy, a TOF stimulus, produces four serial thumb adductions. In a person receiving NMBA therapy, the twitches gradually fade. For example, may see only 2 twitches in a person receiving NMBA therapy.Four electrical stimuli are given at 0.5 second intervals.The set of four stimuli should not be repeated more frequently than every 20 seconds, otherwise over stimulation can occur.2.Determining the Supramaximal Stimulation(SMS).a.Increase the mA in increments of 10, untilfour twitches are observed.b.Note the mA that corresponds to four vigoroustwitches. Administer one to two more TOFs.If there is no increase in intensity of the muscle twitch when the mA is increased, the SMS is the level at which four vigorous twitches was observed. For example, if a strong response was observed at 20mA, raise the current to 30 mA. If there is no increase in intensity of the twitch, the SMS is 20mA. If there is an additional increase in twitch intensity, raise it to 40. If the intensity shows no further increase, the SMS is 30 mA.Practice Guidelines Points of Emphasis 3.Determining the TOF response during NMBAinfusion.a.Assess electrode condition and placement forquality.b.Retest the TOF 10 to 15 minutes after a bolusdose and/or continuous infusion of NMBA isgiven/initiated/changed.c.If more than one or two twitches occur andneuromuscular blockade is unsatisfactory forclinical goals, increase the infusion rate asprescribed or according to hospital protocoland retest in 10 to 15 minutes.d.Retest every 4 to 8 hours after clinically stableand a satisfactory level of blockade isachieved. Evaluates the level of blockade provided. Signifies that less than 85% to 90% of receptors are blocked.Evaluates the level of blockade and avoids under- and overestimation of blockade.4.Troubleshooting when there is zero twitches.a.Change electrodes.b.Check lead connections and PNS formechanical failure (i.e. change the battery)c.Increase the stimulating current.d.Retest another nerve (the other ulnar nerve orfacial or posterior tibial nerves).Dry electrode gel or poor contact effects conductione.If there are no other explanations for a zeroresponse, check the NMBA rate infusion dose and concentration. Reduce the infusion rateas needed.Excessive neuromuscular blockade produces absence of twitch response. The desired goal is 1-2 brisk twitches, as this represents 85-90% receptor blockade. Adjust infusion rate of NMBA based upon clinical indicators and TOF testing in order to obtain the 1-2 twitches. Maintaining a receptor block of greater than 90% has been linked to long-term complications such as muscle weakness, prolonged paralysis and difficulty weaning from the ventilator. In addition, longer hospital stays result.Practice Guidelines Points of Emphasis5.Patient monitoring and care issuesa.Cleanse and dry the skin before applying theelectrodes.b.Change the electrodes whenever they are looseor when gel becomes dry.c.Select the most accessible site with the smallestdegree of edema, with no wounds, catheters, ordressings that impede accurate electrodeplacement over the selected nerve.d.Never use the “Single Twitch”, “Tetany” or“Double Burst” settings if available on thePNS.e.The patient may demonstrate subtle movementof the extremities with an acceptable TOFresponse.f.Micro shock hazard may be a risk to patientswith external pacing catheters. Extremecaution must be used to prevent the PNS leadwires from contacting the pacing catheter orpacing lead wires.g.Perform TOF testing every 4-8 hours duringNMBA therapy once stable. For bolus therapy,perform TOF testing before every dose andevery 15 minutes after every dose.h.If using NMBA therapy, ALWAYS providethe patient with adequate sedation andanalgesia.Improves contact and adhesion of electrode.These are less accurate and can cause severe discomfort for the patient.Clinical decisions should never be made based upon one parameter, such as the TOF testing. Assessment of oxygenation and ventilation, neurological function, tissue perfusion, etc. must be used to evaluate before deciding to increase the rate of NMBA infusion.NMBA drugs do not provide any sedating or analgesic effects.Resource Person: Tricia Yates, MSN, RN, FNP-C, CCRNReviewed by: Nursing Policy & Procedure Committee 9/2019Approved: April, 1993Reviewed: 11/95, 8/99, 1/01, 4/05, 7/2016, 9/2019Revised: 8/02, 7/2008, 3/2009Reviewed by Policy & Standard CommitteeReferences: Whetstone Foster, J. G. (2005). Peripheral Nerve Stimulators. In D. J. Lynn-McHale & K. K. Carlson (Eds.), AACN Procedure Manual for Critical Care (pp.837-844). Philadelphia, PA: Elsevier SaundersBallard, N., Robley, L., Barrett, D., Fraser, D>, Mendoza, I. (2006). Patients’ recollections of therapeutic paralysisin the Intensive Care Unit [electronic version] American Journal of Critical Care 15(1), 86-95.。

力学中二维曲线曲率半径的表达式研究

力学中二维曲线曲率半径的表达式研究

2021年 5月 Journal of Science of Teachers′College and University May 2021文章编号:1007-9831(2021)05-0039-05力学中二维曲线曲率半径的表达式研究邵云(南京晓庄学院 电子工程学院,江苏 南京 211171)摘要:介绍了力学中质点二维运动轨迹曲率半径的几种常见的计算公式,证明了它们之间的等价性.利用其中的平面自然坐标系下的公式d d sr j=较简便地推导出平面极坐标系中曲率半径的一般计算公式,据此推导出在通常的极坐标系(以焦点为极点)中圆锥曲线曲率半径的统一表达式,进而推导出在通常的直角坐标系(以中心或顶点为原点)中各圆锥曲线曲率半径的表达式. 关键词:极坐标系;直角坐标系;圆锥曲线;曲率半径中图分类号:O311.1 文献标识码:A doi:10.3969/j.issn.1007-9831.2021.05.008Research on the expression of curvature radius oftwo dimensional curve in mechanicsSHAO Yun(School of Electronic Engineering,Nanjing Xiaozhuang College,Nanjing 211171,China)Abstract:Introduces several common formulas for calculating the radius of curvature of two-dimensional trajectory of particle in mechanics,and proves their equivalence.By using the formula of d d sr j=in the plane natural coordinate system,the general formula for calculating the radius of curvature in plane polar coordinate system is derived simply.Based on this formula,the unified expression of curvature radius of conic curves in the usual polar coordinate system(with the focus as the pole)is derived,and then the expression of the radius of curvature of each conic curve in the usual rectangular coordinate system(with the center or vertex as the origin)is derived. Key words:polar coordinate system;rectangular coordinate system;conic curve;radius of curvature1 几种常见的曲率半径计算公式及相互等价关系在直角坐标系中,二维曲线()y y x =的曲率半径通常表示为()3/221y y r ¢+=¢¢(1)其中:d d y y x ¢=;22d d yy x¢¢=.而在自然坐标系下(见图1),质点二维运动轨迹L 的方程可以写成()s s j =,其曲率半径则可表示成[1]12d d sr j=(2)收稿日期:2021-01-12基金项目:江苏省教育科学“十三五”规划课题(D/2020/01/55)作者简介:邵云(1973-),男,江苏镇江人,讲师,从事理论物理研究.E-mail:*******************其中自然坐标s 定义为:沿自然坐标轴O s ¢正方向从原点O ¢到质点P 的路程(有正负之分);自然坐标j 则定义为:过P 点的切线与x 轴正方向的夹角(要求随质点运动连续变化).在微分几何中,三维曲线的曲率半径常常又被表示成[2]17112t 2d d d d s s r --==e r(3)其中:r 是质点的位置矢量;s 即上述自然坐标,t d d s=re 为切向单位矢量.而在力学中,二维曲线的曲率半径既可表示为d /d tr w j ==v v(4) 其中:v 为做二维曲线运动质点的速率;d /d t w j =为质点绕曲率圆心做瞬间小圆弧运动的角速率;j 同上,又可表达成[3]673r =´v v a(5)其中:v 和a 分别为质点的瞬时速度矢量和瞬时加速度矢量.式(1)~(5)这5个表达式其实是彼此等价的.下文将从直角坐标系下曲率半径的计算公式(1)导出极坐标系下的计算公式(2)[4],进而导出式(3)~(5). 由图1可见d tan d y y xj ¢== (6)将式(6)两边对x 求导得2d sec d y xjj ¢¢= (7) 再将式(6)(7)代入式(1),可得d sec d xr jj= (8) 于是根据几何关系:d sec d s x j =,即得d d sr j=. 只需将式(2)中右边导数的分子和分母同除以d t ,即得式(4);而将图1中右下角所显示的微分几何关系:t d d j =e 代入式(2),即得式(3). 在平面自然坐标系下,二维曲线上质点的运动速度可表示为t =v v e (9)则质点的加速度为y第5期 邵云:力学中二维曲线曲率半径的表达式研究 41t t d d d d d d t t t==+e a e vv v (10) 从图1中右下角的微分分析可知t nd d j =e e(11)于是,将d d s r j =、d d st=v 、式(11)一起代入式(10),即得 2t n d d t r=+a e e v v (12)此即通常的力学教材中质点在自然坐标系下的加速度公式.由式(9)(12)可得333t n r r ==´´a e e v v vv , 这样就从式(2)即d d sr j=证明了式(5).经验表明,式(5)极为实用. 从以上简单的推证可知,式(1)~(5)确实是等价的.2 极坐标系下曲率半径的一种简便的推理方法由于质点P 的元位移d r 的方向就是质点速度的方向(见图2),因此图2中的j 角就相当于图1中的j 角.在图2的极坐标系中,元位移d r 可表示为d d d r r r q q =+re e (13) 于是可见,j 角可以表示为d arctan arctan d r r rr qj q q æöæö=+=+ç÷ç÷¢èøèø(14) 其中:d d r r q ¢=.将式(14)两边对q 求导并整理,得2222d 2d r rr r r rj q ¢¢¢-+=¢+ (15) 其中:22d d rr q¢¢=.此外,根据式(13)有d d s ==r(16)d d sq= (17) 于是,将式(15)(17)代入式(2),即得()3/22222d d /d d d /d 2r r s s r rr rqr j j q ¢+===¢¢¢-+ (18) 此即在极坐标系下二维曲线曲率半径的一般计算公式[5-6].与其它的推理方法[6]14-15相比较,这里利用式(2)即d d sr j=来推理式(18)的方法要简便许多. 3 在通常的极坐标系和直角坐标系中圆锥曲线的曲率半径在通常的极坐标系(以焦点为极点)中,圆锥曲线方程可以统一表示成[1]521cos pr e q=+ (19) 其中:p 为半正焦弦长;e 为偏心率.于是有42 高 师 理 科 学 刊 第41卷()22d sin sin d 1cos r pe r e r pe q q q q ¢===+ (20)2222d 2sin cos d r er re r p p qq q æö¢¢==+ç÷èø(21)将式(19)~(21)一起代入式(18),经计算整理后,得 ()3/222212r e pr pr éù-+ëû= (22)此即在通常的极坐标系下圆锥曲线曲率半径的统一表达式.若将圆锥曲线准线的性质:r e x x =-准代入式(22),并利用圆锥曲线诸参量(如a ,b ,c ,e ,p )之间的关系,即可推得在通常的直角坐标系(以中心或顶点为原点)中诸正圆锥曲线的曲率半径[7]194-196.具体的推理过程如下:(1)在直角坐标系中(见图3),对于椭圆:22221x y a b+=,根据准线知识有2a r e x a ex c æö=-=-ç÷èø(23)其中:椭圆的偏心率为/e c a =.需要说明:r ¹,下同.将式(23)及椭圆的半正焦弦长:()221b p a e a=-=代入式(22),即得()()()()3/223/23/222222223/21/22p a ex p a ex a e x a e x a abpa pr éù--+-êú--ëû===×椭 (24)(2)对于双曲线(见图4):22221x y a b-=(左支),根据准线知识有()2a r e x a ex c æö=--=-+ç÷èø(25)其中:双曲线的偏心率/1e c a =>,左支的x a £-.将式(25)及双曲线的半正焦弦长()221b p a e a=-=代入式(22),即得()()()()3/223/23/222222223/21/22pa ex p a ex e x a e xa a abp a p r éù+-+êú--ëû===双 (26)易见,该结论同样适用于图4中双曲线的右支(x a ³).(3)对于抛物线(见图5):22y px =,根据准线知识有22p pr e x x éùæö=--=+ç÷êúèøëû(27)其中:抛物线的偏心率1e =.将式(27)及1e =代入式(22)(注:该式与抛物线的开口方向无关),则得()3/23/221/2222p p x x p p pr éùæö+ç÷êú+èøëû==抛(28)图3 正椭圆及其准线第5期 邵云:力学中二维曲线曲率半径的表达式研究 434 结语本文介绍了5种常见的曲率半径计算公式(1)~(5),并从式(1)逐步推导出式(2)~(5),显示出它们之间的等价性;利用式(2),即d d sr j=推导出极坐标系下二维曲线曲率半径的一般计算公式(18),即()3/222222r r r rr r r ¢+=¢¢¢-+,该推理方法十分简便,值得推荐;最后,利用式(18)推导出在通常的极坐标系(以焦点为极点)中圆锥曲线曲率半径的统一表达式(22),即()3/222212r e pr pr éù-+ëû=,进而利用它及圆锥曲线的准线性质:r e x x =-准,推导出在通常的直角坐标系(以中心或顶点为原点)中3种正圆锥曲线的曲率半径表达式(24)(26)(28).虽然式(24)(26)(28)可以从传统的直角坐标计算公式(1)直接推得[8],但是本文却是从式(22)推得.这在提供了一种新的推理思路的同时,也揭示出这些公式之间内在的联系,或更便于相关记忆.另外,从本质上说,本文中出现的质点运动学知识实际上也是微分几何知识[2,6,9],也可以说属于数学范畴.需要说明的是,本文主要阐述的是曲率半径在3个不同坐标系中的一般计算式(1)(2)(18),和在运动学中的3个计算式(3)~(5),以及圆锥曲线曲率半径的几个具体的表达式(22)(24)(26)(28).当质点做二维运动的轨迹方程或运动方程已知时,利用这些计算式或表达式便可求出相应的曲率半径,但是方法各异,不一而足[3,7,10].本文在此不再赘述. 参考文献:[1] 周衍柏.理论力学教程[M].3版.北京:高等教育出版社,2009:12,52. [2] 彭家贵,陈卿.微分几何[M].北京:高等教育出版社,2002:15-20.[3] 王化银.一般方法求解曲率半径举隅[J].物理教师,2014,35(5):67,69.[4] 同济大学数学系.高等数学:上册[M].7版.北京:高等教育出版社,2014:169-173. [5] 中国矿业学院数学教研室.数学手册[M].2版.北京:科学出版社,1980:85.[6] 邵云.简析极坐标系下曲线曲率半径的数学与力学推理方法[J].大学物理,2020,39(8):14-17.[7] 李崇虎.用动力学方法求圆锥截线上各点的曲率半径[J].西南师范大学学报(自然科学版),2006(4):193-196. [8] 杨胜,梁双凤.圆锥曲线的渐屈线和曲率圆[J].楚雄师范学院学报,2009,24(6):29-34. [9] 蔡肖兵.对物理学之几何化发展的哲学思考[J].哲学研究,2011(3):86-92.[10]宋辉武,陈钢.用质点匀速率曲线运动的方法求解曲线任意点处的曲率半径[J].物理教师,2018,39(06):56-58.。

N(2,2,0)代数的双极值模糊理想

N(2,2,0)代数的双极值模糊理想

N(2,2,0)代数的双极值模糊理想王丰效【摘要】The concept of bipolar-valued fuzzy set is applied to N(2,2,0) algebra,the notion of bipolar-valued fuzzy ideal of N(2,2,0) algebra is proposed and relative properties of bipolar-valued fuzzy ideal ofN(2,2,0)algebra are discussed.The relationship between bipolar-valued fuzzy ideals and fuzzy ideals of N(2,2,0) algebra is discussed.It is proved that the intersection of two bipolar-valued fuzzy ideals of N(2,2,0)-algebra is also bipolar-valued fuzzy ideal.%将双极值模糊集的概念应用于N(2,2,0)代数,给出了N(2,2,0)代数的双极值模糊理想的概念,讨论了N(2,2,0)代数的双极值模糊理想的相关性质.研究了N(2,2,0)代数的双极值模糊理想与模糊理想的关系.证明了N(2,2,0)代数的两个双极值模糊理想的交仍然是双极值模糊理想.【期刊名称】《山西师范大学学报(自然科学版)》【年(卷),期】2017(031)003【总页数】4页(P26-29)【关键词】N(2,2,0)代数;双极值模糊理想;交集【作者】王丰效【作者单位】喀什大学数学与统计学院,新疆喀什844000【正文语种】中文【中图分类】O1591 IntroductionIn recent years, from the perspective of non classical logic algebra has attracted more and more attention of scholars. Non classical logic and multi valued logic as the expansion and development of the classical logic and classical logic inference and model provides a form of change, has become the computing science and artificial intelligence to deal with the uncertain information is an important form of tools. The N(2,2,0) algebra is introduced to the fuzzy implication operator of the fuzzy implication algebra.N(2,2,0) algebra is an algebra system with two dual semigroups[1~3].The related properties of N(2,2,0) algebra have been discussed in literatures[4~6].The fuzzy ideal of algebra system are widely studied[7~10].In the traditional fuzzy sets, the membership degrees of elements range over the interval[0,1].The membership degree expresses the degree of belongingness of elements to a fuzzy set. The traditional fuzzy set representation cannot tell apart contrary elements from irrelevant elements.Only with the membership degrees ranged on the interval [0,1], it is difficult to express the difference of the irrelevant elements from the contrary elements in fuzzy sets. If a set representation could express this kind of difference, it would be more informative than the traditional fuzzy set representation. Based on these observations, Lee introduced an extension of fuzzy sets named bipolar-valued fuzzy sets[7].Using the notion of bipolar-valued fuzzy set, the subalgebras and ideals of BCK/BCI-algebras are discussed based on bipolar-valued fuzzy sets[8].In this paper, the notions of bipolar-valued fuzzy sets are applied toN(2,2,0) algebra, the concept of bipolar-valued fuzzy ideals of N(2,2,0) algebra is introduced, and related properties are discussed. The relations between bipolar-valued fuzzy ideal and fuzzy ideal are investigated.2 PreliminariesIn this section, we review some basic facts for N(2,2,0) algebra and bipolar-valued fuzzy set.Definition 1 An algebra (S,*,Δ,0) i s called a N(2,2,0) algebra if it satisfies the following conditions: for any x,y,z∈S.(1)x*(yΔz)=z*(x*y), (2)(xΔy)*z=y*(x*z),(3)0*x=x.In what follows, Let S denotes a N(2,2,0) algebra (S,*,Δ,0) unless otherwise specified.If (S,*,Δ,0) is a N(2,2,0) algebra, then (S,*) and (S,Δ) are dual semigroup. Any N(2,2,0) algebra (S,*,Δ,0) satisfies the following axioms: for x,y,z∈S,(1)x*y=yΔx;(2)(x*y)*z=x*(y*z),(xΔy)Δz=xΔ(yΔz);(3)x*y*z=y*x*z,xΔyΔz=xΔzΔy.Definition 2 A fuzzy set μ in a N(2,2,0) algebra S is said to be a fuzzy subalgebra of S if it satisfies, for all x,y∈S,μ(0)≥μ(x) μ(x*y)≥min{μ(x),μ(y)}Definition 3 A fuzzy set μ in a N(2,2,0) algebra S is said to be a fuzzy ideal of S if it satisfies, for all x,y∈S,μ(0)≥μ(x) μ(x)≥min{μ(x*y),μ(y)} μ(x)≥min{μ(xΔy),μ(y)}Bipolar-valued fuzzy sets are an extension of fuzzy sets whose membership degree range is enlarged from the interval [0,1] to [-1,1]. Bipolar-valued fuzzy sets have membership degrees that represent the degree of satisfaction to the property corresponding to a fuzzy set and its counter-property.Let X be the universe of discourse. A bipolar-valued fuzzy set φ in X is an object having the form φ={(x,φ-(x),φ+(x))|x∈X}.Where φ-:X[-1,0] and φ+:X[0,1] are mapping. The positive membership degree φ+(x) denoted the satisfaction degree of an element x to the property corresponding to a bipolar-valued fuzzy set φ={(x,φ-(x),φ+(x))|x∈X}; and the negative membership degree φ-(x) denotes the satisfaction degree of x to some implicit counter-property of φ={(x,φ-(x),φ+(x))|x∈X}.For a bipolar-valued fuzzy set φ and [s,t]∈[-1,0]×[0,1], we defineN(φ,s):={x∈X|φ-(x)≤s} P(φ,t):={x∈X|φ+(x)≥t}Which are called the negative s-cut of φ and the positive t-cut of φ, respectively. The set C(φ,(s,t))=N(φ,s)∩P(φ,t) is called t he (s,t) -cut of φ.3 Bipolar-value Fuzzy Ideal of N(2,2,0) AlgebraDefinition 4 A bipolar-valued fuzzy set φ={(x,φ-(x),φ+(x))|x∈S} in S is called a bipolar-valued fuzzy ideal of S if it satisfies: for all x,y∈S.(1)φ-(0)≤φ-(x),φ+(0)≤φ+(x),(2)φ-(x)≤max{φ-(x*y),φ-(y)},φ+(x)≥min{φ+(x*y),φ+(y)},(3)φ-(x)≤max{φ-(xΔy),φ-(y)},φ+(x)≥min{φ+(xΔy),φ+(y)}.Theorem 1 Let φ={x,φ-(x),φ+(x)} be a bipolar-valued fuzzy set of S.Then φ is a bipolar-valued fuzzy ideal of S if and only if (1) and (2) holds in Definition 4.Th eorem 2 Let φ={x,φ-(x),φ+(x)} be a bipolar-valued fuzzy ideal of S.Then φ+ is a fuzzy ideal of S.Theorem 3 Let φ={x,φ-(x),φ+(x)} be a bipolar-valued fuzzy set in S.Thenφ={x,φ-(x),φ+(x)} is a bipolar-valued fuzzy ideal of S if and only if for all (s,t)∈[-1,0]×[0,1], the nonempty negative s-cut N(φ,s) and the nonempty positive t-cut P(φ,t) are ideals of S.Proof Assume that φ=(x,φ-(x),φ+(x)) is a bipolar-valued fuzzy ideal of S and let (s,t)∈[-1,0]×[0,1] be such that N(φ,s) and P(φ,t) are notempty.Obviousl y,0∈N(φ,s)∩P(φ,t). Let x*y∈N(φ,s)and y∈N(φ,s), then φ-(x*y)≤s and φ-(y)≤s. Since φ=(x,φ-(x),φ+(x)) be a bipolar-valued fuzzy ideal of S, soφ-(x)≤max{φ-(x*y),φ-(y)}≤sHence x∈N(φ,s) and N(φ,s) is an ideal of S.Now assume that a*b∈P(φ,t) and b∈P(φ,t), thenφ+(a*b)≥t and φ+(b)≥t. Since φ=(x,φ-(x),φ+(x)) be a bipolar-valued fuzzy ideal of S, soφ+(a)≥min{φ+(a*b),φ+(b)}≥tHence a∈P(φ,t) and P(φ,t) is an ideal of S.Conversely, suppose that the nonempty negative s-cut N(φ,s) and the nonempty positive t-cut P(φ,t)are ideals of S for every (s,t)∈[-1,0]×[0,1]. If φ-(0)>φ-(a) or φ+(0)<φ+(b) for some a,b∈S, then 0∉N(φ,φ-(a))or0∉P(φ,φ+(b)). But N(φ,φ-(a)) and P(φ,φ+(b)) are ideals of S. This is a contradiction. Thus φ-(0)≤φ-(x) and φ+(0)≥φ+(x) for all x∈S.Assume that φ-(a)>max{φ-(a*b),φ-(b)}=s for some a,b∈S. Then we have φ-(a*b)≤s and φ-(b)≤s, that is a*b,b∈N(φ,s).Since N(φ,s) is an ideal of S,thus a∈N(φ,s), this shows thatφ-(a)≤s. This is impossible, and thus φ-(x)≤max{φ-(x*y),φ-(y)} for all x,y∈S. Suppose that there exists c,d∈S. such that φ+(c)<min{φ+(c*d),φ+(d)}=t, the c*d,d∈P(φ,t), and hence c∈P(φ,t). This is a contradiction since P(φ,t) is an ideal of S. Thereforeφ+(x)≥min{φ+(x*y),φ+(y)}for all x,y∈S. Consequently, φ is a bipolar-valued fuzzy ideal of S.The orem 4 If φ=(x,φ-(x),φ+(x)) is a bipolar-valued fuzzy ideal of S, then the nonempty (s,t)-cut C(φ,(s,t))of φ=(x,φ-(x),φ+(x)) is an ideal of S.Proof Since C(φ,(s,t)) is nonempty, thus0∈C(φ,(s,t)). Assume thatx*y,y∈C(φ,(s,t)), then x*y,y∈N(φ,s) and x*y,y∈P(φ,t). By Theorem 3, N(φ,s) and P(φ,t) are ideals of S, there fore x∈N(φ,s) and x∈P(φ,t), that isx∈C(φ,(s,t)). Hence the nonempty (s,t)-cut C(φ,(s,t))of φ=(x,φ-(x),φ+(x)) is an ideal of S.Theorem 5 If φ=(x,φ-(x),φ+(x)) is a bipolar-valued fuzzy ideal of S, then the nonempty (-t,t)-cut C(φ,(-t,t))of φ=(x,φ-(x),φ+(x)) is an ideal of S. Theorem 6 Let φ=(x,φ-(x),φ+(x)) be a bipolar-valued fuzzy ideal of S. Then N(φ,0) and P(φ,0) are ideals of S. WhereN(φ,0)={x∈S|φ-(x)=φ-(0)} P(φ,0)={x∈S|φ+(x)=φ+(0)}Proof I f x*y,y∈N(φ,0), then φ-(x*y)=φ-(y)=φ-(0). Since φ be a bipolar-valued fuzzy ideal, thus φ-(x)≤max{φ-(x*y),φ-(y)}=φ-(0) and φ-(x)≥φ-(0), So φ-(x)=φ-(0), that is x∈N(φ,0). Hence N(φ,0) is an ideal of S. Ifa*b,b∈P(φ,0), then φ+(a*b)=φ+(b)=φ+(0). Since φ be a bipolar-valued fuzzy ideal, thus φ+(a)≥min{φ+(a*b),φ+(b)}=φ+(0), and φ+(a)≤φ+(0), So φ+(a)=φ+(0), that is a∈P(φ,0). Hence P(φ,0) is an ideal of S.The following theorem shows that bipolar-valued fuzzy ideal of S can be obtained by two fuzzy ideal of S.Th eorem 7 Let μ1 and μ2 are fuzzy ideals of S. Define a bipolar-valued fuzzy set φ=(x,φ-(x),φ+(x)) in S by φ-(x)=-μ1(x),φ+(x)=μ2(x). Then φ=(x,φ-(x),φ+(x)) is a bipolar-valued fuzzy ideal of S.Let φ1=(x,φ1-(x),φ1+(x)) and φ2=(x,φ2-(x),φ2+(x)) be two bipolar-valued fuzzy set. Define the bipolar-valued fuzzy set φ=φ1∩φ2, whereφ-(x)=φ1-(x)∨φ2-(x) φ+(x)=φ1+(x)∧φ2+(x)The bipolar-valued fuzzy set φ is called the intersection of φ1and φ2. Theorem 8 Let φ1=(x,φ1-(x),φ1+(x)) and φ2=(x,φ2-(x),φ2+(x)) are bipolar-va lued fuzzy ideals of S. Then φ=φ1∩φ2 is a bipolar-valued fuzzy ideal of S. Proof Assume that φ1 and φ2 are bipolar-valued fuzzy ideals of S, then for all x,y∈S, we haveHenceTherefore φ=φ1∩φ2 is a bipolar-valued fuzzy ideal of S. This completes the proof.References:【相关文献】[1] Deng F A.RC-semigroups of N (2, 2, 0) algebra[J].Journal of Shandong University, 2011,46:8~11.[2] Deng F A, Xu Y.On N (2, 2, 0) algebras[J].Journal of Southwest Jiaotong University,1996, 31:457~463.[3] Deng F A, Yong L Q.Regular semigroups of N (2, 2, 0) algebra[J].Advances in Mathematics, 2012,41:665~671.[4] Chen L.Medial idempotents of N (2, 2, 0) algebra[J].Pure and Applied Mathematics, 2011,27:433~436.[5] Li X D.Image and converse image of translation transform of N (2, 2, 0)algebra[J].Journal of Mathematical Research and Exposition,2005,25:148~153.[6] Li X D.Two classes congruence decomposition on N (2, 2, 0) algebras[J].Journal of Lanzhou University2005, 41:120~122.[7] Lee K J.Bipolar fuzzy subalgerbas and bipolar fuzzy ideals of BCK/BCI-algerbas[J].Bull Malays Math Sci Soc, 2009,32:361~373.[8] Lee K parison of interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets[J]. Fuzzy Logic Intelligent Systems,2004, 14: 125~129.[9] Wang F X.On (λ,μ)-fuzzy subalgebras in Boolean algebras[J].Applied Mathematics A Journal of Chinese Universities), 2011,26: 495~500.[10] Wang F X.Fuzzy dot subalgebra in N (2, 2, 0)-algebras[J].Journal of Heilongjiang University, 2016,33(6):770~773.。

AnEmpiricalSoilLossEquation-tucson.ars.ag.gov

AnEmpiricalSoilLossEquation-tucson.ars.ag.gov

An Empirical Soil Loss EquationLiu Baoyuan, Zhang Keli and Xie YunDepartment of Resources and Environmental Sciences, Beijing Normal University, Key Laboratory of Environmental Change and Natural Disaster, the Ministry of Education of China,Beijing 100875, PR ChinaAbstract: A model was developed for estimating average annual soil loss by water on hillslope for cropland, which is called Chines Soil Loss Equation (CSLE). Six factors causing soil loss were evaluated based on soil loss data collected from experiment stations covering most regions of China and modified to the scale of Chinese unit plot defined. The model uses an empirical multiplicative equation, A=RKLSBET, for predicting interrill erosion from farmland under different soil conservation practices. Rainfall erosivity (R) was the product of rainfall amount and maximum intensity of 10min, and also was estimated by using daily rainfall data. The value of soil erodibility (K), the average soil loss of unit plot per rainfall erosivity, for 6 main soil types was calculated based on the data measured from unit plots and other data modified to the unit plot level. The method of calculating Kfrom soil survey data for regions without measured data was given. The slope length and steepness factors was calculated by using the equations in USLE if slope steepness is less than 11 degree, otherwise the steepness factor was evaluated by using a new seep slope equation based on the analysis of measured soil loss data from steep slope plots within China.According to the soil and water conservation practices in China, the values of bio-control, engineering-control, and tillage factors were estimated.Keywords: Chines soil loss equation, soil loss, unit plot1 IntroductionSoil loss equation is to predict soil loss by using mathematical methods to evaluate factors causing soil erosion. It is an effective tool for assessing soil conservation measures and making land use plans. Universal soil loss equation is an empirical equation developed during 1950’s that had been applied for natural resources inventory successfully in the US and revised in 1990’s. From 1980’s the process-based models for prediction of soil loss have been studied though the world, such as WEPP (Water Erosion Predict Project, Nearing et al., 1989), GUEST (Griffith University Erosion System Template, Misra and Rose, 1996), EUROSEM (the European Soil Erosion Model Morgan etal., 1998) and LISEM (Limburg Soil Erosion Model, De Roo and Wesseling, 1996). There have been many studies on soil erosion models and related experiments since 1940's, but the models were limited in local levels and difficult to expand to broad regions due to data collected without universal standard. So far there is not any soil loss equations that could be applied through China with minor errors. The objective of this study is to develop a soil loss equation used within China based on measured data from Chinese unit plots and data from many plots modified to Chinese unit plot, which is called Chinese soil loss equation (CSLE).2 Model descriptionSoil loss is a process of soil particle detachment by raindrops and then transported by runoff from the rainfall. Many factors like soil physical characteristics slope features, land surface cover etc. will influence soil loss amount, but they have interactions. It is necessary to distinct their effects on soil loss mathematically and to evaluate them on the same scale in order to improve the accuracy of the model. Unit plot is such a good method to solve the problem. The normalized data covering the China by modified to unit plot supported the development of Chines soil loss equation. In addition,22 two features of soil erosion in China are distinctive and should be considered in the equation. One issoil erosion with steep slope, and the other is the systematical practices for soil conservation during thelong history of combating soil erosion, which could be classified as biological-control, engineering-control and tillage measures. So the Chinese soil loss equation was express as followsafter the analysis of data collected from most regions of ChinaA = RKSLBET (1)where A is annual average soil loss (t/ha), R is rainfall erosivity (MJ mm/(h ha y)), K is soilerosibility (t ha h/(ha MJ mm y)), S and L are dimensionless slope steepness and slope lengthfactors, B , E , and T are dimensionless factors of biological-control, engineering-control, and tillagepractices respectively. The dimensionless factors of slope and soil conservation measures were defined as the ratio of soil loss from unit plot to actual plot with aimed factor changed but the samesizes of other factors as unit plot. Chinese soil loss equation is to predict annual average soil lossfrom slope cropland under different soil conservation practices.To evaluated factors in the equation, about 1841 plot-year data were analyzed. Of these, 214plot-year data from 12 plots and 1143 rainfall events from 14 weather stations were used to evaluaterainfall erosivity. The Chinese unit plot was determined by analyzing 384 plot size data, and about200 plot-year data from 12 plots modified to unit plot were used to evaluate erodibility for 6 types ofsoil. About 30 plot-year data from steep plots modified to unit plot was used to establish the steepslope factor equation. Other plot data was used to calculate the values of biological-control, engineering-control and tillage factors.3 Factor calculations for the equation3.1 Rainfall erosivity (R )A threshold for erosive rainfall of 12mm was estimated, close to that suggested by Wischmeierand Smith (1978), 12.7mm. After comprehensive considering the accuracy of rainfall erosivity, thedata availability and calculation simplicity, the rainfall index of a rainfall event for Chinese soil losserosion was defined. It is the product of rainfall amount (P ) and its maximum 10-min intensity (I 10),and the relationship between PI 10 and the universal rainfall index EI 30 was also estimated as follows:EI300.1773PI 10 R 20.902 (2)where E is the total energy for a rainfall event (MJ/ha), I 30 and I 10 are the rainfall maximum 30min and10min intensities respectively (mm/hr), P is the rainfall amount (mm). The annual rainfall erosivityis the sum of PI 10 for total rainfalls through the year. Actually, it is also difficult to get the rainfallevent data. To apply the weather data from weather stations covering the China, an equation functionfor estimating half-month rainfall erosivity by using daily rainfall data was developed.1010.184(n hm d d i i R P ==∑)IR 20.973 (3)where R hm is the rainfall erosivity for half-month (MJ mm/h ha), P d is the daily rainfall amount (mm)and I 10d is the daily maximum 10min rainfall intensivity (mm/h). I =1, …, n is the rainfall days withina half-month. If there is no I 10d available, R hm was also calculated by using only daily rainfall amount.1()n hm d i i R P βα==∑ (4)where α and β are fitted coefficients and other variables had the same meaning as above.Seasonal rainfall erosivity distribution could be estimated by the sum of R hm . To plot Chineseisoerodent map for estimation or interpolation of local values of average annual rainfall erosivity in23 any place, the empirical relationships by using different rainfall available data were estimated (not listed). Users can choose different equations to calculate average annual rainfall erosivity according to the data availability.3.2 Soil erodibility (K)Soil erodibility is defined as soil loss from unit plot with 22.1m long and 9% slope degree per rainfall erosion index unit (Olson and Wischmeier, 1963). Different from the US, much of soil loss was from steep slope in China. So the Chinese unit plot was defined as a 20m long, 5m wide and 15°degree slope plot with continuously in a clean-tilled fallow condition and tillage performed upslope and downslope. The suggestion of Chinese unit plot made data measured be used to evaluate K values as much as possible without large errors. Because 15° is the middle values for most plots in China. Modified data measured from both plots of less than 15° and larger than 15° to a unit plot had the relative minimum errors.Based on the K defination and Chines unit plot, soil erodibility for 6 main soil types in China was estimated. For example, the values of K for loess were 0.61, 0.33, and 0.44 t ha h/(ha MJ mm) in Zizhou, Ansai, and Lishi in Loess Plateau of China.3.3 Slope length (L) and slope steepness (S) factorsTopography is an important factor affecting soil erosion. It is significant to quantitatively evaluate the effects of topography on erosion for predicting soil loss. The effects of topography on erosion includes slope length and steepness in terms of soil-loss estimation. In soil loss equation, factors of slope length and steepenss were cited with dimensionless values. They are values of the ratio of the soil loss from the plot with actual slope steepness or slope length to that from the unit plot.The relationship between slope length and soil loss has been studied from field or lab data for a long time. Many studies showed that the soil loss per area is proportional to some power of slope length except that the values of the exponent are slightly different. For example, Zingg (1940) derived a value of 0.6 for the slope-length exponent. Musgrave (1947) used 0.35. USLE (Universal Soil Loss Equation ) published in 1965 suggested the values of 0.6 and 0.3 respectively for slopes steeper than 10% and very long slopes, and 0.5 for other conditions. In 1978, the USLE (Wischmeier and Smith, 1978) adjusted the exponent values for different cases, 0.5 for 5% slope or more, 0.4 for slopes between 3.5% and 4.5%, 0.3 for slopes between 1% and 3%, and 0.2 for slopes less than 1%. Revised USLE (RUSLE) published in 1997 used a continuous function of slope gradient for calculating slope-length exponent.Soil erosion from the steep slope is serious in China. How slope length influences soil loss on steep slopes needs further studies. For this end, the relationship between slope length and soil loss on steep slopes was examined based on the plot data obtained at Suide, Ansai, and Zizhou on the loess plateau of China and modified to the unit plot. The results indicated that the slope-length equation in the RUSLE could not be used for soil loss prediction under the steep slope conditions. The equation for calculating soil length factor in the USLE published in 1978 could be applied into China:22.13mLλ=(5)where λ is slope-length (m), m is the slope length exponent.Slope gradient is another topographical factor affecting soil erosion. Most studies have shown that the relation of soil loss to gradient may be expressed as some exponential function or quadratic polynomial. Zingg (1940) concluded that soil loss varies as the 1.4 power of percent slope, and Musgrave (1947) recommended the use of 1.35. Based on a substantial number of field data, Wischmeier and Smith (1965) derived a slope-gradient equation expressed as quadratic polynomial function of gradient percent. Having analyzed the data assembled from plots under natural and simulated rainfall, McCool et al., (1987) found that soil loss increased more rapidly from the slopes24 steeper than 5° than that from slopes less than 5°, and he recommended two different slope steepnessfactor equations for different ranges of slopes:S =10.8sin θ + 0.03 θ 5° (6-1)S =16.8 sin θ – 0.5 θ > 5°(6-2) These equations were established based on soil loss data from gentle slopes, and have not beentested for steep slope conditions. We used soil loss plot data from Suide, Ansai, and Tianshui on theloess plateau of China to test the equations. The results showed that great errors were produced whenusing equations suggested by McCool et al ., (1987) for predict soil loss from slopes steeper than 10°. After the slope degree was larger than 10°, soil loss from steep slopes increased ripedly. Based theregression analysis of our data, am equation to calculate slope steepness factor for seep slopes wasdeveloped:S =21.91 sin θ – 0.96 θ10°(6-3)So in Chinese soil loss equation, slope steepness factor could be estimated by using equation (6-1)to (6-3) under different slope conditions3.4 Bilogical-control (B ), engineering-control (E ), and tillage (T ) factorsDuring the development of the historical agriculture traditions in China, the systematical practicesfor soil and water conservation formed. They could be divided into three categories: biological-control, Engineering-control and tillage measures. Biological-control practices include theforest or grass plantation for reducing runoff and soil loss. Engineering-control practices refer to thechanges of topography to reduce runoff and soil loss by engineering construction like terrace, check-dams. Tillage practices are the measures taken by farmland equipment. The difference between engineering and tillage is that the latter does not change the topography and is only applied onthe farmland.Table 1 Estimated values of biological-control factor for cropsSeedbed Establishment Development Maturing crop Growing season Annual averageBuckwheat 0.71 0.54 0.19 0.21 0.74 0.74 Potato 1.00 0.53 0.47 0.30 0.47 0.50 Millet 1.00 0.57 0.52 0.52 0.53 0.55 Soybean 1.00 0.92 0.56 0.46 0.51 0.53 Winter wheat 1.00 0.17 0.23Maize intercropping with soybean1.00 0.40 0.26 0.03 0.28 Hyacinth Dolichos 1.00 0.70 0.46 0.57Table 2 Estimated values for factors of woodland and grassland vegetationSophora Korshinsk Peashrub Seabuckthorn Seabuckthorn & PoplarSeabuckthorn & Chinese Pine Erect Milkvetch Sainfoin Alfalfa First year Sweetclover Second year Sweetclover0.004 0.054 0.083 0.144 0.164 0.067 0.160 0.256 0.377 0.08325Many studies gave the B values for different biologic measures in China, but they were not from the universal calculated methods and could not be used directly in soil loss equation. Based on the defination of B values, the ratio of soil loss from plots with some biological-control practice to that from unit plot, we calculated B values for some types of biological-control practices (Table 1). Some values for typical engineering-control and tillage measures in China were summarized (not listed).References[1] Nearing, M.A., G.R. Foster, L.J. Lane, and S.C. Finkner. A process-based soil erosion modelfor USDA-water erosion prediction project technology. Transactions of The ASAE, 1989, 32(5):1587-1593.[2] Misra R K, Rose C W. Application and sensitivity analysis of process -based erosionmodel—GUEST[J]. European Journal Soil Science. 1996,10:593-604.[3] Morgan R P C, Quinton J N, Smith R E, et al., The European soil erosion model (EUROSEM): Adynamic approach for predicting sediment transport from fields and small catchments[J]. Earth Surface Processes and Landforms, 1998,23:527-544.[4] De Roo A P J. The LISEM project: an introduction. Hydrological Processes. 1996, vol.10:1021-1025.[5] Wischmeier W H, Smith D D. Predicting rainfall erosion losses[R]. USDA AgriculturalHandbook No.537. 1978.[6] Olson,T.C., and Wischmeier,W.H. 1963. Soil erodibility evaluations for soils on the runoff anderosion stations. Soil Science Society of American Proceedings 27(5):590-592.[7] Zingg A W. Degree and length of land slope as it affects soil loss in runoff[J]. AgriculturalEngineering, 1940,21: 59-64.[8] Musgrave G W. The quantitative evaluation of factors in water erosion A first approximation[J].Journal Soil and Water Cons, 1947, 2:133-138.[9] Renard K G,Foster G R,Weesies G A et al., RUSLE―A guide to conservation planning with therevised universal soil loss equation[R]. USDA Agricultural Handbook No.703. 1997.[10] McCool, D. K. Brown, L.C., Foster, G. R., et al., Revised slope steepness factor for theuniversal soil loss equation. TRANSACTIONS of the ASAE, 1987, 30(5): 1387-1396.。

摄像机英汉互译

摄像机英汉互译

摄影词汇英汉对照表AAberration像差Accessory附件Accessory Shoes 附件插座、热靴Achromatic消色差的Active主动的、有源的Acutance锐度Acute-matte磨砂毛玻璃Adapter适配器adva nee system输片系统AE Lock(AEL)自动曝光锁左AF(Autofocus)自动聚焦AF IlluminatorAF照明器AF spotbeam projector AF照明器Alkaline碱性Ambient light环境光Amplification factor放大倍率Angle finder弯角取景器Angle of view视角Ant 卜Red-eye防红眼Aperture光圈Aperture priority光圈优先APO(APOchromat)复消色差APZ(Advaneed Program zoom)高级程序变焦Arc弧形ASA(American Standards Association)美国标准协会Astigmatism像散Auto bracket自动包国Auto composition自动构图Auto exposure自动曝光Auto exposure bracketing 自动包用曝光Auto film advanee自动进片Auto flash自动闪光Auto loading自动装片Auto multi-program自动多程序Auto rewind自动退片Auto wind自动卷片Auto zoom自动变焦Automatic exposure(AE)Automatio n自动化Auxiliary辅助的BBack机背Back light逆光、背光Back light compensation 逆光补偿Background 背景Balance contrast 反差平衡Bar code system条形码系统Barrel distortion 桶形畸变BAse-Stored Image Sensor (BASIS)基存储影像传感器Battery check电池检测Battery holder 电池手柄Bayonet 卡口Bellows 皮腔Blue filter蓝色滤光镜Body-integral 机身一体化Bridge camera桥梁相机Brightness control 亮度控制Built in 内置Bulb B 门CCable release 快门线Camera照相机Camera shake相机抖动Cap盖子Caption贺辞、祝辞、字幕Card 卡Cartridges 暗盒Case机套CCD(Charge Coupled Device)电荷耦合器件CdS cell硫化镉元件Center spot中空滤光镜Center weighted averaging中央重点加权平均Chromatic Aberration 色差Circle of confusion 弥散圆Close-up 近摄Coated镀膜Compact camera 袖珍相机Composition 构图Compound lens复合透镜Computer计算机Contact 触点Continuous advance 连续进片Continuous autofocus 连续自动聚焦Contrast反差、对比Convetor转换器Coreless无线圈Correction 校正Coupler耦合器Coverage覆盖范围CPU (Central Processing Unit)中央处理器Creative expansion card 艺术创作软件卡Cross交叉Curtain 帘幕Customized function用户自选功能DData back数据机背Data panel数据而板Dedicated flash专用闪光灯Definition 淸晰度Delay 延迟、延时Depth of field 景深Depth of field preview 景深预测Detection 检测Diaphragm 光阑Diffuse 柔光Diffusers柔光镜DIN (Deu&he Industrische Normen)徳国工业标准Diopter屈光度Dispersion 色散Display 显示Distortion 畸变Double exposure 双重曝光Double ring zoom双环式变焦镜头Dreams filter梦幻滤光镜Drive mode驱动方式Duration of flash闪光持续时间DX-code DX 編码EED(Extra low Dispersion)超低色散Electro selective pattern(ESP)电子选择模式EOS(Electronic Optical System) 电子光学系统Ergonomic人体工程学EV(Exposure Value)曝光值Evaluative metering综合评价测光Expert专家、专业Exposure曝光Exposure adjustment曝光调整Exposure compensation 曝光补偿Exposure memory曝光记忆Exposure mode曝光方式Exposure value(EV)曝光值Extension tube近摄接圈Extension ring近摄接圈External metering外测光Extra wide angle lens超广角镜头Eye-level fixed眼平固立Eye-start眼启动Eyepiece目镜Eyesight correction lenses 视力校正镜FField curvature 像场弯曲Fill in填充(式)Film胶卷(片)Film speed胶卷感光度Film transport 输片、过片Filter滤光镜Finder取景器First curtain前帘、第一帘幕Fish eye lens鱼眼镜头Flare耀斑、眩光Flash闪光灯、闪光Flash range 闪光范11;]Flash ready闪光灯充电完毕Flexible program 柔性程序Focal length 焦距Focal plane焦点平而Focus焦点Focus area聚焦区域Focus hold焦点锁定Focus lock焦点锁左Focus prediction 焦点预测Focus priority 焦点优先Focus screen 聚焦屏Focus tracking 焦点跟踪Focusing聚焦、对焦、调焦Focusing stages 聚焦级数Fog filter雾化滤光镜Foreground 前景Frame张数、帧Freeze冻结、凝固Fresnel lens菲涅尔透镜、环状透镜Frontground 前景Fuzzy logic模糊逻辑GGlare眩光GN(Guide Number)闪光指数GPD(Gallium Photo Diode)稼光电二极管Graduated 渐变HHalf frame 半幅Halfway 半程Hand grip 手柄High eye point远视点、高眼点High key 高凋Highlight高光.髙亮Highlight control 高光控制High speed 髙速Honeycomb metering 蜂巢式测光Horizontal 水平Hot shoe热靴、附件插座Hybrid camera混合相机Hyper manual 超手动Hyper program 超程序Hyperfocal 超焦距IIC(Integrated Circuit)集成电路Illumination angle 照明角度Illuminator 照明器Image control影像控制Image size lock影像放大倍率锁定Infinity无限远、无穷远Infra-red(IR)红外线Instant return 瞬回式Integrated 集成Intelligence 智能化Intelligent power zoom智能化电动变焦Interactive function 交互式功能Interchangeable 可更换Internal focusing 内调焦IntervaI shooting 间隔拍摄ISO(International Standard Association)国际标准化组织JJIS(Japanese Industrial Standards)日本工业标准LLandscape 风景Latitude宽容度LCD data panel LCD 数据而板LCD(Liquid Crystal Display)液晶显示LED(Light Emitting Diode)发光二极管Lens镜头、透镜Lens cap镜头盖Lens hood镜头遮光罩Lens release镜头释放钮Lithium battery 锂电池Lock闭锁、锁泄Low key低调Low light低亮度、低光LSI(Large Scale Integrated)大规模集成MMacro微距、巨像Magn ificatio n放大倍率Main switch主开关ManualManual exposure 手动曝光Manual focusing 手动聚焦Matrix metering 矩阵式测光Maximum最大Metered manual 测光手动Metering测光Micro prism微棱Minimum最小Mirage倒影镜Mirror反光镜Mirror box反光镜箱Mirror lens折反射镜头ModuleMonitor监视、监视器Monopod独脚架Motor电动机、马达Mount卡口MTF (Modulation Transfer Function 调制传递函数Multi beam多束Multi control多重控制Multi-dimensi onal多维Multi-exposure多重曝光Multi-image多重影Multi-mode多模式Multi-pattern多区、多分区、多模式Multi-programMulti sensor多传感器、多感光元件Multi spot metering多点测光Multi task多任务NNegative 负片Neutral 中性Neutral density filter中灰密度滤光镜Ni-Cd battery線铭(可充也电池Off camera 离机Off center偏离中心OTF(Off The Film)偏离胶卷平面One ring zoom单环式变焦镜头One touch单环式Orange filter橙色滤光镜Over exposure曝光过度pPanning 摇舶Panoema 全景Parallel 平行Parallax平行视差Partial metering 局部测光Passive被动的、无源的Pastels filter水粉滤光镜PC(Perspective Control)透视控制Pentaprism 五棱镜Perspective 透视的Phase detection 相位检测Photography 摄影Pincushion distortion 枕形畸变Plane of focus焦点平而Point of view 视点Polarizing偏振、偏光Polarizer偏振镜Portrait人像、肖像Power电源、功率、电动Power focus电动聚焦Power zoom电动变焦Predictive 预测Predictive focus control 预测焦点控制Preflash 预闪Professional 专业的Program 程序Program back程序机背Program flash程序闪光Program reset程序复位Program shift程序偏移Programmed Image Control (PIC)程序化影像控制QQuartz data back仃英数据机背RRainbows filter彩虹滤光镜Range finder测距取景器Release priority 释放优先Rear curtain 后帘Reciprocity failure 倒易律失效Reciprocity Law 倒易律Recompose重新构图Red eye红眼Red eye reduction 红眼减少Reflector反射器、反光板Reflex反光Remote control terminal 快门线插孔Remote cord 遥控线、快门线Resolution 分辨率Reversal films 反转胶片Rewind退卷Ring flash环形闪光灯ROM(Read Only Memory)只读存储器Rotating zoom旋转式变焦镜头RTF(Retractable TTL Flash)可收缩TTL 闪光灯SSecond curtain后帘、第二帘幕Secondary Imaged Registration(SIR)辅助影像重合Segment 段、区Selection 选择Self-timer 自拍机Sensitivity 灵敏度Sensitivity range 灵敏度范I 间Sensor传感器Separator lens 分离镜片Sepia filter褐色滤光镜Sequence zoom shooting 顺序变焦拍摄Sequential shoot 顺序拍摄Servo autofocus伺服自动聚焦Setting 设置Shadow阴影、暗位Shadow control 阴影控制Sharpness淸晰度Shift偏移、移动Shutter 快门Shutter curtain 快门帘幕Shutter priority 快门优先Shutter release 快门释放Shutter speed快门速度Shutter speed priority 快门速度优先Silhouette 剪影Single frame advance 单张进片Single shot autofocus 单次自动聚焦Skylight filter天光滤光镜Slide film幻灯胶片Slow speed synchronization 慢速同步SLD(Super Lower Dispersion)超低色散SLR(Single Lens Reflex)单镜头反光照相机SMC(Super Multi Coated)超级多层镀膜Soft focus柔焦、柔光SP(Super Performance)超级性能SPC(Silicon Photo Cell)硅光电池SPD(Silicon Photo Dioxide)硅光电二极管Speedlight闪光灯、闪光管Split image 裂像Sport体冇、运动Spot metering 点测光Standard 标准Standard lens标准镜头Starburst星光镜Stop 档Synchr on izati on 同步TTele converter增距镜、望远变换器Telephoto lens长焦距镜头Tmilin g-shutter curtain后帘同步Trap focus陷阱聚焦Tripod三脚架TS(Tilt and Shift)倾斜及偏移TTL flashTTL闪光TTL flash meteringTTL闪光测光TTL(Through The Lens)通过镜头、镜后Two touch双环uUD(Ultra-low Dispersion)超低色散Ultra wide超阔、超广Ultrasonic 超声波UV(Ultra-Violet)紫外线Under exposure 曝光不足VVari-colour 变色Var-program 变程序Variable speed 变速Vertical 垂直Vertical traverse 纵走式View finder 取景器wWarm tone暖色调Wide angle lens 广角镜头Wide view 广角预视、宽区预视Wildlife野生动物Wireless remote 无线遥控World time世界时间XX-sync X・同步ZZoom变焦Zoom lens变焦镜头Zoom clip变焦剪裁Zoom effect变焦效果。

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2
µ The variable xµ i (t) is the world-line coordinate of the i-th particle and pi is its corresponding momentum. The Lagrange multipliers λAij implement the constraints φAij = 0 and satisfy

Theory Group, Department of Physics The Univ. of Texas at Austin RLM 5208, Austin, TEXAS

Departament d’Estructura i Constituents de la Mat` eria Universitat de Barcelona Diagonal, 647 E-08028 BARCELONA
λ1ji = λ1ij , The explicit form of φAij is φ1ij = 1 pi pj , 2 φ2ij = pi xj
λ3ji = λ3ij .
and
φ3ij =
1 xi xj . 2
(2.2)
These 2M 2 + M constraints close under Poisson bracket giving a realization of the Sp(2M ) algebra. It is useful to introduce a matrix notation for the coordinates and momenta of the particles r ¯ = p⊤ , −r ⊤ R= , R (2.3) p with x1 . r= . . , xM
the equations of motion (2.9) and transformation properties of the matter fields (2.6) can be written as ¯ − Az (∂ (∂ − Az )R = 0. (2.11) ¯)R = 0, This linear system of partial differential equations has an integrability condition Fz z ¯ = 0, which is equivalent to the transformation law of the Lagrange multipliers (2.7) under the identifications (2.10). These relations will continue to hold when we fix the gauge partially. The above discussion explains why a relativistic particle model becomes after partial gauge-fixing a model of matter with a non-linear W -symmetry. As we will see below it is useful to express the model in terms of lagrangian variables in order to construct the finite transformations of the model. If we write the momenta p in terms of the lagrangian variables p = A−1 (r ˙ − Br ) ≡ K, the action is now rewritten as S= The gauge transformations are δr = Aβ K + Bβ r, ˙ − [Λ, β ]. δΛ = β (2.14) dt 1 K ⊤ AK − r ⊤ Cr . 2 (2.13) (2.12)
1
(2.6) (2.7)
Bβ Aβ ⊤ −Cβ −Bβ
(2.8)
For a previous discussion of geometrical models and Yang-Mills gauge theories see [16].
3
and Aβ , Bβ , Cβ are the M × M matrices gauge parameters associated to the constraints φ1ij , φ2ij , φ3ij . The equations of motion of the matter fields are ˙ − ΛR = 0. R If we make the following identifications ¯ δ → ∂, β → Az ¯, Λ → Az , d → ∂, dt (2.10) (2.9)
In the last few years a lot of attention has been devoted to the study of W -algebras [1]. For recent update reviews see [2], [3] where extensive lists of references can be found. An interesting way to construct classical W -algebras is by the zero-curvature method [4], [5], [6],[7] [8]. If one constraints an Az gauge potential the residual gauge transformations can be obtained as a zero-curvature condition Fz z ¯ = 0. This zero-curvature condition is the integrability condition of a linear system of partial differential equations. As we will see this system can be related to the transformation properties and equations of motion of matter coupled to the gauge fields. In this letter we consider a relativistic model of M particles with an Sp(2M ) gauge group, the matter variables being the coordinates and momenta of the particles and the gauge variables being the Lagrange multipliers. We find that under some formal identifications between 2d gauge theories and 1d particle models, the equations of motion and transformation properties of the matter variables can be written as a system of partial differential equations whose integrability condition is precisely the zero-curvature condition Fz z ¯ = 0. This condition is equivalent to the transformation properties of the Lagrange multipliers. These relations continue to hold when we fix the gauge partially. This fact explains why a model of relativistic particles exhibits, after a partial gaugefixing, invariance under non-linear W -symmetry tranformations. In a sense, it can be understood as a coupling of matter to (world-line) W -gravity. The particle model is also useful for the construction of finite W -transformations. Finite transformations are necessary in order to understand completely the W -geometry [6], [8], [9], [10], [11], [12], [13], [14], [15]. The strategy is the following: we first construct the finite linear transformations of the Sp(2M ) model and then, by a partial gaugefixing at the finite level, we find residual finite W -transformations. In this way one avoids the direct integration of non-linear infinitesimal W -transformations. We will explicitly construct in this paper finite W -transformations obtained from the Sp(4) gauge group.
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