WinTWINS教程

WinTWINS version 2.3

Mark O. Hill

Petr ?milauer

2005

1 Preface

TWINSPAN, based partly on an earlier program called ‘Indicator Species Analysis’ (Hill et al. 1975), was written in 1979, five years before the Apple Macintosh revolutionized personal computing, and two years before MS DOS was launched. The first version of Windows did not appear till November 1985. In 1979, a personal computer was an expensive luxury possessed only by a few geeks; all serious calculations were made on mainframes. At Cornell University, newly-available computer terminals had made programming much easier than in earlier years. Programs and problems could be submitted electronically (to another room in the Cornell’s Langmuir Lab), though the output was still normally on paper. The time was therefore ripe for development of numerical methods in ecology to the point where they could become routine tools rather than interesting prospects for development.

The early proponents of numerical methods, notably Goodall (1953a, 1953b), had seen themselves as champions of objectivity. They were uncomfortable about the Zürich-Montpellier tradition of continental Europe, which had sought to construct a comprehensive system of knowledge. In the eyes of many British and American ecologists, the Z-M system was subjective and therefore intellectually dubious, because field workers sampled in a way that allowed them to prove what they wanted to find out in the first place. However, not everybody in Britain and America was convinced that objectivity and the Z-M system were in opposition. R.H. Whittaker (1962) urged ecologists to be pragmatic. He visited Tüxen in Germany, and with Tüxen’s blessing edited the monumental Ordination and classification of communities (1973) in which the various protagonists set out their points of view. The publication of a good English-language manual by the German ecologists Mueller-Dombois and Ellenberg (1974), included an exposition of ‘Tablework’, a nearly-algorithmic method of sorting two-way tables. This narrowed the gap still further.

Thus it was natural that Mark Hill, visiting Whittaker in Cornell, should seek to develop an algorithm whose purpose was to sort tables in an objective way but in the spirit of Z-M methodology. There remained a difficulty, namely that existing numerical methods almost all classified either the samples (the so-called Q methods) or the species (R methods) but did not seek to arrange both together. Underlying this difficulty was the fact that many mathematicians were committed to the metric paradigm, according to which a classification should reflect as faithfully as possible a metric compositional distances between samples (for Q methods) or between species (for R methods). By 1979, the metric paradigm for ordination had already been subjected to severe criticism (Austin 1976), and its applicability to classification was therefore also open to question.

This was the background to the writing of TWINSPAN. A practical problem that had already been solved for Indicator Species Analysis was to keep magnitude of the calculation so that it rose only linearly with the size of the dataset. (Clearly any explicit calculation of distance matrices would increase the problem to magnitude m2 if m is the number of samples, or n2 where n is the number of species.) An algorithm linear in the size of the dataset was achieved by, figuratively speaking, sending signals through the data matrix in search of resonances in which the species and samples sounded together, and then dividing the data accordingly. When the samples and subsequently the species were repeatedly divided, TWINSPAN resulted.

TWINSPAN was originally written in FORTRAN 4, a language well suited to mathematical calculations but with poor handling of alphabetical data. Some improvements

were made with FORTRAN 77, which handled alphabetical data rather better. However, when MS DOS was dropped in favour of Windows, the excellent Microsoft Fortran compiler was discontinued. TWINSPAN became difficult to run except under older operating systems such as UNIX or in a DOS window. Given that the program has retained its popularity, there is a need for it to be available in a modern form. It is with pleasure therefore that after 25 years, we present it in an updated form suitable for the 21st Century.

Mark O. Hill

Petr ?milauer

2 Copyright

The WinTWINS software consists from three parts, with different code ownership. The twindll.dll dynamic library implements the actual algorithm of the TWINSPAN method and is owned by Mark Hill’s employer, the Natural Environment Research Council of Great Britain. The executable file wintwins.exe represents a user-friendly wrapper for the method, allowing the user to easily specify analysis options and inspect analysis results. Its code is owned by Petr Smilauer, ?eské Budějovice, Czech Republic. The CanoDatC.dll dynamic library parses the input data files in format compatible with Canoco software and it is written by Cajo Ter Braak and owned by Biometris, Wageningen, The Netherlands.

All the code owners give to everyone the right to use the WinTWINS software for analysis of research data, both in non-commercial and in commercial work. This software can be further distributed in the form of installation program. Integration of any part of the WinTWINS software into any commercial or non-commercial software package is explicitly prohibited.

When using this software, please cite it as follows: Hill, M.O. & ?milauer, P. (2005): TWINSPAN for Windows version 2.3. Centre for Ecology and Hydrology & University of South Bohemia, Huntingdon & Ceske Budejovice.

This software is provided on an "AS IS" basis, without warranty of any kind, including without limitation the warranties of merchantability, fitness for a particular purpose and non-infringement. The entire risk as to the quality and performance of the software is borne by you. Should the software prove defective, you and not the authors of the software assume the entire cost of any service and repair.

The source code for the twindll.dll and wintwins.exe parts is available from its respective authors:

Dr. Mark Hill - moh@https://www.wendangku.net/doc/978118933.html,

Dr. Petr ?milauer - petrsm@https://www.wendangku.net/doc/978118933.html,

3 TWINSPAN Method

3.1 Ordered two-way tables

TWINSPAN (Tw o-way in dicator sp ecies an alysis) is a computer program designed primarily for ecologists and vegetation scientists who have collected data on the occurrence of a set of species in a set of samples. The samples may be stands, relevés, stomach contents, island faunas, or whatever is appropriate to the study. The program first constructs a classification of the samples, and then uses this classification to obtain a classification of the species according to their ecological preferences. The two classifications are then used together to obtain an ordered two-way table that expresses the species' synecological relations as succinctly as possible.

Table 1 : An ordered two-way table derived from the Dune meadow data and

ordered by TWINSPAN; the 0/1 numbering at the bottom and right specify

classifications of the samples and species

In an ordered two-way table, some of the structure of a dataset is apparent. For example in Table 1, we see that the ‘dry’ species Aira praecox, Anthoxanthum odoratum, Empetrum nigrum and Hypochaeris radicata are found especially in samples 17 and 19. Likewise the ‘wet’ species Eleocharis palustris, Potentilla palustris, Ranunculus flammula and Calliergonella cuspidata are found especially in samples 14, 15, 16 and 20. Other species such as the ‘semi-dry’ Lolium perenne and Poa pratensis avoid the wettest samples, while the ‘semi-wet’ Agrostis stolonifera avoids the driest samples.

In this case the arrangement reflects one major variable. This is often what is found. However, in some cases, more complex groupings are revealed and (often) aberrant samples or groups of samples are identified.

3.2 Differential species

The ‘wet’ and the ‘dry’ species are examples of differential species. A differential species is one with clear ecological preferences, so that its presence can be used to identify particular environmental conditions. In Table 2.1, the ‘wet’ species Agrostis stolonifera and the ‘dry’ species Lolium perenne are examples of differential species. There is some overlap between them, but on the whole they avoid each other. On the other hand Leontodon autumnalis is a poor differential species, not showing any marked affinity for a particular group of samples.

3.3 Basic structure of TWINSPAN

TWINSPAN is designed to construct ordered two-way tables, and the method of doing so is by identification of differential species. In this respect it closely resembles the graphical method of classification outlined by Mueller-Dombois & Ellenberg (1974). It differs, however, in its treatment of the species. In the method outlined by Mueller-Dombois & Ellenberg, the species are classified at the same time as the samples. In TWINSPAN, on the other hand, the samples are classified first, and the species are classified second, using the classification of the samples as a basis.

The basic structure of TWINSPAN is as follows.

1.Classify the samples in a divisive hierarchy, dividing them first into 2 subsets, then 4, 8,

16, etc.

2.Convert the sample classification into an ordering.

https://www.wendangku.net/doc/978118933.html,ing the groups of samples as a basis, construct attributes for the species. For example,

in Table 2.1, species 1, 6, 7 and 18 would be described as possessing the attribute ‘preferential to the left side of the major division.’

4.Classify the species in the same way as the samples, but with the difference that whereas

the species were treated as attributes of the samples, the species have attributes of the kind indicated above.

5.Convert the species classification also into an ordering.

6.Print out the resulting ordered two-way table.

3.4 Making a dichotomy

The basic activity in TWINSPAN is to make a dichotomy. Indeed, as indicated above, all that the program does, in effect, is to divide up the samples into groups by repeated dichotomization, and then to do the same for the species.

It can be argued that this is a bad way to organize a table because dichotomies do not arise naturally. Indeed, Mueller-Dombois & Ellenberg (1974) recommend dividing the species into three categories, those that are preferential to one side of the division, those that are preferential to the other, and those that are indifferent. In practice, the indifferent categories are usually picked out in later dichotomies. For example the species labeled 01 in Table 1 are indifferent.

The stages of making a dichotomy in TWINSPAN are as follows.

1.Identify a direction of variation in the data by ordinating the samples. This ordination is

referred to below as the ‘primary’ ordination, and is made by the method of correspondence analysis (also known as reciprocal averaging; Hill, 1973a, 1974).

2.Divide the ordination at its middle to get a crude dichotomy of the samples.

3.Identify differential species that are preferential to one side or the other of the crude

dichotomy.

4.Construct an improved ordination (referred to below as the ‘refined’ ordination), using

the differential species as a basis.

5.Divide the refined ordination at an appropriate point to derive the desired dichotomy.

If this were all, TWINSPAN would be relatively easy to describe. However, yet a third ordination is also constructed, the ‘indicator’ ordination. This is based on a small number of the most strongly differential species, and is designed to provide a simple criterion for re-identification of the groups. So a further stage must be added.

6.Construct a simplified ordination, the ‘indicator’ ordination, based on a few of the most

highly preferential species. See whether the dichotomy suggested by the refined ordination can be reproduced by a division of the indicator ordination.

To summarize, TWINSPAN makes its dichotomies by dividing ordinations in half. There are three ordinations involved:

1.The primary ordination (correspondence analysis), which is divided to obtain an initial,

crude dichotomy;

2.The refined ordination, which is derived from the primary ordination through the

identification of differential species; and

3.The indicator ordination.

With the exception of borderline cases, the refined ordination is the one that is used to determine the dichotomy. The indicator ordination is essentially an appendage, put there for the convenience of users who want a succinct characterization of the dichotomy.

3.5 Pseudospecies

The idea of a differential species is essentially qualitative, and to be effective with quantitative data must be replaced by a quantitative equivalent. This equivalent is the ‘pseudospecies’ (Hill, Bunce & Shaw, 1975; Hill, 1977). The essential idea is that much of the quantitative information can be retained by expressing it on a relatively crude scale such as the Braun-Blanquet scale of cover-abundance (Mueller-Dombois & Ellenberg, 1974). The levels of abundance that are used in TWINSPAN to define the crude scale are here termed ‘pseudospecies cut levels’.

Consider, for the sake of example, the Braun-Blanquet scale. Ignoring the distinction between 1, +, and r, the scale is as follows:

1 0 - 4% 4 51 - 75%

2 5 - 25% 5 76 - 100%

3 26 - 50%

If quantitative data with cover expressed on a percentage scale are entered into TWINSPAN, then the Braun-Blanquet scale can be used by entering the pseudospecies cut levels:

0 5 26 51 76

Consider now a species, for example Poa annua, whose cover in one sample is 18% and in another is 36%. Although these values differ by a factor of 2, the samples have an important feature in common, namely that they share a moderate abundance of Poa annua. Given the Braun-Blanquet scale defined above, this fact can be expressed in terms of pseudospecies by saying that the samples contain the following pseudospecies:

Sample with Poa annua 18% - P. annua 1, P. annua 2;

Sample with Poa annua 36% - P. annua 1, P. annua 2, P. annua 3.

Hence, using the Braun-Blanquet scale, the samples are registered as having two pseudospecies in common, and one different. This is arguably a correct way to view them; in spite of the rather large difference in cover, the samples actually have more in common than by way of difference.

The method of pseudospecies allows quantitative values to be used as differential ‘species’ and as indicators. Thus, in TWINSPAN, instead of a differential species Poa annua, it is possible to have a differential species P. annua 2, which—with the pseudospecies cut levels defined above—occurs when and only when P. annua has cover 5% or greater.

In practice, users of WinTWINS who are mainly interested in a good tabular arrangement will hardly concern themselves with the technical detail of pseudospecies. For them it suffices to know that they are using a particular scale of abundance. However, there is one important practical point, namely that information on the occurrence of each pseudospecies is stored separately in the computer. Use of very numerous pseudospecies may therefore be undesirable with larger problems, as they use up storage space.

4 Working with WinTWINS

Each analysis is represented by a project that is stored in a file with .twp file extension. This is a binary file that cannot be directly viewed or edited with other programs. To modify project settings, you must use the Setup Wizard, a series of dialog pages, where you specify input data and other analysis options (see section 4.2 for details of this procedure).

4.1 Program workspace

WinTWINS works at any time with a single project. The project is manipulated using the commands in the program menu and the main analysis results are displayed in the WinTWINS window (see Figure 1).

Figure 1

The commands in the File submenu allow you to create a new project, open an existing one and save the project, optionally under different name (the Save As command). The project log (shown in the window) can be also printed.

The commands in the Edit submenu (the Undo, Cut, Copy, and Paste commands) provide basic support for working with the analysis log displayed in the program window.

The commands in the View submenu allow you to show or hide the program toolbar (Toolbar), show or hide the status bar at the window lower edge (Status Bar), or to change type and size of font used to display analysis log (Window Font).

4.2 Setup Wizard

The project setup wizard is shown when you select the Project / Settings menu command and also when you start the program or open new TWINSPAN project file. At the first wizard page (Figure 2), you must specify the name of data file.

Figure 2

The WinTWINS program is able to read data in condensed, full or free formats, corresponding to formats supported by Canoco? for Windows, version 4.x (Ter Braak & ?milauer 2002). The maximum allowed number of samples is 25000, maximum number of variables is 10000. Note that the later number includes not only the variables (species) present in your data file, but also the pseudo-variables (pseudo-species) that are created based on the cut levels you specify in the next setup wizard page. The maximum number of presences for variables and pseudo-variables (pseudo-species) is set to 4000000, but it should not exceed 2000000 for a classification of species to be created in addition to a classification of your samples.

After you click the Next button, WinTWINS program parses the data to check their size and range of values. This information is displayed in the following page (Figure 3) and also the suggested cut levels are adjusted for the range of data values. WinTWINS uses the default cut levels (0, 2, 5, 10, 20), but removes the levels exceeding the largest value in data.

Figure 3

You must select unique, increasing cut levels. They should be chosen so as to reflect typical values of abundance, e.g. "present", "a little", "a lot", "more-or-less dominant". It is important, however, not to over-weight the effect of dominance by including many relatively high cut levels. With percentage data the default cut levels have proved very effective, and the user is recommended to adhere to them until he or she has some experience with the method.

For each cut level, you can specify its relative weight in the analysis. Note that this weight must be specified with whole positive numbers, representing the multiple of the default weight (=1) to use for a particular level. In many applications, it is not necessary to use weights at all (i.e. to use the value of 1 for all cut levels). However, with community composition data taken from very large plots, it may sometimes be advisable to give presences and absences relatively low weights, as they may be due to anomalous small patches of terrain that occupy little of the plot area.

The last column in the Cut Levels page represents the indicator potential for individual cut levels. If a particular box is checked, the (pseudo-)variables resulting from that level (or, usually, the presence of species for the first cut level) can be used as indicators for individual splits. Very often, all levels have an indicator potential. The most common variation is that all the indicators should be real species, not pseudospecies. In this case, only the first level is checked.

Note that if you increase the number of cut levels from that suggested by the setup wizard, the extra levels do not have appropriate threshold values. You must specify the correct thresholds (increasing from top to bottom) before you can proceed to the next page.

In the next property page (Figure 4), you can select samples that will be omitted in the analysis.

Figure 4

To specify the samples to be "deleted", select them in the left-hand list and click the Select>> button. Depending on the structure of the analysed data, you might prefer your samples to be listed in the increasing order of their indices or sorted alphabetically using their names (labels). You choose the appropriate method using the two radio-button controls at the bottom.

The next wizard page (Figure 5) can be used to select species to be ignored in the analysis, based on their frequency (number of occurrences) in the data.

Figure 5

You should specify the minimum number of occurrences (positive values) that a species must have to take part in the analysis. In Figure 5, the listed five species have obviously just one occurrence (or they might be absent altogether) in the data. Note that the occurrences in the samples deleted in the previous page are ignored here.

In the next page (Figure 6), you can explicitly specify species to omit from the analysis. Note that all the species in your data are shown there, even those with implied omission by the preceding page. The final list of omitted species represents a logical union of the two lists.

Figure 6

The Nondiagnostic Species wizard page (Figure 7) seems similar to the previous one, but species selected here are not completely omitted from the analysis. They are just ignored when WinTWINS looks for diagnostic species, used in the indicator ordination step (see section 3.4). This is useful, for example, if the indicator criterion is to be applied by relatively inexperienced field workers. It may be then better if difficult taxonomic groups are omitted.

Figure 7

In WinTWINS, you can also specify specific weights for some samples and/or species, that are combined with the sample and species weights implied by the reciprocal averaging (RA) algorithm, used in the TWINSPAN method.

In the Sample Weights wizard page (Figure 8), you may specify non-default user weights for samples. Samples not present in the list have the default user weight (1.0).

Figure 8

To remove weighted samples from the list, select the corresponding rows in the list and click the Delete button. Use the Add button to specify additional weighted samples.

A new dialog box is shown (see Figure 9).

Figure 9

In this dialog box, all the samples with default weight are listed. Select in the list the samples to be weighted, enter the required weight (range 0.001 to 1000.0) and click the OK button. To change weight value for an already weighted sample, you must first Delete it in the parental page and then specify new weight for it here.

A similar property page is also available for species. The species weights are applied to all corresponding pseudo-species that result from the specified pseudo-species cut levels. The weights have an effect on the species classifications as well.

The final page of the project setup wizard (Figure 10) collects the options concerning the TWINSPAN algorithm and also the output shown in the WinTWINS main window or stored in the classification file.

Figure 10

The maximum number of division levels (1 to 9) determines the maximum level of recursive splitting for samples (in the sample classification) and for species (in the species classification). Even with large data sets there is little purpose in continuing to subdivide groups beyond a certain limit. Six levels of division are apt to produce about 64 groups. If there is large number of groups, then interpretation can be difficult. Users will soon get a feel for how much division they require with any problem. Rarely will they want fewer than four levels, and almost never more than seven.

The groups of samples or species (in species classification step) smaller than the value specified in the Minimum group size for division field will not be further divided. It must be noted, however, that groups smaller than the specified size will often be formed, e.g. when the natural structure of the data is one large group and a few outliers. It is generally better to control the maximum number of division levels (preceding parameter) than to control the size of generated groups, though there certainly does come a size below which it is not worth dividing groups further.

The value specified in the Maximum number of indicators per division determines the maximum number of species that can be used in the indicator ordination (see section 3.4). If no indicator ordination is desired, then this parameter can be set to zero. If a particular dichotomy can be specified precisely by an indicator ordination based on a smaller number of indicators than the maximum, then the smaller number will be used.

The Number of species in final tabulation is used when creating the two-way table at the end of WinTWINS output (the table is created in two alternative formats, see section 5.7).

It is often inconvenient to clutter this table with rare species. Therefore only the N commonest species are shown, where N is the value specified here.

The diagrams of division display in a simple form how the indicator ordination relates to the refined ordination, and in particular whether the misclassifications are approximately borderline cases (see section 5.4 for further explanation). If the Show diagrams of division in the analysis log option is checked, a text-form scatter diagram is present in the analysis log for each dichotomy (split).

The hierarchical classification of samples and species can be stored in simply formatted text file (known as the "machine-readable copy of solution" in the previous TWINSPAN versions), typically with a .pun extension. If the Store classification in a file option is checked, the output file name should be specified in the Classification file name edit field. WinTWINS project setup wizard fills the file name field with a default value, which is the file path for the input data, with the file extension changed to .pun.

If the final option, named Continue with the project analysis, is checked, WinTWINS continues, after you click the Finish button, with the actual analysis of the project data.

Instead of pressing the Finish button, you can also use the Back button to return to previous pages, to review or modify the settings made there.

4.3 Analysis output

The analysis results are placed (except the classification of individual samples and species, placed in the classification file) in the WinTWINS window, where they can be inspected, copied to other programs and saved into a file with text format (using the Project / Save log … menu command). To make reading of the analysis log shown in the WinTWINS window easier, you can adjust the size of displayed characters using the dialog displayed by the View / Window Font. Note that the output formatting relies in many places on use of a fixed pitch (non-proportional) font, such as Courier or Courier New. If you select instead a typeface with varying width of individual characters, the layout of many tables will be broken.

The output from the TWINSPAN method is described in the next chapter.

5 WinTWINS Output

5.1 Reading the data matrix

The first part of the output concerns the input of the data matrix and the omission of samples. The cut levels are explained above. Only the beginning and end of the data matrix are printed, and all quantities are multiplied by 1000 and rounded to the nearest integer. Values of -1 indicate the end of a sample. Thus a record

1 46000 5 2500 -1 1 56000 6 4500 -1

would refer to two samples, one with species 1 having quantity 46.0 and species 5 having quantity 2.5, and the other with species 1 having quantity 56.0 and species 6 having quantity 4.5. The matrix is printed out in the form that is held in the computer in order to remind the user of two things:

1.How the length of the raw data array relates to the number of non-zero items in the data

matrix; and

2.That quantities smaller than 0.001 are lost in roundoff.

5.2 Entry of parameters

The program gives information about parameters entered when the project was set up. After the input parameters, three important statistics on the data are printed out:

1.Length of data array after defining pseudospecies;

2.Total number of species and pseudospecies; and

3.Number of species, excluding pseudospecies and ones with no occurrences.

With big problems these statistics may be relevant to determining whether it is necessary to reduce the number of pseudospecies to make room for the data in the computer.

5.3 Classification of the samples

Divisions are made successively, according to the scheme below.

1

*

┌─────────┴─────────┐

││

2 3

*0 *1

┌────┴────┐┌────┴────┐

││││

4 5 6 7

*00 *01 *10 *11

Each group is represented by two numbers, one in decimal, one in binary notation. If, in the hierarchy above, the symbol * is replaced by the number 1, then the decimal and binary numbers can be seen to be identical. Thus 10 is the representation of 2 in binary code, 110 represents 6, etc.

In general, group n is divided to obtain group 2n (the ‘negative’ group) and group 2n+1 (the ‘positive’ group). The binary representation of the nodes of the hierarchy is more directly interpretable than the decimal representation, 0 denoting a left arm and 1 denoting a right arm.

***********************************************************************************

DIVISION 1 (N= 20) I.E. GROUP *

Eigenvalue 0.531 at iteration 3

INDICATORS, together with their SIGN

Agr sto 1(+) Ran fla 1(+) Lol per 5(-)

Maximum indicator score for negative group 0 Minimum indicator score for positive group 1

Items in NEGATIVE group 2 (N= 12) i.e. group *0

1.......

2.......

3.......

4.......

5.......

6.......

7....... 10...... 11...... 17...... 1

8...... 1

9......

Items in POSITIVE group 3 (N= 8) i.e. group *1

8....... 9....... 12...... 13...... 14...... 15...... 16...... 20......

NEGATIVE PREFERENTIALS

Ach mil 1( 7, 0) Ant odo 1( 6, 0) Bel per 1( 6, 0) Bro hor 1( 5, 0) Ely rep 1( 5, 1) Hyp rad 1( 3, 0)

Lol per 1( 10, 2) Pla lan 1( 7, 0) Poa pra 1( 11, 3) Tri pra 1( 3, 0) Vic lat 1( 3, 0) Ach mil 2( 6, 0)

Ant odo 2( 6, 0) Bel per 2( 6, 0) Bro hor 2( 5, 0) Ely rep 2( 5, 1) Hyp rad 2( 3, 0) Lol per 2( 10, 2)

Pla lan 2( 7, 0) Poa pra 2( 10, 3) Tri pra 2( 3, 0) Ant odo 3( 5, 0) Bro hor 3( 3, 0) Ely rep 3( 5, 1)

Leo aut 3( 8, 1) Lol per 3( 8, 1) Pla lan 3( 6, 0) Poa pra 3( 9, 2) Rum ace 3( 3, 0) Ant odo 4( 4, 0)

Ely rep 4( 5, 1) Leo aut 4( 4, 0) Lol per 4( 8, 1) Pla lan 4( 3, 0) Poa pra 4( 7, 2) Tri rep 4( 3, 1)

Leo aut 5( 4, 0) Lol per 5( 8, 0) Pla lan 5( 3, 0) Tri rep 5( 3, 1) Lol per 6( 6, 0) Poa tri 6( 3, 1)

POSITIVE PREFERENTIALS

Agr sto 1( 2, 8) Alo gen 1( 3, 5) Ele pal 1( 0, 5) Jun art 1( 0, 5) Jun buf 1( 1, 3) Pot pal 1( 0, 2)

Ran fla 1( 0, 6) Sag pro 1( 3, 4) Cal cus 1( 0, 3) Agr sto 2( 2, 8) Alo gen 2( 3, 5) Ele pal 2( 0, 5)

Jun art 2( 0, 5) Jun buf 2( 1, 3) Pot pal 2( 0, 2) Ran fla 2( 0, 6) Sag pro 2( 3, 4) Cal cus 2( 0, 3)

Agr sto 3( 2, 8) Alo gen 3( 1, 5) Ele pal 3( 0, 5) Jun art 3( 0, 5) Jun buf 3( 0, 3) Cal cus 3( 0, 3)

Agr sto 4( 2, 7) Alo gen 4( 1, 4) Ele pal 4( 0, 5) Jun art 4( 0, 3) Jun buf 4( 0, 2) Bra rut 4( 3, 4)

Agr sto 5( 1, 3) Alo gen 5( 1, 3) Ele pal 5( 0, 2)

NON-PREFERENTIALS

Leo aut 1( 11, 7) Poa tri 1( 8, 5) Rum ace 1( 3, 2) Tri rep 1( 10, 6) Bra rut 1( 9, 6) Leo aut 2( 11, 7)

Poa tri 2( 8, 5) Rum ace 2( 3, 2) Tri rep 2( 9, 5) Bra rut 2( 9, 6) Poa tri 3( 7, 4) Tri rep 3( 4, 3)

Bra rut 3( 4, 4) Poa tri 4( 7, 4) Poa tri 5( 5, 2)

End of level 1

***********************************************************************************

Figure 11 First dichotomy resulting from application of WinTWINS to the dune meadow data. The indicators have been underlined and emboldened. It may seem surprising that the pseudospecies Sag pro 2 (3, 4) is a positive preferential. The reason for this is that it occurs in 3/12 = 25% of the samples in the negative group, and in 4/8 = 50% of the samples in the positive group. It is twice as likely to occur in the positive group as in the negative group and hence qualifies as preferential.

Consider the output for a dichotomy line by line (refer to Fig. 11).

Line 1 gives the number of the group to be divided, both in decimal and in binary notation, together with the number of elements that it contains.

Line 2 gives information on the primary ordination (correspondence analysis). Each iteration involves eight passes of the data.

Lines 3-5 specify the indicator ordination. Each sample is given an ‘indicator score’ found by adding +1 for each positive indicator and -1 for each negative indicator that it contains. The sign (+ or -) of each indicator follows immediately after its name in the list of indicators. The division derived from the indicator ordination is specified on line 5. The two values printed are the maximum indicator score for a sample to be assigned to the negative group, and the minimum score for it to be assigned to the positive group. If these values are A and B respectively, then B is always 1+A.

In the example, only those samples with an indicator score of 0 or less are included in the negative group. Referring to the two-way table (Table 1), it can be seen that two samples (numbers 3,4) have indicators for both sides, namely Lol per 5 (high abundance of Lolium perenne) indicating the drier end and Agr sto 1 (presence of Agrostis stolonifera) indicating the drier end. These samples are assigned to the negative group, but are obviously somewhat borderline – though not so much so as to count as borderline cases for this dichotomy. (In the second division *0, separating the extreme ‘dry’ groups 17,19 from the ordinarily ‘dry’ samples, the sample 18 is signified as a borderline positive.) In the first division, the extreme ‘dry’ samples 17, 19 contain no indicators for the first division but are classified correctly.

The indicators are listed in an approximate order of effectiveness. Thus in Fig. 11, Agrostis stolonifera (positive) is a better indicator than Ranunculus flammula (positive) and Lolium perenne 5 (negative).

5.4 Relation between indicator ordination and refined ordination

This section is somewhat technical, and is for readers who want to understand how borderline and misclassified cases are defined. We consider here the ordinations for the first dichotomy obtained when classifying the Danube meadow data discussed by Mueller-Dombois & Ellenberg (1974). A scatter diagram showing the position of the samples is shown below (Fig.

12).

For ease of computing the ordination is divided into segments. These have no special significance, but are merely a convenient way of calculating where to locate the zone of indifference. The critical zone (Fig. 12) is a zone near the centre of the refined ordination where it is allowable to make divisions. There are five possible positions for the zone of indifference such that it lies entirely within the critical zone. The location of the zone of indifference is selected to minimize the number of misclassified samples (see below).

Segments of the refined ordination are shown along top of the scatter diagram, which is not to scale. The critical zone (segments 5-12) is 20% of the length of the whole ordination. The length of the segments within the critical zone is one-quarter of the length of the segments outside it.

Borderline negatives are those items that are in the negative group and which also lie in the zone of indifference. In Fig. 12, sample 16 is a borderline negative, and is assigned to the negative group because it has an indicator score of -1. The refined ordination is highly polarized, so that there are normally few borderline cases. A borderline case is assigned to its class according to its indicator score, the refined ordination being held in this case to be indecisive.

Misclassified negatives are those samples lying to the left of the zone of indifference but whose indicator score would assign them to the positive arm of the dichotomy. This is regarded as a failure on the part of the indicator ordination to reproduce the dichotomy accurately, and

hence as a ‘misclassification’. Because such samples are outside the narrow zone of indifference, the refined ordination takes priority.

Items in positive group, borderline positives, and misclassified positives are defined as for negatives.

Fig. 12 Relation between indicator ordination and refined ordination for Danube meadow data (not discussed here). The segments of the refined ordination are indicated along the top of the diagram. Segments 5-12 constitute the ‘critical zone,’ and segments 8-11 constitute the ‘zone of indifference’ (abbreviated Z.I.) in the diagram. Samples are designated as ‘borderline cases’ if they lie in the zone of indifference. In this example, only sample 16 is borderline.

5.5 Preferential species and pseudospecies

Negative preferentials are those pseudospecies and species that are at least twice as likely to occur on the negative side as on the positive side. Only those that occur in at least 20% of the samples on the negative side are listed. Values given in brackets are actual numbers of occurrences, so that the entry

Lol per 2( 10, 2)

signifies that Lolium perenne occurs with abundance 2 or more in 10 samples on the negative side of the dichotomy and in 2 samples on the positive side of the dichotomy (Tab. 1). When there is a very uneven split, negative preferentials can easily occur in more samples on the positive side of the dichotomy than on the negative side. Suppose, for example, that the split is

into 2 samples on the negative side and 10 on the positive. Then a species that occurs in 2 samples on the negative side and 4 samples on the positive side is a good negative preferential. It has 100% frequency on the negative side and 40% frequency on the positive side, and is therefore 2.5 times as likely to occur on the negative side as on the positive side.

Positive preferentials are defined as for negative preferentials; non-preferentials are pseudospecies and species that are reasonably common and which are not preferentials. Here again, only those that achieve 20% frequency on one side or the other are listed.

5.6 Species classification

Species are classified by WinTWINS in much the same way as samples. However, there is an important difference in that the species classification is made in the light of the sample classification, and not using the raw data. In fact, the species classification is made on the basis of fidelity, i.e. using the degree to which species are confined to particular groups of samples. For example, in Tab. 1, Aira praecox and Achillea millefolium are both completely faithful to group *0 of the sample hierarchy—i.e. they have no occurrences outside this group. However, Aira is also completely faithful to group *00 of the sample hierarchy, whereas Achillea is not.

Species are assigned attributes according to differing degrees of fidelity to sample groups. For example, both Aira praecox and Achillea millefolium will be registered as having the attribute ‘very highly faithful to group *0’, but Aira is also very highly faithful to group *00, whereas Achillea is not. Indeed, Achillea occurs widely outside group *00, in group *01.

Because the species classification is made on the basis of fidelity to groups defined by the sample classification, altering the level of division of the classification may alter the species classification even if no other changes are made.

The species classification differs from the sample classification in that ‘indicator characters’ are of little interest. It is scarcely of much significance to know that (for example) ‘low level of fidelity to sample group X’ is a preferential attribute of a particular group of species. Consequently, the indicator ordination is omitted from the calculation and output.

If the data set is very large, there may be no room for the new matrix of species' attributes as well as the original matrix. If so, then the species classification is abandoned.

5.7 Order of species and samples

The samples are generally ordered so that the group *1 is smaller than *0, and thereafter so that *01 resembles *1 more than *00 resembles *1. Further divisions are also ordered by similarity. Species are ordered so that species in group *0 generally occur in sample group *0 and species in group *1 generally occur in sample group *1. Thus the ordering of each dichotomy is not arbitrary, but is designed to ensure that the occurrences of species are located on the positive diagonal (Tab. 1).

These orderings are printed out for two purposes. In the first place, the final table may not contain all the less common species, so that the orderings may be convenient as an indication of where the rarer species would come in the arrangement. Secondly and more important, the final tabulation refers to the samples by number, not by name as elsewhere in the program. The (historic) reason for this is that it was difficult to write the names vertically using FORTRAN 4. In WinTWINS, the sample names are available in the second tabulation, in TSV format. This can be read directly into a spreadsheet.

钢琴入门：汤普森简易钢琴教程

钢琴入门：汤普森简易钢琴教程

钢琴入门教程：小汤（汤普森简易钢琴教程》 《小汤》的学习目的是：通过对中央C附近的音符进行反复认音练习，掌握五线谱基础知识，并将线谱与键盘有机地联系起来；通过数拍子，培养正确的节奏感。为下一步的学习打下一个良好的基础 第一册涵盖的音域有意识地作了限制。只介绍中央C以上的五个音和以下的五个音，时值也从全音符到四分音符为止，不再扩展，这样就有可能包括许多以复习形式出现的曲例，不必另找补充教材。总之，本教程的每一册课本都保持各自的完整性，都有各自的书写练习、视谱练习和复习曲目，在以后的各册中还将增加技巧练习。 伴奏： 书中大部分曲例，配有供教师和家长用的伴奏谱。它们都经过精心的创作，使那些简易的小曲听起来尽可能像结构庞大的乐曲的片断。这样做有很多好处，不仅有可能用不同的调弹奏，避免总是徘徊在C大调上，令人生厌；而且可以用自己严格的速度和鲜明的节奏对学生施加影响，特别是他们在弹奏中带有充满活力的节拍重庆，能使学生一开始就“感觉”到节奏的存在。 第一册：《约翰.汤普森简易钢琴教程一》 A 音符节奏：(图 1) （图2） B 音域：中央C为中心，上下五个音（见图2） C 节拍：4/4 、2/4、 3/4 D 调性：C调 重点：主要以认识五线谱、手指编号，一拍二拍三拍四拍音符组合。 《小汤1》： 1.严格地只在白键上弹奏。 2.在附点前较早引入了切分节奏（《雷格泰姆舞》、《弗吉尼，我的故乡》、《摘棉花的老人》、《我的忧愁谁知道》） 3.三拍子的巧妙进入。《小矮人进行曲》紧接《小矮人舞曲》。 4.逐渐增加手指数量。 第一学时

内容：从钢琴键盘至练习题 本课时的知识点：1.音名、五线谱、全音符、二分音符、四分音符、中央C 2.钢琴键盘、基本弹奏姿势、基本手型 本课时的重点：1.中央C在五线谱与钢琴键盘上的位置、四个音符的不同形状及时值 2.正确手型中手指支撑与手腕放松的平衡感觉 本课时的难点：1.音符时值在弹奏中的把握、唱名与音名的本质区别 2.大指弹奏中央C的正确手型 练习把握：1.反复确认键盘上中央C在高音谱表与低音谱表上的不同位 置、 2.四个音符长短时值的练习：我采用一个比喻方法，叫数苹果法，具体做法是，在一个相同距离内（比如学生放在钢琴上的书的长短为例）学生拍一下，嘴数一拍，节拍器响一次等于手拍一下的时间，而我就告诉他们，拍一次一个苹果。这个方法对于年龄小（4-5周岁，下同）的初学学生掌握拍子、用节拍器练习节奏非常适用。 3.大指弹奏中央C时虎口的开度、手指支撑时力的均衡。 时间安排：各年龄段不同，我这里以年龄小的学生为例。开始每天以15-20分钟为基点，然后循序渐进逐步增加时间。 第二学时 学习内容：火车、玛丽有只小羔羊、海军工兵、伞兵。注：1.年龄小的学生，可把内容分为两次上课和练习。2.根据学生的心里特点，老师可把课本内容顺序做一定调整，便于学生集中学习和记忆。 知识点：1.高音谱表D、E音名的位置、低音谱表B、A音名的位置 2.键盘上D、E、B、A的位置 3.左右手2、3手指在键盘上的站立练习 4.曲子四拍子、三拍子中强拍的弹奏 重点：1.高音谱表、低音谱表中新学音名的位置 2.键盘上新学音名位置的确认

钢琴基础教程(五线谱)37747

五线谱钢琴基础教程（1） 基础 篇 1 键盘知识中央C 五线谱入门线上线间八度 2 线上音符线间音符白键7个音符 3 （一）你用的键盘乐器 无论你是拥有一个真正的钢琴，还是一个电钢琴、电子琴或风琴，这里的教程都会教你认识键盘，弹奏五线谱曲子，并学习基本的五线谱知识。 简单说来，钢琴的学习包括认识键盘，将手放到合适的位置，如何控制运用你的手指，如何用双手而不是单单右手来共同弹奏，当然还有如何看懂五线谱钢琴曲谱。 你是用哪种键盘乐器来学习的呢？一共有多少个键盘？我建议你最好使用有61个键盘的那种。如果你的键盘有重力感觉（垂重感键盘）的就更好了，就更接近真实钢琴的机械装置和触感。一般来说简易低档的电子琴的键盘没有重力感设计，键盘的按下时没有什么阻力（比较真实的钢琴键盘就会知 道）。 （二）白键盘黑键盘从哪里开始呢？ 看到键盘可能一开始会迷惑：这么多的键盘---88个键我如何能记住呢？

不过你很快就可以总结出黑键的分布规律：即三个黑键和两个黑键规律性的排列，而且间隔是完 全一样的。 你还会发现上图的白键上有规律的标出绿色的字母C，这个C是出现在两个黑键左面的白键上的。至于这个为何叫C以后会详细介绍。另一个你要注意记忆的是键盘中央的C位置，既所谓的中央C。这是一个需要牢记的位置，你以后会发现这个标志性的C的很多意义。而下面的中央C位置是真实钢琴的 键盘位置。 （三）钢琴键盘的分组五线谱基本要素

上图最上面的就是你经常看到的钢琴的五线谱，中间那个空心圆在短横线的位置---线间就是中央C。这个中央C位置是你弹奏任何一个钢琴曲子都要参考的键盘。 五线谱是记录音乐的一种语言，就象英语、汉语一样，它同样有自己的规则，告诉你弹什么和如何弹奏。最明显的特征就是左端的谱号形式-----高音谱号和低音谱号一起成联合谱表，这是一个标准的钢琴五线谱形式。音符（后面还要讲）在线间或线上。 将中央C的一组白色键盘灰颜色填充，你会发现以C为一个组，七个白色琴键加上五个黑色键盘（两个黑色和三个黑色的)构成一12个键盘组，而且这个C组不断重复。随便用左手或右手弹奏这些不同的 组会发现越往右侧的声音越高，越往左声音越低。 （四）C 和八度 上面的图示显示出在中央C右面和左面的其他的C在五线谱上面的位置。从中可以看出，在键盘上有规律的C的位置排列到了五线谱上面就没有什么规律可循。换句话说，不同C组的键盘位置在五线谱位置上没有什么联系，你只能通过大量的练习和不断的记忆来逐渐掌握。 线上音符

网店美工视觉设计实战教程(全彩微课版)-48481-教学大纲

《网店美工视觉设计实战教程（全彩微课版）》 教学大纲 一、课程信息 课程名称：网店美工：店铺装修+图片美化+页面设计+运营推广（全彩微课版） 课程类别：素质选修课/专业基础课 课程性质：选修/必修 计划学时：21 计划学分：2 先修课程：无 选用教材：《网店美工视觉设计实战教程（全彩微课版）》，何晓琴编著，2018年；人民邮电出版社出版教材； 适用专业：本书可作为有志于或者正在从事淘宝美工相关职业的人员学习和参考，也可作为高等院校电子商务相关课程的教材。 课程负责人： 二、课程简介 随着网店的迅速普及和全民化，衍生了“淘宝美工”这个针对网店页面视觉设计的新兴行业。本书从淘宝美工的角度出发，为淘宝卖家提供全面、实用、快速的店铺视觉设计与装修指导。主要包括网店美工基础、图片调色、图片修饰、店铺首页核心模块设计、详情页视觉设计、页面装修、视觉营销推广图制作等，最后针对无线端进行首页、详情页视觉的设计与装修。本书内容层层深入，并通过丰富的实例为读者全方面介绍淘宝美工在日常工作中所需的知识和技能，有效地引导读者进行淘宝店铺装修的学习。 本课程主要对淘宝美工的设计基础和方法进行详细介绍，通过学习该课程，使学生了解网店美工的基本要求，以及掌握网店的设计与制作。 三、课程教学要求

体描述。“关联程度”栏中字母表示二者关联程度。关联程度按高关联、中关联、低关联三档分别表示为“H”“M”或“L”。“课程教学要求”及“关联程度”中的空白栏表示该课程与所对应的专业毕业要求条目不相关。 四、课程教学内容

五、考核要求及成绩评定 注：此表中内容为该课程的全部考核方式及其相关信息。 六、学生学习建议 （一）学习方法建议 1. 理论配合实战训练进行学习，提高学生的实战动手能力； 2. 在条件允许的情况下，可以申请一个网店，进行深入学习； 3. 提高学生的是设计感和审美能力; （二）学生课外阅读参考资料 《网店美工：店铺装修+图片美化+页面设计+运营推广（全彩微课版）》，何晓琴编著，2018年，人民邮电出版社合作出版教材

钢琴入门：汤普森简易钢琴教程

钢琴入门教程：小汤（汤普森简易钢琴教程》 《小汤》的学习目的是：通过对中央C附近的音符进行反复认音练习，掌握五线谱基础知识，并将线谱与键盘有机地联系起来；通过数拍子，培养正确的节奏感。为下一步的学习打下一个良好的基础 第一册涵盖的音域有意识地作了限制。只介绍中央C以上的五个音和以下的五个音，时值也从全音符到四分音符为止，不再扩展，这样就有可能包括许多以复习形式出现的曲例，不必另找补充教材。总之，本教程的每一册课本都保持各自的完整性，都有各自的书写练习、视谱练习和复习曲目，在以后的各册中还将增加技巧练习。 伴奏： 书中大部分曲例，配有供教师和家长用的伴奏谱。它们都经过精心的创作，使那些简易的小曲听起来尽可能像结构庞大的乐曲的片断。这样做有很多好处，不仅有可能用不同的调弹奏，避免总是徘徊在C大调上，令人生厌；而且可以用自己严格的速度和鲜明的节奏对学生施加影响，特别是他们在弹奏中带有充满活力的节拍重庆，能使学生一开始就“感觉”到节奏的存在。 第一册：《约翰.汤普森简易钢琴教程一》 A 音符节奏：(图 1) （图2） B 音域：中央C为中心，上下五个音（见图2） C 节拍：4/4 、2/4、 3/4 D 调性：C调 重点：主要以认识五线谱、手指编号，一拍二拍三拍四拍音符组合。 《小汤1》： 1.严格地只在白键上弹奏。 2.在附点前较早引入了切分节奏（《雷格泰姆舞》、《弗吉尼，我的故乡》、《摘棉花的老人》、《我的忧愁谁知道》） 3.三拍子的巧妙进入。《小矮人进行曲》紧接《小矮人舞曲》。 4.逐渐增加手指数量。 第一学时

内容：从钢琴键盘至练习题 本课时的知识点：1.音名、五线谱、全音符、二分音符、四分音符、中央C 2.钢琴键盘、基本弹奏姿势、基本手型 本课时的重点：1.中央C在五线谱与钢琴键盘上的位置、四个音符的不同形状及时值 2.正确手型中手指支撑与手腕放松的平衡感觉 本课时的难点：1.音符时值在弹奏中的把握、唱名与音名的本质区别 2.大指弹奏中央C的正确手型 练习把握：1.反复确认键盘上中央C在高音谱表与低音谱表上的不同位置、 2.四个音符长短时值的练习：我采用一个比喻方法，叫数苹果法，具体做法是，在一个相同距离内（比如学生放在钢琴上的书的长短为例）学生拍一下，嘴数一拍，节拍器响一次等于手拍一下的时间，而我就告诉他们，拍一次一个苹果。这个方法对于年龄小（4-5周岁，下同）的初学学生掌握拍子、用节拍器练习节奏非常适用。 3.大指弹奏中央C时虎口的开度、手指支撑时力的均衡。 时间安排：各年龄段不同，我这里以年龄小的学生为例。开始每天以15-20分钟为基点，然后循序渐进逐步增加时间。 第二学时 学习内容：火车、玛丽有只小羔羊、海军工兵、伞兵。注：1.年龄小的学生，可把内容分为两次上课和练习。2.根据学生的心里特点，老师可把课本内容顺序做一定调整，便于学生集中学习和记忆。 知识点：1.高音谱表D、E音名的位置、低音谱表B、A音名的位置 2.键盘上D、E、B、A的位置 3.左右手2、3手指在键盘上的站立练习 4.曲子四拍子、三拍子中强拍的弹奏 重点：1.高音谱表、低音谱表中新学音名的位置 2.键盘上新学音名位置的确认 3.左右手2、3指断奏练习的力量感觉

钢琴入门教程之手指操

钢琴入门教程之手指操 钢琴入门教程之手指操 如何提高儿童的兴趣,是当今幼儿钢琴教学中的一个难题.下面就钢琴入门教程中手指操的相关知识点传授给大家，希望对你们有益。 一手臂放松操 面对钢琴零起点的孩子,在开始正规的课程之前,我总是先让他们信识身体的哪一部分是手臂,手臂可以怎样活动,怎样才能找到手臂 的重量,重量又是如何落下.于是编了以下三节手臂放松操,由易到难,邮简到繁,让孩子们在游戏中轻松愉快地认识了手臂以及手臂在演奏 钢琴时的规范动作.为钢琴演奏的第一步"非连音的弹奏"学习做好了 准备. 1.手臂放松的练习 通过长期观察,我发现孩子们在弯腰的时候比站立时更容易找到 手臂放松的感觉,于是编了以下的练习操,让他们看着自己的手臂放 松的甩动,在教他们认识手臂的同时也找了手臂的重量. "大象甩鼻子:小朋友们请站好,两腿稍微分开,距离与肩宽.弯腰 九十度,手臂放松垂直.手臂像大象鼻子一样,左右服起来. 2.手臂用力的练习 通过前一节前一节"手臂放松操"的练习,有朋友们能够在弯腰的 状态下顺利地找到手臂的重量.此时马上站立起来做以下的第二节操,还象前一节那样前后服甩动.把刚才的整党带过来,就能比较方便的 找到站立状态下的手臂重量.随着摆动幅度的加大,小朋友们还能体 会到重量的由小到大,由轻到重,在找到重量的同时学会如何运用,为"非连音的弹奏"打下扎实的基础.

"小猴荡秋千"小朋友们请站好,两腿稍微分分开,距离与肩宽.两个手臂同时向前后摆动,就象小猴在荡秋千一样越摆越大,越甩越高. 3.手臂协调的练习 第三节操"手臂放松操"可以有多种练习方法:单干手向前甩动,单手向后甩动,双手同时向前甩动,双手同时身后甩动以及双手反向甩动. "荷兰大风车"小朋友们请站好,双手顺时针甩手臂,双手逆时针甩手臂.右手顺时针甩手臂,左手逆时针甩手臂,做两个荷兰大风车. 能过以一一系列的练习,小朋友们已经完全了解手臂的重量,能够熟练地运用重量,并且在用力的同时能够感觉到腰部的支持和身体的平衡,身体的协调能力也得到了很大的锻炼. 二五指练习操 通过第一部分三节操的训练,小朋友们已经能够在弯腰和站立的状态下顺利地找到手臂的重量,并能放松自如的运用,可以说这是个很好的开始.现在我们要训练坐着的状态下找到手臂的重量,让这个重量完全放松,自然地掉在大腿上.这样就可以非常方便地把手臂的重量掉在钢琴上了. 1.手臂重量垂落的练习 在做这个练习时要注意检查坐姿是否端正,手臂弯曲的角度和手掌的方向是否正 确.手臂重量掉在腿上时,是用手腕落下而不是手掌拍打. "宝宝抱大熊"请小朋友们坐在琴凳上,双脚放平,自然分开.两个手臂慢慢抬起,与肩同高,做抱大熊的姿势.手掌向下,手臂自然垂落在腿上.重复抬起--落下的动作. 2.手指关节的练习 为了让小朋友们在弹钢琴时能够拥有比较漂亮的手型和比较独立的手指,需要一些手掌肌肉和手指各关节的练习.此时可以做以下的

钢琴基础教程,最经典钢琴入门教程

第十八课本教程由牛津爱乐钢琴节奉献练习要求：以下的每个学习环节，请做到清楚要求，掌握应学的知识和技巧再进入下一环节的学习，不可走马观花。每一首乐曲的练习都要采取很慢的速度进行，只有在慢速的练习中才能更好地注意手型、避免错误。翻到课本第三十八页，乐曲《牛仔之歌》是一首复习前面所学知识的曲子，首先请注意乐曲的调号，要记住，这首乐曲中的si（B）要降低半音。在开始练习之前我们要有这样的习惯，先细心地把乐曲看几遍。看看曲子中有什么特点。这样练习起来可以避免错误。记住这首乐曲要分手分段练习，乐曲分成两段，第一段从第一小节（只有一拍）到第十七小节。其余的为第二段。具体就是先分手练习第一段，分手练好了两手合起练好第一段，然后再进入第二段的分手练习?6?7?6?7 第五、九、十三、十七小节中两手都是休止符的拍子要注意休止正确的时值，有的学生会漏掉这些休止的拍子。第八、九小节中的si（B）有还原记号，不用降，其它的si（B）都要降。乐曲中没有连线的音用断奏，有连线的音用连奏，有点的音用跳音弹奏，这些都要清楚表现。弹出自信、活泼的感情出来。请按照示范乐曲的速度弹奏：《牛仔之歌》：《牛仔之翻到课本第三十九页，我们来学习《小雷格泰姆》，这是一首切分音节奏的乐曲。注意弹出切分音节奏“弱强-弱”的特点出来。乐曲中左手的跳音要弹得轻巧，注意休止

符的时值要准确。请按照示范乐曲的速度弹奏：《小雷格泰姆》翻到课本第四十页《假日之歌》，这是我们接触的第一首三个升记号的乐曲。升的是f（fa）、c（do）、g（sol），这是a 大调的调号，要记住要升的三个音，避免在弹奏时忘记了。练习乐曲之前先练习一下“预备练习”和a 大调的音阶，通过预备练习我们可以熟悉乐曲的左手和弦音型，通过音阶的练习我们可以熟悉右手的手位。注意每一乐句的大连线，要求我们用连奏来弹奏，但是连线与连线之间就要用断奏了，手要拿起来，就象说话的呼吸一样。注意乐曲的力度记号的表现。mf、mp、渐强、渐弱要表达好，让别人听到你的弹奏就能感受到力度的变化。十三小节的延长记号要注意，这里弹成三拍就好了。左手虽然没有要求连奏，但这里还是要用连奏比较合理，也比较容易。翻到课本第四十一页的《走，走，走》，乐曲中出现了一种新的拍号：六八拍子。什么意思呢？首先我们要知道拍号是由上下两个数字组成的，上面的数字的意思就是每小节有多少拍，下面的数字就是以多少分音符为一拍。因为我们学过的拍号有二四拍子、三四拍子、四四拍子，下面的数字都是四，就是说都是以四分音符为一拍的，而我们这节课学的是六八拍子，下面的数字是八，所以就是以八分音符为一拍，那么我们知道八分音符的时值是四分音符的一半，所以在六八拍子中音符的拍数就要算多一倍了。看看课本里

钢琴基础教程1教案

钢琴基础教程1教案

篇一：钢琴基础教学教案1 钢琴基础教学教案 课时：1 课时 课型：集体课 执课教师：陈仕进 【教学内容】 1、基本技术：（1）熟练掌握《孩子们的哈农第2条》。 （2）熟练掌握双手弹奏c大调、a小调音阶。 2、练习曲：贝伦斯作品70之35。 3、乐曲：学习《天真烂漫》 【教学目标】 1、基本技术：（1）能有针对性地训练各个手指，做到保持松弛的肩膀，完好的手型来弹奏。 （2）熟练掌握双手弹奏c大调、a小调音阶，熟练大、小调指法，能够积极主动挥动掌关节连接双手，使手指有力均衡发展。 2、练习曲：熟练掌握双音和跳音的演奏技巧，左手掌握好用正

确的指法弹奏和弦的技巧，注意力度的对比变化。 3、乐曲：熟练掌握移位动作，做好手指转换练习，注意连跳结合，在十六分音符跑动时注意要均匀，注意乐曲中出现的音乐记号和小连线。 【教学重点】学习手指转换的技巧。 【教学难点】十六分音符的音阶跑动时注意手指弹奏的均匀和独立性。 【教学过程】 1、强调弹奏的基本要求： 正确的坐姿：（1）重心稳定、靠前、身体放松，腰部要直，上身略向前倾；（2）座位的高低与座位离琴的远近要根据自身的条件来决定；（3）养成弹奏前调整座位的习惯； 正确的手型：最符合自然状态的手型就是好的手型手的状态是手指略向里自然弯曲，手呈半圆形，指尖和手腕基本在一条水平线上，手型既舒服自然、支撑力也最强。

利用以上要求来弹奏《孩子们的哈农》。 2、师：在琴上弹出《少女的祈祷》的音乐片段，并请学生注意乐曲中快速跑动乐句的特点。 生：思考乐句的规律是上行还是下行的？什么是音阶？师：要求学生回答什么是音阶。 生：回答问题。 师：讲解c大调、a小调音阶的指法（圈起来的地方手指需要转换），即拇指应在3指下穿过，讲完后示范演奏一遍音阶，并请学生演奏一遍。 生：演奏音阶。 师：讲出学生的不足之处，并再一次示范音阶的正确弹法。生：改正错误，然后先分手练习，后合手练习。 师：总结音阶的弹奏要点：能够积极主动的挥动掌关节自然连接双手，使手指有力均衡发展。 3、出示贝伦斯作品70之35 （注意圈起来的地方手指的转换和谱表的变化）

拜厄钢琴基础教程指导分析

拜厄钢琴基础教程指导分析 第一节课 正确的坐姿： 养成良好的坐姿是至关重要的，坐的位臵应该在琴键的正中间， 也就是正对中央C的位臵，椅子与琴的距离大约与小臂的长短一致 臂部坐在琴凳的二分之一处。不要太靠里，也不要太靠外，臂部成为 一个支撑点，双脚自然，平稳地放在踏板前，上身须坐正，保持平稳， 不能弯腰驼背，椅子必须调到适当的高度，以肘部，腕部与键盘基本 持平为宜，上手臂与小臂形成一个略大于90度的纯角即可，双臂自然 垂下，呼吸平稳，全身放松，体会一下从头，脖子，腰，背，肩，手腕，手臂，手指全部畅通无阻的感觉。 正确的手型： 手的姿势应成圆弧形，手腕应略微比手掌低一点。手指关节要保持稳定，手掌不向内凹。大拇指应略微向内弯曲，与二指保持一定距离。大拇指弹奏时要用指端外侧触键，但面积不要太大，第一关节不要碰到键面。小指应立起来，用指尖的肉触键，手指的第一关节不要塌。 第二节 第二节课 单手与双手的触键练习 （1）每一个手指下落触键时，前一个已触键的手指应立即抬起离键。 起，落两个动作应同时完成。 （2）手指触键要平稳，扎实，用力均匀，切忌用力过猛，过分用力 会使手与手指的肌肉处于紧张，僵硬的状态，造成弹奏粗重，笨拙。 生硬，不利于进一步地学习 刚开始练习时，可以用很慢的速度弹奏，每一条练习按照教师的要求 反复多次。 对于初学者来说，在这个阶段的练习中，可以采取断奏和非断奏两种 方法来练习。 断奏：训练手臂放松，保持良好的手型和手指的支撑。按照谱面的音高 和指法自然地抬起手臂，手指应保持自然弯曲状，臵于琴键上方谱架的 位臵，然后让手臂放松，毫不用力地掉下，用已准备好的手指，稳稳地 支撑在琴键上，其余的手指和手臂保持自然放松，手掌不要塌。 非连奏：每当一个手指触键时。前一个已触键的手指应立刻抬起，高抬 指不仅可以训练手指的肌肉与骨骼，也能增强手指的独立性。考虑小朋友的肌肉和骨骼均未发育成熟，尚不能完全胜任强有力的弹奏，所以开始阶段幼儿用断奏方法练习会比较适合。

淘宝网店二维码使用教程全攻略

一、什么是二维码——还记得超市的条形码吗？ 谈起“二维码”，可能很多人会犯糊涂。但是与它类似的“一维条形码”广泛地运用于超市商品识别，却是我们每个人都十分熟悉的。二维码正是“一维条形码”发展的“高级阶段”，在一个小小的方块里面包含一条链接地址，引导使用者通过扫描设备（如手机）快速进入相应 的网址。 图1：一维条形码图2：淘宝二维码 现在，淘宝为卖家们提供二维码在线生成的工具，您可以将您的店铺和宝贝的“手机浏览链接”转化成二维码印制出来，夹在包裹中、印在优惠券上甚至是你的商品上。举例来说，接收包裹时，买家拿到印有二位码的优惠券，此时，他们只需用手机的摄像头“照”一下这个黑白相间的小方块，就可以快速地通过手机进入您的店铺中。二维码还有更多的妙用和更多的好处。 图3：生活中使用淘宝二维码的场景

二、淘宝二维码妙在何处——轻轻一扫客源不断！ 1. 好处在哪里？ 淘宝买家通过手机上的二维码识别软件,扫描卖家发布的淘宝二维码,可以直接找到卖家的促销活动，店铺首页，宝贝单品。免去输入网址、关键词搜索的麻烦。 淘宝卖家可以将二维码印刷到包裹中的宣传物上（如优惠券、宣传册），随包裹发给买家，吸引买家通过二维码进入店铺进行二次购买，为您带来源源不断的客流。 您可以在PC店铺和商品详情页中贴出二维码，使顾客可以在手机中快速收藏，随时随地光顾您的店铺！ 卖家还可以考虑在平面媒体（如《淘宝天下》）上发布带有二维码的促销活动。对于有能力大卖家，还可以在自己的商品上贴上相应的二维码。 图4：二维码的引流作用 2. 买家的操作方法 有软件和摄像头的买家：淘宝合作的免费二维码软件有淘宝Android版、快拍、QuickMark、码上淘、魔印等，手机访问https://www.wendangku.net/doc/978118933.html,可快速下载。这些手机软件提供二维码扫描功能，只要买家打开这些软件，将摄像头对准二维码1秒中左右，识别成功后手机将自动进入对应的网页。 没有软件或者摄像头的买家可以在手机上进入https://www.wendangku.net/doc/978118933.html,页面，输入活动码，同样能够进入到您所设置的链接中。 图5：买家的使用方法

钢琴基础教程1教案

篇一：钢琴基础教学教案1 钢琴基础教学教案 课时：1 课时 课型：集体课 执课教师：陈仕进 【教学内容】 1、基本技术：（1）熟练掌握《孩子们的哈农第2条》。 （2）熟练掌握双手弹奏c大调、a小调音阶。 2、练习曲：贝伦斯作品70之35。 3、乐曲：学习《天真烂漫》 【教学目标】 1、基本技术：（1）能有针对性地训练各个手指，做到保持松弛的肩膀，完好的手型来弹奏。 （2）熟练掌握双手弹奏c大调、a小调音阶，熟练大、小调指法，能够积极主动挥动掌关节连接双手，使手指有力均衡发展。 2、练习曲：熟练掌握双音和跳音的演奏技巧，左手掌握好用正确的指法弹奏和弦的技巧，注意力度的对比变化。 3、乐曲：熟练掌握移位动作，做好手指转换练习，注意连跳结合，在十六分音符跑动时注意要均匀，注意乐曲中出现的音乐记号和小连线。 【教学重点】学习手指转换的技巧。 【教学难点】十六分音符的音阶跑动时注意手指弹奏的均匀和独立性。 【教学过程】 1、强调弹奏的基本要求： 正确的坐姿：（1）重心稳定、靠前、身体放松，腰部要直，上身略向前倾；（2）座位的高低与座位离琴的远近要根据自身的条件来决定；（3）养成弹奏前调整座位的习惯； 正确的手型：最符合自然状态的手型就是好的手型手的状态是手指略向里自然弯曲，手呈半圆形，指尖和手腕基本在一条水平线上，手型既舒服自然、支撑力也最强。 利用以上要求来弹奏《孩子们的哈农》。 2、师：在琴上弹出《少女的祈祷》的音乐片段，并请学生注意乐曲中快速跑动乐句的特点。 生：思考乐句的规律是上行还是下行的？什么是音阶？师：要求学生回答什么是音阶。 生：回答问题。 师：讲解c大调、a小调音阶的指法（圈起来的地方手指需要转换），即拇指应在3指下穿过，讲完后示范演奏一遍音阶，并请学生演奏一遍。 生：演奏音阶。 师：讲出学生的不足之处，并再一次示范音阶的正确弹法。生：改正错误，然后先分手练习，后合手练习。 师：总结音阶的弹奏要点：能够积极主动的挥动掌关节自然连接双手，使手指有力均衡发展。 3、出示贝伦斯作品70之35 （注意圈起来的地方手指的转换和谱表的变化） 师：要求学生视奏练习。 生：认真视谱，并马上慢速练习。

dw网页淘宝设计模板

竭诚为您提供优质文档/双击可除dw网页淘宝设计模板 篇一：dreamweaver在淘宝美工课中的应用 泉州新东方叶生方老师整理 dreamweaver在淘宝美工课中的应用 一、店铺装修（网页技术） 专业型课程分两个阶段：1、店铺装修基础阶段，2、html+css行内样式部分。 （一）实用型阶段——店铺装修基础 1、认识旺铺及类型、模板、模块、布局；旺铺20xx标准版拓展版20xx新版 2、ps切片导出web代码 3、dw表格排版 4、分类导航设计——ps切片、dw表格、热区三种技术综合运用 5、旺铺宽屏效果制作（页面背景、店头自定义、自定义大图三部分组成） 6、图片背景、旺旺代码、收藏代码、滚动代码 7、识别哪些效果属于模块功能，那些效果可以自定义

实现，常用的促销工具（限时打折、满就送、团购宝、搭配减价、会员管理等） 8、淘宝首页整体风格设计、“宝贝描详情页”风格设计 9、html常用标注介绍table、tR、td、img、a等用功能 （二）专业提升阶段——html+css行内样式 10、dw使用的注意要点以及认识tabletRtdFontdiVspanh1imgapliulmarquee等常用标注。 11、表格结构分析，学会手写简单的网页代码 12、认识css盒子模型 13、了解css排版基本方法——实际表格相似导航效果 14、运用css设计宝贝详情页版式、利用p、img标注的css属性设置版式 15、css美化表格 16、了解淘宝widget库的作用与意义——悬浮旺旺、宝贝放大镜等等特效 篇二：用dw做出漂亮的模板 如何用dw做出漂亮的促销表格模板 很多的卖家在逛淘宝店时会发现很多的店铺装修很美观，如打折模块和团购模块，自己想去问店主怎么制作，但是有几个淘宝卖家会告诉你呢，不过不要着急，下面让开淘网为你详解一番吧。今天我们讲的是如何用dReamweaVeR做

钢琴基础教程

《钢琴基础教程》 本套VCD共四片，系高等师范院校教材《钢琴基础教程》的示范演奏配套有声读物。 由上海音乐学院根据该教程内所附的《春之歌》《夜莺》等124首中外钢琴名曲和小品摄制而成。 第一片曲目： 1引子2春之歌3牧马童之歌4农舞5花灯 6幽默曲7天真烂漫8太阳出来9沂蒙山小调10窗花舞 11风笛舞曲12苏格兰舞13扎红头绳14土耳其进行曲15弦子舞 16可爱的家17瑶族长鼓舞18绿袖子19士兵进行曲20老黑奴 21古老的法兰西歌曲22波尔卡舞曲23斗牛士之歌24摇篮曲25进行曲 26到敌人后方去27勇敢的骑士28盼红军29南泥湾30小丑 31小奏鸣曲32抒情曲33快乐的农夫34舞曲35小奏鸣曲 36《魔笛》主题变奏37未完成交响曲主题38小奏鸣曲39牧童的歌40踏板前奏曲之一41牧羊人42波尔卡扬卡舞曲43一根扁担44战斗进行曲 第二片曲目： 1引子2花鼓3回旋曲4塔兰台拉5练习曲 6松溪河水盘山流7再会8啊，我的太阳9叙事曲10小广板 11小奏鸣曲12北风吹13致野玫瑰14纺织歌15绣荷包 16快板17小托卡塔18小奏鸣曲19珍重再见20深思 21骑士22小夜曲23舞曲24进行曲25阿拉木汗 26夜莺27舞曲28郊外曲29春30舞曲 31桑塔露琪亚32共产儿童团歌33G大调小步舞曲34马赛曲35小奏鸣曲 36山村新歌37小号手 第三片曲目： 1大八板2威尼斯船歌3恰空4重归苏莲托5献给爱丽丝 6红头绳7民间音乐风8友情9草原情歌10纪念册的一页 11小奏鸣曲p62 12欢乐的牧童13抒情圆舞曲14奏鸣曲p84 15序曲16视唱曲17摇篮曲18梦幻19奏鸣曲p103 20玛祖卡 第四片曲目： 2捉迷藏丁善德3跑马溜溜的山上西康民歌4四月－松雪草柴可夫斯基5夜曲菲而德6幻想曲莫扎特 7牧童短笛贺绿汀8奏鸣曲斯卡拉蒂9采茶扑蝶刘福安10圆舞曲勃拉姆斯11致春天格里格 12奏鸣曲莫扎特13湖南花鼓奚其明14练习曲莫什科夫斯基15奏鸣曲贝多芬16陕北民歌变奏曲周广仁 17乌苏里船歌陈铭志编18夜莺格林卡19阿拉伯风德彪西20猎歌门德尔松

拜厄钢琴基本教程BEYER精编版

拜厄钢琴基本教程 B E Y E R 公司标准化编码 [QQX96QT-XQQB89Q8-NQQJ6Q8-MQM9N]

拜厄钢琴基础教程指导分析 第一节课 正确的坐姿： 养成良好的坐姿是至关重要的，坐的位置应该在琴键的正中间， 也就是正对中央C的位置，椅子与琴的距离大约与小臂的长短一致 臂部坐在琴凳的二分之一处。不要太靠里，也不要太靠外，臂部成为 一个支撑点，双脚自然，平稳地放在踏板前，上身须坐正，保持平稳， 不能弯腰驼背，椅子必须调到适当的高度，以肘部，腕部与键盘基本 持平为宜，上手臂与小臂形成一个略大于90度的纯角即可，双臂自然 垂下，呼吸平稳，全身放松，体会一下从头，脖子，腰，背，肩，手腕，手臂，手指全部畅通无阻的感觉。 正确的手型： 手的姿势应成圆弧形，手腕应略微比手掌低一点。手指关节要保持稳定，手掌不向内凹。大拇指应略微向内弯曲，与二指保持一定距离。大拇指弹奏时要用指端外侧触键，但面积不要太大，第一关节不要碰到键面。小指应立起来，用指尖的肉触键，手指的第一关节不要塌。 第二节课 单手与双手的触键练习 （1）每一个手指下落触键时，前一个已触键的手指应立即抬起离键。 起，落两个动作应同时完成。 （2）手指触键要平稳，扎实，用力均匀，切忌用力过猛，过分用力 会使手与手指的肌肉处于紧张，僵硬的状态，造成弹奏粗重，笨拙。 生硬，不利于进一步地学习 刚开始练习时，可以用很慢的速度弹奏，每一条练习按照教师的要求 反复多次。 对于初学者来说，在这个阶段的练习中，可以采取断奏和非断奏两种 方法来练习。 断奏：训练手臂放松，保持良好的手型和手指的支撑。按照谱面的音高 和指法自然地抬起手臂，手指应保持自然弯曲状，置于琴键上方谱架的 位置，然后让手臂放松，毫不用力地掉下，用已准备好的手指，稳稳地 支撑在琴键上，其余的手指和手臂保持自然放松，手掌不要塌。 非连奏：每当一个手指触键时。前一个已触键的手指应立刻抬起，高抬 指不仅可以训练手指的肌肉与骨骼，也能增强手指的独立性。考虑小朋友的肌肉和骨骼均未发育成熟，尚不能完全胜任强有力的弹奏，所以开始阶段幼儿用断奏方法练习会比较适合。 右手触键练习一共有24条，主要是针对右手单手的五指练习，弹奏时必须慢速且耐心地反复多次练习。 第三节 练习要点：（右手左手触键练习） NO1：本曲是针对右手大拇指和2指的练习。大拇指是

淘宝装修 SDK高级模板开发流程

淘宝网装修市场 高级模板设计指南1

目录 1、本地开发环境 (3) 1.1 什么是SDK (3) 1.2 下载SDK (3) 1.3 安装SDK (4) 1.4 SDK的目录结构 (7) 1.5 启动SDK (8) 2、使用SDK后台 (10) 2.1 创建模板 (10) 2.2 预览模板 (17) 2.3 配置SDK本地属性 (18) 3、模板的标准结构 (18) 4、设计模块 (18) 4.1 创建相关文件资源 (18) 4.2 编写模块代码 (19) 4.2.1 引入标识 (19) 4.2.2 使用KISSY效果 (19) 4.2.3 使用模块参数 (19) 4.2.4 调用数据接口 (20) 4.3 配置模块信息 (20) 5、设计页面 (22) 5.1 结构化页面 (22) 5.2 设计页面 (24) 5.2.1 引入单个模块 (24) 5.2.2 划分页面片区 (25) 5.2.3 配置页面信息 (26) 6、特殊页面要求 (27) 6.1 宝贝详情页面 (27) 6.2 宝贝列表，文章列表页面 (27) 6.3 不可装修的页面 (27) 7、调试模板装修效果 (28) 8、关于白名单过滤 (28) 9、PHP函数 (28) 10、发布模板到装修系统 (28) 2

1、本地开发环境 1.1什么是SDK SDK是淘宝提供给设计师在本地设计模板的软件开发包，SDK模拟了淘宝店铺环境。 SDK主要包括三个部分：Server，PHP引擎和模拟数据。 Server是指SDK会在本机上启动一个Server，这样我们可以通过浏览器浏览设计的作品。PHP引擎是指设计师只能使用PHP作为模板设计的语言。 模拟数据是指SDK包括了淘宝各个典型的店铺，这样设计师在设计模板时不用考虑实际的数据库。 目前，提供Windows操作系统和Mac操作系统的SDK工具。 1.2下载SDK 装修市场后台下载安装包，安装至本地，创建SDK web控制台。您将使用淘宝ShopSDK 来开发和上传设计模板。在开始设计之前，需在本地部署SDK环境。 设计师登录装修市场后，点击“设计师后台”导航按钮进入。(加入设计师注册流程帐号。) 进入设计师后台界面： 3

钢琴入门教程(1-18)

钢琴入门教程（第一课） 练习要求：以下的每个学习环节，请做到清楚要求，掌握应学的知识和技巧再进入下一环节的学习，不可走马观花。每一首乐曲的练习都要采取很慢的速度进行，只有在慢速的练习中才能更好地注意手型、避免错误。 一、学学坐姿 首先，一开始要先学好正确的坐姿。坐的姿势以自然,端正,有利于弹奏为原则,不要僵硬,紧张,或过于松懈。 身体的重心要依靠臀部和脚尖支撑。一般来说,弹奏者只应坐到标准琴凳的三分之一左右的地方,不要把座位都坐满,或把身子靠在椅背上（标准的琴椅都是没背可以靠的啦）。因为这样才能让我们在弹奏时身体能自然地左右摇摆延伸。上身直立,腰不要塌陷,这样,弹奏者可以从腰或者更下一些部位用上力量,上身略向前倾,使重心略靠前,便于将力量向前传送至手指。建议弹琴要采用标准的琴椅，不要采用其它类型的椅子。 座位的高低,要根据弹奏者的高矮来决定,原则是肘,前臂,手,手与键盘保持一个相同的水平高度,或略高于键盘。身高不够的学生要应将琴凳垫高使到符合上面提到的原则。 座位的远近,也要视弹奏者的胳膊的长短来决定,要使在弹奏时,胳膊可以自如的移动。一般说，就是膝盖刚好对齐钢琴最接近演奏者的地方。脚的位置,一般可以放在踏板附近,这个位置比较适合于演奏,也有利于使用踏板。身高不够的学生因为琴凳垫高使到脚不着地，应该用小凳子垫脚。 二、初步认识五线谱 经常有人这样问我：“五线谱是不是很难学啊？”其实并不难，而且很容易，因为我们并不需要一下子就全部把它学完，我们只需在学钢琴的过程中跟着一点点地学习。所以下面我也不用讲很多，只是说一下最开始的时候应该知道的。在以后的学习中我们再“碰到什么学什么”。 1.找到钢琴上的中央C，请看看课本第四页。 2.翻到课本第六页和第七页，学习有关的五线谱知识。在这两页我们应该掌握的知识是什么是全音符、数多少拍？什么是高音谱号、什么是低音谱号？中央C记录在乐谱的什么地方？什么是小节和小节线？记住十个手指的编号。 三、开始弹琴啦 翻到课本第8页，下面我们就开始了学琴的第一首乐曲，这首乐曲很简单，就是四个全音符的中央C，在弹这首乐曲的时候我们应该采用“全臂断奏法”来进行演奏。 全臂断奏法的练习主要的作用在于找到手臂的自然落体重量通过指尖沉落在琴上的感觉，使到弹琴时手臂放松、灵活，手指触键稳、准。初学者会觉得很枯燥,很没有成就感。但非连音的练习是连音弹奏以及

piano钢琴基础教程

目录 序 第一章：钢琴技巧 I 技巧 I-1 目的 I-2 什么是钢琴技巧？ I-3 技巧与音乐 I-4 基本方法，诠释，音乐训练，绝对音感 II 钢琴练习的基本步骤 II-1 练习的程序 II-2 手型 II-3 座椅的高度以及与琴的距离 II-4 开始练一首新曲子：分析与聆听 II-5 从最困难的段落开始练习 II-6 将困难的段落缩短：段落式的练习 II-7 分手练习：获得技巧 II-8 连续性的规则 II-9 和弦起音 II-10 自由落体，和弦练习，及放松 II-11 平行组合 II-12 学习与记忆 II-13 速度，选择练习的速度 II-14 如何放松 II-15 学习后的进步 II-16 慢速练习的危险━直觉性方法的陷阱II-17 慢弹的重要性 II-18 指法 II-19 精确的节拍与节拍器 II-20 较弱的左手：用一只手来教另一只手

II-21 培养耐力，呼吸 II-22 坏习惯：钢琴家的最大敌人 II-23 延音踏板 II-24 柔音踏板，音色，及琴弦的常态震动 II-25 合手练习：以萧邦的幻想即兴曲为例 II-26 结语 待翻译章节目录 序- 第一段 这是有史以来，第一本有关于如何练习钢琴的书！倘若你尚未学习一些有效率的钢琴练习方法，那本书中所揭橥的，是一些可以增进学钢琴速度的方法，将使你的学琴速度增进千倍！令人惊讶的是，虽然这些方法在钢琴的早期年代就已经知道，但是却很少被传授，原因是只有极少数的人知道这些方法，而这些人也未曾将它写下！(或是说让其它人知道这些方法)。 在1960 年代时期，并没有关于练习钢琴的好书。怀赛德(Whiteside)的书是我找到最好的书，不过却也令人失望。(请参阅参考书目一节)。当我在康乃尔大学当研究生的时期，虽然常常苦读至半夜，以期与来自世界各的的顶尖学生并驾齐驱，我还是尽量挤出一些时间练琴。虽然在年轻时期，我学了7 年的琴，这些我所知的练习方法还是不管用，以此我一直在寻找一些更好方法。这些钢琴演奏家，如何能弹到这种程度，对我而言，一直是一个谜。就如大多数人的想法，这只是努力，时间与天赋的差异吧！假如真是如此，那我一定是无望成为一个钢琴家，因为我也曾投下相当的时间与努力，至少在我年轻时，在周末一天练习8 小时。 在1970 年代时，这些答案慢慢的浮现，当我知道我两个女儿的钢琴老师教导她们一些极为有效率的练习方法，而这些方法与大多数钢琴老师所教的截然不同。在超过10 年对于这些有效率的方法不停的探索，我了解到，学琴过程中最重要的是练习方法。努力、时间与天赋反倒是其次！事实上，天赋，是难以

富网店,淘宝详情页及主图制作

加入富网店俱乐部https://www.wendangku.net/doc/978118933.html, 摘要：主图决定点击率，详情页决定转化率，主图是详情页的精华所在，是整个详情页的缩影。一般来说，先有详情页，后有主图。 在讨论今天的问题之前，我们先想想，淘宝能提供的资源其实就是展现。每一个网页的位置是有限的，例如搜索页的第一页就只能展示48个宝贝，类目页的第一页是95个宝贝，我们凭什么淘宝要把有限的免费展位资源给自己呢？ 其实这点，和我们交朋友拜把子一个道理，当双方都能够给对方正反馈的时候，感情会一直维系下去。当一方长期接收对方的付出，但是却不反馈的时候，这段感情就岌岌可危了。所以，你希望淘宝照顾你，你自己也得照顾淘宝。 一、为什么要做好主图和详情页？ 要怎么照顾淘宝？那就要了解淘宝的KPI（关键绩效指标）——销售额。我们先从一个订单的产生过程入手，看看如何帮助淘宝，提高它的KPI。 第一步：产生需求。比如天冷了，需要一件长袖的衣服。此刻脑海里面浮现出一件长袖衬衫。 第二步：选择淘宝平台，敲入关键词：长袖衬衫男。 第三步：看图片。

第四步：看哪个宝贝顺眼，点击宝贝。 这一页的搜素结果只有48个宝贝，但是我优先选择了其中一个点击进去，其他的47个宝贝就不会有流量了。这里就涉及到一个核心指标“点击率”，展现量*点击率=点击量（也就是流量）。 注意，淘宝把你放在搜索的第一页上面，不一定有流量，还得有买家点击了，你才有流量。试想，如果淘宝把你放在搜索首页，半天都没人点你的宝贝，猜猜下一秒会发生什么？淘宝会很生气，后果很严重！结果就是连展现都不给你了！ 第五步：从上往下扫描详情页。 第六步：看评价，到了这一步如果没有什么意外就买单了。 第七步：掏钱买单 当然，如果详情页看了让我一点欲望都没有，我连评价都不看了，直接关掉页面走人。所以这里又有一个核心指标“转化率”，访客数*转化率*客单价=销售额，客单价=支付宝成交金额/成交用户数，也就是人均在你店里消费多少RMB。 综上，我们会发现，核心KPI主要是点击率（主图）和转化率（详情页）。一般来说，都有这么一个逻辑，先有详情页，后有主图，因为主图就是详情页的精华所在，是整个详情页的缩影。所以这里，我们先讲详情页。 二、详情页设计必要的几个动作

2019年中职商品经营专业改革建议

2018年商品经营专业改革建议 经济与管理教研组起草 1 现状分析 鉴于2017年8月商品经营专业招生只有16人报读，以至于该专业被暂停招生。商品经营专业作为经济管理教研组的主要专业，具有招生的吸引力，能够吸引多一点女生前来报读我校。目前商品经济已经进入互联网+时代，互联网、物联网、大数据已经占领了商品经营的半壁江山，即网商、微商、电子商务、B2C、电子支付结算的兴起，传统实体零售业态如超市、零售店、百货商场已经逐步被网店、微店取代，信息化、自动化、现代化是当今商品经营发展主要趋势。因此，商品经营专业课程设置必须与时俱进，商品经营专业的课程设计目标是围绕培养实体店铺的超市、百货商店的销售人员，在大格局观的指导下，商品经营的课程设置必须改革，必须紧跟时代步伐，否则我们这个专业必将遭到淘汰。只有改革，才能生存，才能图发展，为了商品经营专业在2018年8月秋季新学期的持续发展，吸取2017年的经验教训，建议从新经济格局出发，全面改革商品经营的课程设计,课程设计方向结合我校校情和资源配置，与新兴市场业态接轨，重新拟定本专业课程开设方向。 2 课程设计改革 2.1改革方向 在商品经营这个大专业下，设计两个方向分支，第一，商品经营（“互联网+”营销方向）。第二，商品经营（房地产营销方向）。之所以这样设计，是因为目前这两个方向都是市场经济最热门的方向，吸引大量的人才进入，前景很好，有利于就业。互联网+的网络虚拟经济也是国家鼓励发展的创新创业型商业业态，吸引了很多人才在电子商务领域从业。此外，房地产一手房、二手房的销售、租赁、按揭也正处于上升期。 2.2 专业改革策划思路 在招生宣传时，重新修改商品经营专业的课程授课方向及专业优秀，让学生、家长都知道，商品经营这个传统专业已经与时俱进，灌入了与当今互联网、物联