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Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering

Data publikacji: 21 Oct 2022
Tom & Zeszyt: AHEAD OF PRINT
Zakres stron: -
Otrzymano: 08 Apr 2022
Przyjęty: 27 Apr 2022
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2444-8656
Pierwsze wydanie
01 Jan 2016
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
Introduction

In recent years, the advantage of cheap labour in China has gradually disappeared, and the economic development mode relying on resource consumption and low labour cost is facing numerous challenges. According to the law of economic market development, education progress is the most effective way to promote economic development, and it is also the only way to improve production efficiency [1, 2]. Only by playing the role of education, can we turn the progress of education into the driving force of development, thus playing an effective role in promoting development in the socialist market economy with Chinese characteristics. Among them, the implementation of ideological and political education (IPE) is often ignored; as the main battlefield of production, operation and management, higher vocational colleges have a far-reaching impact on the promotion of industrial upgrading and economic transformation [3]. As China’s economic development has entered the stage from high-speed growth to high-quality development, higher requirements are needed for applied talents; therefore, it is an important move to train technical talents with high moral and technical skills through ideological and political teaching, so as to enhance the driving force of China’s economic development [4, 5].

Nowadays, in higher education system, the homogenisation of education is more serious, which directly leads to the difficulty in meeting the needs of social talents for the output of university education, thus making the development of regional economy lag behind [6]. In addition, in the Western region, the level of ideological and political identity among college students in minority areas is low, which has seriously restricted the regional talent training and economic development [7, 8]. In the future, IPE will be conducive to promote economic growth and become a vital force to improve social production.

In order to develop higher education, we must rely on continuous investment in education funds. Only sustained and numerous investment can support the material demand and strategic change of IPE, which is related to the future development of the whole country, so it must be the focus of financial security [9, 10]. Therefore, it is of great significance to advance the level of regional IPE and stimulate the development of regional economy.

Internal relationship between economic growth and IPE
Development characteristics of IPE

Under the background of multiculturalism, the innovation of IPE in colleges and universities has adhered to the basic principles of flat development and diversified practice, which fully absorbed advanced educational ideas in multi-cultural, so as to enhance the advancement and foresight of IPE and ensure that the IPE work in colleges can play a positive role in education and guidance, which should be carried out with high efficiency. Through the summary and analysis, IPE in colleges in China has the characteristics of a development stage, as shown in Figure 1.

Fig. 1

Characteristics of IPE development stage. IPE, ideological and political education.

As can be seen from Figure 1, at present, the popularisation of IPE in colleges has gradually increased. Under the current economic background, by combining with the national development strategy and based on the needs of economic development, an in-depth study of economic thought in the new era has been constantly integrated into the IPE and its internal management, decision-making mechanism, and teaching form and so has undergone multiple changes.

Regional economic input

Improvement of the education level is closely related to the development of economy [11]. As shown in Figure 2, there are many different funding channels for higher education in China, including special expenditure of the state finance, donations from social organisations and organisations, funds from many private schools, business income and other sources.

Fig. 2

Sources of education funds.

In addition, there are also some sources of income, which are from the investment of schools, and the funds paid by the units subordinate to the schools. To sum up, we can divide the five funds mentioned in Figure 2 into two types according to whether they are allocated by the government finance: financial education funds and non-financial education funds. As the total amount of education funds is concerned, due to the higher degree of attention and the better and better level of economic development, the funds invested in higher education are constantly increasing [12].

Internal relations

The development of IPE can provide numerous high-level talents for the society and improve intellectual support for social development. Therefore, the long-term and sustained development of higher education is the top priority of future work, which needs continuous investment to ensure the advance of higher education, thereby providing human capital for the society. The internal relationship between IPE investment and economic growth is shown in Figure 3.

Fig. 3

Internal relationship between investment in IPE and economic growth. IPE, ideological and political education.

In the incremental investment analysis method, education investment can be regarded as human capital, which can promote economic growth. In the calculation of specific contribution rate (CR), economic growth should be regarded as the output rate and education investment as the capital item [13,14]. The analysis process of this method firstly finds out the growth surplus according to the production function, uses the counterfactual measurement method to obtain the increment of education investment, finally calculates the contribution of education to the growth of national income through the rate of return on education investment. Many scholars regard capital as the only factor to promote economic development and apply it to the construction of production function, but they could not obtain the obvious relationship between IPE and economic growth. Therefore, this study adopts the improved fuzzy clustering algorithm to establish the calculation model of the CR of China’s IPE.

Improvement of fuzzy clustering algorithm
Fuzzy cluster analysis

Cluster analysis is an important data analysis method used to gather a certain class of the same things together to study and then analyse their overall characteristics and differences [15,16]. Fuzzy clustering analysis introduces fuzzy functions into clustering analysis, and through the similarity between data samples, the data sets are divided into several common data sets, and the membership degree of data samples to each class is given. The calculation principle is as follows:

Assume data set X = {X1, X2, … , Xn} ⊂ Rs, where Rs represents S-d real space. For ∀j, 1 ≤ jn, Xj = (xj1, xj2, … , xjs) ∈ Rs is obtained, where xjk(k = 1, 2, … , s) is the first k-th attribute values of sample xj(j = 1, 2, … , N).

Fuzzy clustering of sample data sets is to divide the set X into c subsets of data with common attributes, such as class c, and each class is represented by a fuzzy set Fi(i = 1, 2, … , c). Assume U(X) = (uij)c×nRc×n, and the membership degree of sample data Xj to set Fi is denoted as μij ∈ [0,1], which satisfy i=1cμij=1 \sum _{i=1}^{c}{\mu}_{ij} =1 , j = 1, 2, … , n; 0<i=1nμij<n 0<\sum _{i=1}^{n}{\mu}_{ij}<n , i = 1, 2 … , c; and j=1ci=1nμij=n \sum _{j=1}^{c}\sum _{i=1}^{n}{\mu}_{ij}=n , i = 1, 2, … , c; j = 1, 2, … , n

Make V = (V1, V2, … ,Vc) is the class centre for F = (F1, F2, … ,Fc), then the clustering objective function is mJm(U,V,X)=i=1cj=inμijmdij2=i=1cj=inμijm||XjVi||2,1m {\rm m}\, \, J_{m} (U,\, \, V,\, \, X)=\sum _{i=1}^{c}\sum _{j=i}^{n}{\mu}_{ij}^{m} d_{ij}^{2} =\sum _{i=1}^{c}\sum _{j=i}^{n}{\mu}_{ij}^{m} {||X_{j} -V_{i} ||}^{2},\, \, 1\le m\le \infty where m indicates ambiguity, and Vi=j=1N(μij)mXjj=1N(μij)m,i=1,2,,c V_{i}=\frac{\sum _{j=1}^{N}{({\mu}_{ij})}^{m} X_{j}}{\sum _{j=1}^{N}{({\mu}_{ij})}^{m}},\, \, i=1,\, \, 2,\, \, \cdots,\, \, c μij=(k=1c(dijdkj)2m1)1 {\mu}_{ij}={\left(\sum _{k=1}^{c}\left(\frac{d_{ij}}{d_{kj}} \right) ^{\frac{2}{m-1}} \right)}^{-1}

Improved fuzzy clustering analysis algorithm

At present, the existing fuzzy clustering algorithms are improved on the basis of existing clustering algorithms, such as fuzzy C-means clustering algorithm and fuzzy spectrum clustering algorithm, but these algorithms have the following shortcomings:

The number of clusters must be given, which reduces the credibility of the classification results.

The classification algorithm has high requirements on the objective function; is sensitive to the selection of initial value, which is easy to fall into local optimum; and is difficult to achieve the global optimum.

Therefore, in this study, the gravity search method is used to optimise fuzzy clustering, and the genetic algorithm is used to determine the classification data sets in the eastern, western and central regions of China. The calculation principle of the gravity search method is as follows:

Central force optimisation is an evolutionary algorithm based on Newton’s law of universal gravitation. The core formula of the algorithm is as follows:

In ND dimension search space S. In, put in NP individual, that is, xp(p = 1, 2, … , pk, … , NP). The calculation formula of speed and acceleration in pk-th individual is as follows: Rpk,t=Rpk,t1+Vpk,t1Δt+0.5Apk,t1(Δt)2 R_{pk,t}=R_{pk,t-1} +V_{pk,t-1} \Delta t+0.5A_{pk,t-1} {(\Delta t)}^{2} Apk,t=Gp=1ppkNpU(Mp,t1Mpk,t1)(Mp,t1Mpk,t1)α(Rp,t1Rpk,t1)Rp,t1Rpk,t1β A_{pk,t}=G\sum _{\begin{subarray}{c}{p=1}\\ {p\ne pk}\end{subarray}}^{N_p}U(M_{p,t-1} -M_{pk,t-1}){(M_{p,t-1} -M_{pk,t-1})}^{{\alpha}} (R_{p,t-1} -R_{pk,t-1}){\|R_{p,t-1} -R_{pk,t-1}\|}^{-{\beta}} Vpk,t=(Rpk,tRpk,t1)(Δt)1 V_{pk,t}=(R_{pk,t} -R_{pk,t-1}){(\Delta t)}^{-1} where Rpk,t, Apk,t and Vpk,t represent the position, acceleration and velocity of the pk-th individual in the t generation, respectively. Mpk,t = f (xpk,t) represents the fitness of the pk-th individual, that is, the value of the objective function.

The introduction of the criterion can better determine the optimal classification number c, which is based on the compactness, that is, the distance between the same kind of sample data, and the degree of classification, that is, the distance between different classes. These two metrics construct a variety of criterion functions. But from a macro perspective, they still have some shortcomings [17, 18]:

The calculation of tightness is too arbitrary. The distance between the sample data and its class centre is taken as the measure of compactness. In extreme cases, when the number of categories approaches the number of sample data, the distance between the sample data and its class centre decreases monotonously and tends to zero, thus making compactness lose its due utility.

The topological structure of the sample data set is not considered. When two classes have overlapping data or no overlapping data, their class spacing may be equal, and the existing separation formula is indistinguishable.

Therefore, in this study, the classification number is adaptively determined by using the genetic algorithm, and the criterion function is optimised by using the gravity search method. The specific steps of the algorithm are shown in Figure 4.

Fig. 4

Implementation steps of improved fuzzy cluster analysis algorithm.

Step 1. Initialise k: = 0 and randomly generate a file with a length of l binary initial population of Y0={Y1k,Y2k,,Ynk} \overrightarrow{Y}_{0} =\{Y_{1}^{k},\, \, Y_{2}^{k},\, \, \cdots,\, \, Y_{n}^{k} \} ;

Step 2. Convert Yik(i=1,2,,n) Y_{i}^{k} (i=1,\, \, 2,\, \, \cdots,\, \, n) to decimal cik(i=1,2,,n) c_{i}^{k} (i=1,\, \, 2,\, \, \cdots,\, \, n) ;

Step 3. Randomly generate a population X = (x1, x2 … , xm), where xl=(xl1,xl2,xlcik) x^{l} =(x^{l1},\, \, x^{l2},\, \, x^{lc_{i}^{k}}) , (l = 1, 2, … ,m) represents the l-th individual, whose component represents the cik c_{i}^{k} -th class centres of categories. Initialise the acceleration vector of each individual Al and velocity vector Vl;

Step 4. Calculate the membership degree of each individual according to formula (1) such that Uk(0)=(μijk(0))cik×n U^{k} (0)={({\mu}_{ij}^{k} (0))}_{c_{i}^{k} \times n} ;

Step 5. If maxij[μijk(t+1)μijk(t)]|<ε {\max}_{ij} \, \, [{\mu}_{ij}^{k} (t+1)-{\mu}_{ij}^{k} (t)]|\, \, <\, \, {\rm \varepsilon} , in which ε is a given positive real number, go to Step 8; otherwise, go to Step 6;

Step 6. Take the reciprocal of formula (1) as a fitness function and select individuals to enter the next-generation population, according to the principle of greed;

Step 7. Update the individual velocity and acceleration vector according to formula (5);

Step 8. Calculate the fitness function G(U*, V*, ci) = f (v), in which f (v) is a criterion function;

Step 9. Seek optimal solution of maxG(U*, V*, ci), that is, (U*, V*, ci* c_{i}^{*} ), and make ci* c_{i}^{*} as the best individual in the population.

Parameter setting

From the perspective of algorithm design, giving the upper and lower bounds of classification numbers is helpful to improve the efficiency of the algorithm. Obviously, the lower bound of the classification number is 1. Considering that China is divided into seven parts, geographically, it is appropriate to define the upper bound of the classification number as 7. Numerical experiments are completed based on the aforementioned example information by utilising the proposed algorithm, and the boundaries of the algorithm are set as follows [19]: the value range of C is [2, 7], with the precision 104; the population size is 20, the crossover probability is 70%, the mutation probability is 0.5%, and the evolution algebra is 100; in addition, α is set as 1, β as 2, and G is 10 et/Tmax, where Tmax is the largest evolutionary algebra with a value of 100.

Calculation model of CR of IPE
Standards of regional classification

According to the China Statistical Yearbook (2014–2020 edition), the annual GDP, fixed asset investment, household consumption level, and employment population data of each province are obtained, and the algorithm mentioned before is used for regional classification. The results are shown in Table 1.

Classification results of provinces.

Category Provinces
A Sichuan, Zhejiang, Shandong, Guangdong
B Shaanxi, Xinjiang, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Guangxi, Yunnan, Henan Hunan, Hebei
C Tibet, Gansu, Qinghai, Ningxia, Guizhou

As can be seen from Table 1, Tibet, Gansu, Qinghai, Ningxia and Guizhou are located in the border areas, with weak industrial foundation, low population base, large proportion of agricultural population, relatively backward level of higher education and IPE, which belong to the least-developed areas in China, and their overall economic level is relatively backward, which requires special attention.

Calculation of the CR of IPE progress

EAi is used to represent the CR of economic progress in the i region, and its calculation formula is as follows: EAi=aiyi×100% E_{Ai} =\frac{a_{i}}{y_{i}} \times 100\%

Similarly, the CR of the labour input, material capital input and human capital input in the i region are as follows: ELi=ailiyi×100% E_{Li}=\frac{a_{i} l_{i}}{y_{i}} \times 100\% EKi=βikiyi×100% E_{Ki}=\frac{{\beta}_{i} k_{i}}{y_{i}} \times 100\% EHi=γihiyi×100% E_{Hi}=\frac{{\gamma}_{i} h_{i}}{y_{i}} \times 100\% where i = 1, 2, … k.

EAj is used to represent the CR of IPE progress in the j-th region, which is equivalent to the CR of all regions that is multiplied by the membership degree of each region belonging to all categories. Its calculation formula is as follows: EAj=EA1μ1j+EA2μ2j++EAkμkj E_{Aj} =E_{A1} {\mu}_{1j} +E_{A2} {\mu}_{2j} +\cdots +E_{Ak} {\mu}_{kj}

Similarly, the CR of material capital investment in each region is given as follows: EKj=EK1μ1j+EK2μ2j++EKkμkj E_{Kj} =E_{K1} {\mu}_{1j} +E_{K2} {\mu}_{2j} +\cdots +E_{Kk} {\mu}_{kj}

The CR of labour capital investment in each region is as follows: EHj=EH1μ1j+EH2μ2j++EHkμkj E_{Hj} =E_{H1} {\mu}_{1j} +E_{H2} {\mu}_{2j} +\cdots +E_{Hk} {\mu}_{kj}

The calculation of the CR of IPE progress is represented as EA, where the CR of the progress of IPE to the economic growth rate is obtained by multiplying the CR of each region by the proportion of GDP in the whole country. Its calculation formula is as follows: EA=EA1w1+EA2w2++EAjwj E_{A} =E_{A1} w_{1} +E_{A2} w_{2} +\cdots +E_{Aj} w_{j}

Among them, wj is the proportion of the j-th region in the national GDP.

The calculation process of the CR of IPE is shown in Figure 5.

Data acquisition: According to the production factors in the calculation model, data are collected, and sample data of each production factor and the sample data classified according to the level of educational progress are obtained.

Elastic coefficient estimation: The elastic coefficient of material capital investment in different regions is estimated by using the regression method, where αi is the elasticity coefficient of labour input and βi the elasticity coefficient of human capital investment γi, in which i = 1, 2, … k.

Calculate the progress rate of IPE. Firstly, the annual average growth rates of output, material capital, labour force and human capital of various regions are calculated according to formulas (11)(14); Then, according to the Solow value equation in the combination model, the progress rate of IPE in each region ai is obtained.

Fig. 5

Calculation process of CR of IPE. CR, contribution rate; IPE, ideological and political education.

Modification based on combined model

It is found that education and economic development are inseparable. Therefore, education is regarded as an element of economic development [20]. By adding the input structure in the calculation model, the model is decomposed to measure the contribution of IPE investment to economic growth. Assuming that the labour input is L, which consists of two parts: one is the basic labour force and the other part is the input of IPE, and L is the product of the two. By the Cobb–Douglas production function, we can get formula (15): Y=AKαL0βEβ Y=A\, \, \cdot \, \, K^{{\alpha}} \, \, \cdot \, \, L_{0}^{{\beta}} \, \, \cdot \, \, E^{{\beta}}

Among them, Y is output, K is capital input, L0 is input of IPE personnel, E is special fund input of IPE, α is the output elasticity of capital, β is the output elasticity of labour force, and A is constant, representing the technical level of the base year.

Take logarithm of both sides of the aforementioned formula, Formula (16) can be obtained: lnY=lnA+αlnK+β(lnL+lnE) {\rm ln}\, \, Y={\rm ln}\, \, A+{\alpha}{\rm ln}\, \, K+{\beta}({\rm ln}\, \, L+\ln E)

Then take the full differential of both sides with respect to time T to obtain the growth rate equation, as shown in Equation (17) dYY=dAA+αdKK+β(dLL+dEE) \frac{dY}{Y} =\frac{dA}{A} +{\alpha}\frac{dK}{K} +{\beta}\left(\frac{dL}{L} +\frac{dE}{E} \right)

In order to more intuitively reflect the CR of IPE to the economy, this article uses the difference quotient formula to separate the factors that affect the growth rate, and then the difference quotient is used to discretise the growth rate equation as follows: y=α+αK+βL+βε y=\alpha+\alpha K+{\beta}L+{\beta}_{{\varepsilon}}

The model of the contribution of IPE education to economic growth rate is as follows: Cε=βεy C_{{\varepsilon}} =\frac{{\beta}_{{\varepsilon}}}{y}

Among them, Cε is the CR of IPE to economic growth, βε represents the economic growth rate brought by the effect of educational factors, ε represents the annual growth rate of IPE investment, and y represents the total growth rate of national economy.

Under actual circumstances, it is difficult to estimate βε, so the annual average growth rate of the comprehensive index of education Rε is used instead. Thus, the calculation formula of CR of IPE to economic growth is as follows: Cε=βRεy C_{{\varepsilon}} ={\beta}\frac{R_{{\varepsilon}}}{y}

Results and analysis

According to the 2014–2020 edition of the China Statistical Yearbook, the annual GDP, fixed assets investment, household consumption level and employment population data of each province, as well as the statistical data of teaching efficiency and IPE quality of colleges and universities in various regions, are used the this model. The influencing factors of the CR of higher education to economic growth selected in this study are the number of students enrolled, the number of teachers and students in school, the investment scale of IPE and the number of special projects of IPE.

Although some scholars have considered the turnover rate of graduates, after analysing the system dynamics simulation model, the results show that the relative error between simulation results and actual results is only 7.33%. In addition, the data statistics of turnover rate in China are not comprehensive, so the influence of other factors such as brain turnover rate is not considered in this study.

CR of IPE progress

Figure 6 reflects the CR of IPE progress in A, B and C areas, with average values of 8.46%, 5.38% and 1.99%, respectively. At present, the development of IPE in China is unbalanced among regions, with the highest level of education in class A, far exceeding that in classes B and C. Therefore, we should vigorously speed up the construction of IPE in classes B and C, implement better preferential policies and subsidy policies and guide social investment, so as to realise the common progress of higher education in all areas.

Fig. 6

CR of IPE progress in three types of areas. CR, contribution rate; IPE, ideological and political education.

Comparison in different regions

In order to more intuitively analyse the CR of IPE in different regions of China, the CR of IPE in eastern, western and central regions is compared, and the results are shown in Table 2.

Comparison of CRs of IPE in eastern, western and central regions.

Province Cε Province Cε
Central China Shaanxi 4.27 Eastern China Shanghai 8.34
Inner Mongolia 3.42 Zhejiang 6.45
Anhui 5.82 Tianjin 4.56
Hunan 8.23 Jiangsu 7.33
Hubei 6.33 Guangdong 7.09
Henan 5.20 Beijing 13.98
Jilin 5.73 Liaoning 5.80
Western China
Province Cε Province Cε Province Cε
Sichuan 6.31 Tibet 1.02 Qinghai 1.28
Yunnan 3.20 Shaanxi 1.24 Xinjiang 0.93
Guizhou 2.83 Gansu 2.49 Guangxi 3.01

CRs, contribution rates; IPE, ideological and political education.

The level of IPE in eastern China is far higher than that in other parts of China, especially in Beijing, Shanghai and Jiangsu. At the same time, it is easy to find that these areas with a high level of higher education are also economically developed areas. Although the absolute CR of other provinces is not high, considering the economic strength of these provinces, its educational output is much higher than that of the central and western regions. In addition, the CR of the central and western regions is far lower than that of the eastern developed regions, which also shows that the CR of IPE between the two regions is greatly different due to the huge gap in economic strength.

Also, it can be seen from Table 2 that Hunan and Sichuan, as depressions of higher education, have accumulated rich educational resources, so the CR of IPE can maintain a steady and rapid growth. However, Guizhou, as a remote and poor area, has very scarce higher education resources, so the CR of IPE is very small, but it can also maintain a small growth with the economic development which also shows that it has a huge development space, as long as the investment in education is increased, the CR of education will be greatly improved.

Conclusion

Vigorously promoting the construction of IPE in various regions is the key to cultivating high-quality talents, which is also an important measure to strengthen the internal driving force of China’s economic development. Therefore, this article introduces the investment of IPE into the function model of social production, where fuzzy clustering is optimised by using the gravity search method, and the classified data sets of various provinces in China are determined by using the genetic algorithm. Based on the improved fuzzy clustering algorithm, the calculation model of the CR of IPE in different regions is established. The results show that at present, the development of IPE in China is unbalanced among regions, and the average CRs of IPE in A, B and C regions are 8.46%, 5.38% and 1.99%, respectively. Due to the huge gap in economic strength, the CR of the central and western regions is far lower than that of the eastern developed regions, but some of them are rich in educational resources and have great development potential, whose investment on IPE should be increased in the future.

Fig. 1

Characteristics of IPE development stage. IPE, ideological and political education.
Characteristics of IPE development stage. IPE, ideological and political education.

Fig. 2

Sources of education funds.
Sources of education funds.

Fig. 3

Internal relationship between investment in IPE and economic growth. IPE, ideological and political education.
Internal relationship between investment in IPE and economic growth. IPE, ideological and political education.

Fig. 4

Implementation steps of improved fuzzy cluster analysis algorithm.
Implementation steps of improved fuzzy cluster analysis algorithm.

Fig. 5

Calculation process of CR of IPE. CR, contribution rate; IPE, ideological and political education.
Calculation process of CR of IPE. CR, contribution rate; IPE, ideological and political education.

Fig. 6

CR of IPE progress in three types of areas. CR, contribution rate; IPE, ideological and political education.
CR of IPE progress in three types of areas. CR, contribution rate; IPE, ideological and political education.

Comparison of CRs of IPE in eastern, western and central regions.

Province Cε Province Cε
Central China Shaanxi 4.27 Eastern China Shanghai 8.34
Inner Mongolia 3.42 Zhejiang 6.45
Anhui 5.82 Tianjin 4.56
Hunan 8.23 Jiangsu 7.33
Hubei 6.33 Guangdong 7.09
Henan 5.20 Beijing 13.98
Jilin 5.73 Liaoning 5.80
Western China
Province Cε Province Cε Province Cε
Sichuan 6.31 Tibet 1.02 Qinghai 1.28
Yunnan 3.20 Shaanxi 1.24 Xinjiang 0.93
Guizhou 2.83 Gansu 2.49 Guangxi 3.01

Classification results of provinces.

Category Provinces
A Sichuan, Zhejiang, Shandong, Guangdong
B Shaanxi, Xinjiang, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Guangxi, Yunnan, Henan Hunan, Hebei
C Tibet, Gansu, Qinghai, Ningxia, Guizhou

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