In order to rationally evaluate the high speed railway operation safety level, the environmental safety evaluation index system of high speed railway should be well established by means of analyzing the impact mechanism of severe weather such as raining, thundering, lightning, earthquake, winding, and snowing. In addition to that, the attribute recognition will be identified to determine the similarity between samples and their corresponding attribute classes on the multidimensional space, which is on the basis of the Mahalanobis distance measurement function in terms of Mahalanobis distance with the characteristics of noncorrelation and nondimensionless influence. On top of the assumption, the high speed railway of China environment safety situation will be well elaborated by the suggested methods. The results from the detailed analysis show that the evaluation is basically matched up with the actual situation and could lay a scientific foundation for the high speed railway operation safety.
According to the high speed railway safety operation research carried out in the laboratory of Nanjing University of Science and Technology, the high speed railway operation failure directly caused by bad environments accounts for 29% from July 2011 to December 2012, and comparatively the speed railway accidents in severe weather take up 81.4% of the total ones at the same time. The above statistics thus give us a better understanding of the fact that the bad weather has significant effects on the high speed railway safety operation.
In China, the current researches of environment impact on high speed railway can be mainly divided into the following two categories: first, the macrodisaster emergency prediction and warning system design and second, the microenvironmental factors impact mechanism analysis. As to the first one, Sun et al., Wang et al., and Tao et al. have outlined some key problems of high speed railway environment safety, such as alarm threshold, the layout of monitoring points, train controlling mode, and the basic component of high speed railway warning system [
The comparison of the studies from abroad and home reveals that the researches of the high speed railway environment safety have been repeatedly carried out in an extremely earlier time and have been carefully studied by a lot of foreign researchers. Many countries have built up their own efficient high speed railway disaster warning system such as the Hokkaido and Shinkansen disaster warning system in Japan, which leads many other countries to conduct the earthquake prediction. For instance, France is now in possession of its Mediterranean earthquake monitoring system and Germany owns high speed railway disaster prevention system. Though the disaster monitoring systems of JingJingtang, Fuxia, and Wuguang have been already built in China, Zhang and Zeng contend that all the systems can be still well improved on the basis of the original ordinary railway disaster warning system [
Through the comparison of present researches between domestic and foreign, we can find that the domestic high speed railway disaster prevention is now in a transition from theory to practice, while foreign high speed railway disaster prevention system has been at a relatively perfect stage. Therefore, it is an urgent mission for the domestic researchers to make an intensive effort to the theory research of high speed railway disaster protection and system construction process so as to promote China high speed railway operating safety level.
The operational problems of the high speed railway are mainly caused by such uncertain factors as raining, thundering and lightning, horizontal wind, earthquake, and so forth, whose degree of intensity will directly decide the degree of danger posing to the high speed railway operation safety. The analysis of the characteristics of various environmental factors in the process of high speed railway operation in recent years and the conclusion of the mechanism of different environmental factors on high speed railway safe operation are presented in Table
High speed railway mechanism analysis of environmental impact factors.
Environmental factor | Mechanism |
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Rainfall | (i) Raining is the foremost factor that is easily causing line fault. Additionally, the current flow will emerge between the pantograph and overhead line systems of the high speed railway when it comes to a heavy rainy day and the train power supply will also be consequently influenced. |
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Cross wind | (i) The mechanism of the influence caused by horizontal wind on the high speed railway is that it can produce the yawing force which will allow the lateral migration. Moreover, the produced lift force will lead to the train derailment through the pneumatic action with the high speed train, which will undeniably increase the risk of train being derailed. |
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Lightning | (i) Lightning can disrupt the power supply of the high speed railway train traction which will result in the sudden stop of the high speed rail train through breaking down the high speed railway along the circuit devices. |
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Earthquake | (i) Earthquake wave can be divided into two kinds: the P wave (primary wave, pressure wave) and the S wave (secondary wave, shear wave). S wave can destroy the building structure and cause the landslides, orbital shift, and train wheel derailment which will influence the safe driving of high speed railway. |
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Temperature | (i) High temperature can lead to a big temperature difference between the internal and external, the increase of air conditioning power, and the aggravation of the train power supply load. Besides, high temperature can cause the short circuit because of the softened line. |
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Snowfall | (i) A lot of snow will cover the track and the ice on the track will increase the degree of danger of the train operation. |
Besides the six factors listed in Table
High speed railway environmental impact evaluation indexes system.
It is necessary to be mentioned that the usual climate environment will not exert any influence upon the operation of high speed railway, except typhoon, sandstorm, blizzard, and earthquakes, while high or low temperatures have significant influence on the operation of high speed railway. Therefore, with the exclusive of the average rainfall in Figure
Average annual rainfall level is
Maximum lightning density is
Disasters wind speed is
Average wind happening is
Average magnitude grade is
Average magnitude happening is
Average high and low temperature are
Average snow depth is
The representative research about the effects of horizontal wind on high speed railway train running is conducted preciously in Japan, which calculates the horizontal wind velocity under the condition of critical capsize under different running speed by wind tunnel experiment and takes the critical wind speed as the threshold of Shinkansen disaster warning (Table
Japanese Shinkansen winds threshold.
Wind scale | Wind speed (m/s) | The impact with no wind-break wall | The impact with wind-break wall |
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8.0-9.0 | 20–25 | The train speed under 160 km/h | No speed limit |
9.0–10.4 | 25–30 | The train speed under 70 km/h | The train speed under 160 km/h |
10.4–12.5 | 30–35 | Off-stream | The train speed under 70 km/h |
Above 12.5 | Above 35 | Off-stream | Off-stream |
In terms of the research results at home and abroad, the calculation of earthquake alarm threshold (
Researches show that when case case
Therefore, we define
Earthquake magnitude threshold of high speed railway (
Rank | Very serious | Serious | General | Slight | No effect |
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Train lateral acceleration ( |
240 Gal | 180 Gal | 120 Gal | 60 Gal | 0 Gal |
Earthquake magnitude (EAT) | >5.2 | 4.8 | 4.4 | 3.9 | <3.9 |
Domestic railway department limits the train running speed based on the size of the rain.
If the rain runs moderately which lasts 12 (or 24) hours and the rainfall capacity arrives at 10.0 mm–22.9 mm (17 mm–37.9 mm), its speed should be reduced.
If the rain runs in a heavy rainy day which lasts 12 (or 24) hours, and the rainfall capacity reaches 23.0 mm–49.9 mm (33.0 mm–74.9 mm), the railway lines are supposed to be blocked and the train operation is supposed to be prohibited.
For the sake of dimensional consistency, we can turn the hour rainfall volume into annual rainfall volume by the following method: it is universal knowledge that our country’s rain season will experience a period of 3 months that can be calculated by 12 rainfall times; thus, we categorize the annual rainfall volume into 900 mm, 1980 mm, and 2970 mm, respectively, as the moderate rainfall city, heavy rainfall city, and the storm rainfall city. Accordingly, we can calculate rainfall threshold effects on high speed railway compared with the provisions of the railway departments in Table
The annual rainfall threshold of high speed railway.
Rank | Very serious | Serious | General | Slight | No effect |
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Annual rainfall | >2970 mm | 1980 mm | 900 mm | 600 mm | <600 mm |
The current theoretical researches both at home and abroad pay less attention to the lightning, snowing, temperature, and snowfall which will definitely bring some influences on the characteristics of the high speed railway operations. Because it is difficult to set up a uniform standard to measure the factors, experts suggest that the reference value and the method of combining qualitative analysis can be employed to determine what degree of lightning, snow, and temperature influencing the high speed rail threshold. The environment impact assessment index of high speed railway can be discriminated as in Table
High speed railway environment impact assessment index discrimination safety threshold.
Environment | Evaluation Index | Particularly serious | Serious | Medium | Slight | No effect |
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Cross wind |
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>30 | 25–30 | 15–25 | 5–15 | 0–5 |
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>3 | 2-3 | 1-2 | 0.5–1.0 | 0–0.5 | |
Snowfall |
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>30 | 22–30 | 17–22 | 9–17 | 0–9 |
Earthquake |
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>5.2 | 4.8–5.2 | 4.4–4.8 | 3.9–4.4 | 0–3.9 |
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>1.0 | 0.6–1.0 | 0.3–0.6 | 0.1–0.3 | 0-0.1 | |
Lightning |
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>55 | 45–55 | 30–45 | 15–30 | 0–15 |
Rainfall |
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>2970 | 1980–2970 | 900–1980 | 600–900 | 0–600 |
Temperature |
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>64 | 48–64 | 35–48 | 25–35 | 10–25 |
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<−20 | −10–−20 | −10–0 | 0–5 | 5–10 |
Attribute recognition model is in essence the problems of multidimensional space between sample and attribution, which is proposed by professor Cheng and has been widely used in evaluation and classification. The sample space
The value of the sample properties has attributes characterized by a sample
However, there is no certain way to evaluate the relative importance of objective indicators in a fairly way. The essence of attribute recognition is to determine the attributes space similarity and methods used to calculate the spatial distance are Euclidean distance, Ming distance, and Mahalanobis distance. Todeschini et al. and Kayaalp and Arslan assert that the Mahalanobis distance has the advantages of weakening the correlation between impact indicators and automatic weight in the index calculation based on data changes [
Therefore, in order to compensate for normal function, we use Mahalanobis distance as the measurement function to build the attribute recognition model.
Assuming the sample
Generally, the greater the similarity of Mahalanobis distance, the smaller the measurement value. Therefore, assuming that Mahalanobis distance between area
Class attribute identification is in accordance with the confidence value
Assuming each evaluation category
Five domestic environmental factors such as rainfall, lightning, wind, temperature, and earthquake in recent years are collected from 2002 to 2012 as the basic assessments data [
Chinese regional environment situation in recent years from 2002 to 2012.
Region | Index | ||||||||
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Beijing | 0 | 0 | 22.5 | 0 | 0 | 13.61 | 498.96 | 26.86 | −2.75 |
Tianjin | 41.67 | 0.11 | 19.7 | 0 | 0 | 8.6 | 504.6 | 26.84 | −3.71 |
Hebei | 47.92 | 0.22 | 20 | 7.8 | 0.03 | 29.97 | 544.97 | 27.48 | −1.77 |
Shanxi | 0 | 0 | 21 | 0 | 0 | 27.41 | 443.46 | 24.48 | −5.11 |
Inner Mongolia | 0 | 0 | 17 | 0 | 0 | 0.6 | 373.29 | 23.56 | −10.87 |
Liaoning | 41.67 | 0.11 | 25 | 7.3 | 0.02 | 8.75 | 701.63 | 24.3 | −12.01 |
Jilin | 26.39 | 0.11 | 27 | 0 | 0 | 9.25 | 598.41 | 23.38 | −14.6 |
Heilongjiang | 0 | 0 | 34 | 0 | 0 | 15.44 | 498.9 | 23.24 | −16.9 |
Shanghai | 32.99 | 0.89 | 8 | 0 | 0 | 17.176 | 1092.41 | 29.39 | 4.75 |
Jiangsu | 35.14 | 1.11 | 22 | 0 | 0 | 40.25 | 1164.72 | 28.81 | 2.95 |
Zhejiang | 39.22 | 2.78 | 6 | 0 | 0 | 76 | 1276.82 | 29.95 | 4.84 |
Anhui | 36.87 | 1.22 | 15 | 0 | 0 | 35.75 | 1057.21 | 28.74 | 2.76 |
Fujian | 39.56 | 3.22 | 4 | 0 | 0 | 35.3 | 1355.53 | 29.81 | 11.3 |
Jiangxi | 38.8 | 1.78 | 12 | 0 | 0 | 35 | 1500.14 | 30.13 | 5.53 |
Shandong | 33.8 | 0.33 | 17 | 0 | 0 | 32.5 | 820.57 | 26.98 | −1.21 |
Henan | 42.13 | 0.33 | 19 | 0 | 0 | 30.67 | 724.81 | 27.29 | 1.4 |
Hubei | 40.67 | 0.78 | 17 | 0 | 0 | 26.72 | 1210.48 | 29.83 | 4.38 |
Hunan | 35.52 | 0.78 | 16.4 | 0 | 0 | 29 | 1276.44 | 29.96 | 8.39 |
Guangdong | 33.93 | 4.11 | 0 | 0 | 0 | 48.25 | 1805.49 | 29.78 | 13.16 |
Guangxi | 34.92 | 2.33 | 4 | 0 | 0 | 26.25 | 1189.73 | 28.3 | 14.49 |
Hainan | 31.74 | 2.22 | 0 | 0 | 0 | 38.75 | 1780.62 | 29.06 | 14.07 |
Chongqing | 0 | 0 | 3.7 | 0 | 0 | 23.58 | 1065.61 | 29.53 | 6.89 |
Sichuan | 0 | 0 | 4.2 | 7.44 | 0.1 | 57.256 | 843.16 | 25.96 | 5.98 |
Guizhou | 43.06 | 0.11 | 4.5 | 0 | 0 | 31.75 | 989.78 | 23.19 | 3.52 |
Yunnan | 35.19 | 0.33 | 0 | 7.33 | 0.1 | 27.21 | 878.28 | 21 | 9.62 |
Tibet | 0 | 0 | 52 | 0 | 0 | 0.29 | 453.12 | 17.31 | 0.62 |
Shanxi | 15 | 2 | 19 | 0 | 0 | 15.46 | 611.11 | 27.61 | 0.36 |
Gansu | 12.4 | 2.22 | 18 | 6.6 | 0.02 | 0.36 | 271.74 | 22.46 | −5.06 |
Qinghai | 32 | 3.56 | 15 | 6.9 | 0.02 | 0.42 | 442.9 | 17.48 | −7.75 |
Ningxia | 4.72 | 1.89 | 17.9 | 0 | 0 | 4.77 | 175.34 | 24.31 | −7.38 |
Xinjiang | 46 | 4.67 | 46 | 7.1 | 0.05 | 0.25 | 309.61 | 24.19 | −12.9 |
The program of MATLAB is employed to work out the estimation. The specific method is made by 31 districts samples and each has 9 indexes. Then we constitute the sample matrix
Use the function
Then make confidence level
Chinese regional environment impacts attribute recognition value of high speed railway.
Region | Value | |||||
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Particularly serious | Serious | Medium | Slight | No effect | Classification | |
Xinjiang | 0.301 | 0.402 | 0.117 | 0.003 | 0.177 | Serious |
Sichuan | 0.376 | 0.246 | 0.196 | 0.120 | 0.062 | |
Jilin | 0.303 | 0.363 | 0.146 | 0.082 | 0.106 | |
Heilongjiang | 0.269 | 0.342 | 0.215 | 0.140 | 0.034 | |
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Hebei | 0.169 | 0.196 | 0.237 | 0.231 | 0.168 | Medium |
Liaoning | 0.201 | 0.203 | 0.218 | 0.198 | 0.180 | |
Jiangsu | 0.177 | 0.209 | 0.228 | 0.211 | 0.174 | |
Zhejiang | 0.180 | 0.202 | 0.225 | 0.205 | 0.188 | |
Anhui | 0.166 | 0.202 | 0.234 | 0.221 | 0.177 | |
Jiangxi | 0.196 | 0.222 | 0.206 | 0.199 | 0.178 | |
Hubei | 0.179 | 0.210 | 0.221 | 0.216 | 0.175 | |
Hunan | 0.175 | 0.205 | 0.221 | 0.222 | 0.176 | |
Guangdong | 0.195 | 0.200 | 0.212 | 0.198 | 0.196 | |
Fujian | 0.194 | 0.211 | 0.195 | 0.200 | 0.200 | |
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Beijing | 0.163 | 0.193 | 0.226 | 0.239 | 0.179 | Slight |
Tianjin | 0.158 | 0.188 | 0.232 | 0.234 | 0.187 | |
Shanxi | 0.160 | 0.190 | 0.227 | 0.228 | 0.195 | |
Inner Mongolia | 0.178 | 0.200 | 0.202 | 0.212 | 0.208 | |
Shanghai | 0.173 | 0.203 | 0.215 | 0.224 | 0.185 | |
Hainan | 0.168 | 0.198 | 0.222 | 0.224 | 0.189 | |
Shandong | 0.162 | 0.196 | 0.234 | 0.222 | 0.186 | |
Henan | 0.150 | 0.184 | 0.247 | 0.237 | 0.182 | |
Guangxi | 0.151 | 0.181 | 0.222 | 0.248 | 0.199 | |
Chongqing | 0.180 | 0.201 | 0.208 | 0.223 | 0.187 | |
Guizhou | 0.162 | 0.187 | 0.202 | 0.206 | 0.244 | |
Yunnan | 0.153 | 0.177 | 0.201 | 0.229 | 0.239 | |
Tibet | 0.178 | 0.192 | 0.202 | 0.213 | 0.215 | |
Shaanxi | 0.155 | 0.187 | 0.235 | 0.245 | 0.178 | |
Gansu | 0.165 | 0.191 | 0.215 | 0.237 | 0.192 | |
Qinghai | 0.177 | 0.194 | 0.194 | 0.203 | 0.231 | |
Ningxia | 0.155 | 0.183 | 0.224 | 0.237 | 0.200 |
The calculation results in the above table show that the environmental safety situation of Xinjiang, Sichuan, Heilongjiang, and Jilin belongs to serious category, which takes up 12.9%. The situation in the Medium level areas accounts for 32.2%, such as Heilongjiang, Hebei, Liaoning, Jiangsu, and Guangdong, and that of the 17 areas such as Beijing, Tianjin, Guizhou, Gansu, and other regions belongs to slight level, which accounts for 54.9%. It is notable that, in addition to Sichuan, the high speed railway environment impacts in the serious level areas are mostly distributed in coastal areas and northern regions, while Chinese abdominal regions are mostly in the medium and light level (see Figure
Chinese environment impacts of high speed railway distribution.
For further analysis, security score in the areas of the serious level should be calculated by means of grading criterion. All kinds of scores are clarified in Table
The attribute recognition of high speed railway classification score.
Classification | No effect | Slight | Medium | Serious | Particularly serious |
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Score | 90 | 80 | 70 | 60 | 50 |
The calculation results show that Sichuan has the lowest scores of 62.460, followed by 63.280 in Heilongjiang and 63.489 in Xinxiang, and Yunnan has the highest score of 72.23.
There are 25 high speed railway operational lines in our country currently, which constitute the total mileage of 10192 kilometers. Most of the high speed railways are located in southeast of China, where complex geological accidents such as landslip, earthquake, and other geological disasters take place frequently. The high speed railway environment safety situation is clearly illustrated in Table
The environment impacts of high speed railway lines distribution.
Lines | Serious environmental impact | Middle environment impact | Light environmental impact |
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Jinhu | — | Nanjing, Jinan | Beijing, Tianjin, and Shanghai |
Hebang | — | Hefei, Bengbu | — |
Jinshi | — | Shijiazhuang | Beijing |
Wuguang | — | Wuhan, Changsha | Guangzhou, Foshan |
Guangshengang | — | — | Guangzhou, Shenzhen |
Yongtaiwen | — | — | Ningbo, Taizhou, and Wenzhou |
Wenfu | — | Wenzhou, Fuzhou | — |
Fuxia | — | Fuzhou, Xiamen, and Quanzhou | — |
Zhenxi | — | Zhengzhou, Xi ’an | |
Xibao | — | — | Xi ’an, Baoji |
Huhang | — | Jiaxing, Hangzhou | Shanghai |
Hewu | — | Nanjing, Hefei, and Wuhan | — |
Hanyi | — | Hankou, Zhijiang, and Yinchuan | — |
Hening | — | Hefei, Nanjing | — |
Jiaoji | — | — | Jinan, Qingdao |
Shitai | — | — | Shijiazhuang, Taiyuan |
Huning | — | Nanjing, Suzhou | Shanghai |
Yiwan | Wangzhou, Ensi | — | Yichang |
Yuli | — | Chongqing, Fuling | Liangwu |
Suiyu | Suining, Shizuishan | — | — |
Dacheng | Dazhou, Suining, and Chengdu | — | — |
The Jinghu line, Fuxia line, and Huning line mainly go across regions of Beijing, Tianjin, Jinan, Nanjing, Shanghai, Hangzhou, and so on. Most of these regions are located in the medium impacted or light impacted areas where raining and storm happen frequently. Thus, we have to pay attention to the influence of heavy rain and storm.
The Wuguang line and Guangshengang line mainly go cross such cities as Guangzhou, Foshan, and others in Guangzhou. These cities are vulnerable to the typhoon from coastal regions, which will affect the progress of the high speed railway.
The Yiwan line, Suiyu line, and Dacheng line go cross Wanzhou, Suining, Shizishan, Chengdu, or other cities of Sichuan province. High speed railway in these areas will suffer seriously from the tough environment, and we should pay attention to prevent cost and loss from landslip and earthquake.
Firstly, the paper makes a detailed analysis of the impact from such environment factors as rainfall, earthquake, lightning, wind, and snow on the high speed railway safety mechanism. On the basis of the analysis, the evaluation index system of safety has been established and the threshold of high speed railway environmental safety has been calibrated by citing the results of domestic and abroad. At last, the high speed railway uncertain safety attribute recognition model is created based on the Mahalanobis distance with the features of dimensionless and weak effect correlation, which simplifies the comprehensive calculation process.
Secondly, the examples of China’s 31 provinces and regions in the paper are selected to make the data of the high speed railway environmental safety much more convincing. The degree of danger is divided into five categories, among which the cities that the high speed railways pass in the serious category account for 16.1%, those in the middle class account for 38.7%, those in the mild category account for 38.7%, and those in the no effect category account for 6.51%. It deserves our attention that cities of Xinjiang, Sichuan, Guangdong, Heilongjiang, and Liaoning belong to the serious category, whose evaluation results are basically consistent with the environmental characteristics. And the results have a certain theoretical reference for the “135” planning of high speed railway operation safety in Xinjiang and other areas.
At last, the analysis of the high speed railway environmental safety is directed to the aspect of weather, geology, and other factors. However, considering the complexity of data acquisition, the high speed railway evaluation index has its own drawbacks in this paper. It is needed to introduce more methods and factors into the evaluation of the high speed railway safety operation to facilitate the further researches.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors are very grateful to the anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. The work also was supported by National Nature Science Funding of China (Project no. 51178157), The Basic Scientific Research Business Special Fund Project in Colleges and Universities (no. 2011zdjh29), National Statistical Scientific Research Projects (no. 2012LY150), “Blue Project” Projects in Jiangsu Province Colleges and Universities (no. 201211), and Youth Fund Projects in Jiangxi Province Department of Education (no. GJJ13314).