Research on the Effectiveness of Integrating Red Culture Nurturing Function in Civic and Political Education of Colleges and Universities Based on Pattern Recognition

With the deepening of China’s socialist core values, red culture, as an important carrier of values, plays an increasingly important role in ideological and political education in colleges and universities. As the crystallization of the wisdom and struggle of the Chinese forefathers, red culture not only carries a profound historical heritage, but also promotes the spiritual power of Chinese children to move forward [1–2]. Its inherent rich connotation of ideology and politics makes it an extremely valuable resource for ideology and politics education. In the practical aspects of ideological education in colleges and universities, the red culture is organically integrated into the teaching content, the inheritance of red genes, and the strengthening of the nurturing value of ideological and political education is the inevitable requirement of focusing on the cultivation of newcomers to the era who will take on the big responsibility of national rejuvenation.

History is the best textbook, and Chinese revolutionary history is the best nutrient. Rich red cultural resources are the profound power of ideological and political education in colleges and universities, and red cultural resources can present the content of ideological and political education in colleges and universities from multiple perspectives through the carriers of stories, physical objects, dramas, images, pictures, ruins, venues, etc., and enhance the attractiveness of ideological and political education [3–6]. The integration of red culture into ideological and political education in colleges and universities helps educate and guide college students to firmly adhere to the guiding position of Marxism in the field of ideology, to oppose the erroneous trend of thinking, to consciously resist the penetration of Western ideology, and to help college students to firmly establish their political position [7–10]. The integration of red culture into ideological and political education in colleges and universities can also subconsciously guide college students to feel the revolutionary spirit of the Chinese communists in the revolutionary culture, and feel the spirit of pioneering, enterprising, and innovation in the advanced socialist culture, so that the college students in the new era can study and inherit the red culture more deeply [11–13]. In addition, patriotism is the core of the spirit of the Chinese nation, and red culture education is an important way to cultivate the patriotism of college students, which helps to strengthen college students’ sense of identity, honor and belonging to the country, and inspire college students to become firm promoters and practitioners of patriotism [14–16].

In this paper, SVM text categorization technique is combined with knowledge graph method. Taking the unstructured text data of red culture vertical sites as the research object, the red culture resource data is cleaned and screened before text classification. Then the SVM classification method is utilized to classify the red culture text. The frequency of words in red cultural text is counted, low-frequency weak keywords are eliminated, and strong associated words are labeled. After obtaining the red cultural resources knowledge map entity, construct the red cultural resources knowledge map. The red cultural resources are taken as an important carrier for the cultivation of people in the ideological and political courses, integrated with the ideological and political theory courses in colleges and universities, and fully integrated into the classroom teaching activities. Finally, the correlation regression method is used to analyze the functional effectiveness of integrating red cultural education into ideological and political education.

2

Classification of red cultural texts based on pattern recognition technology

To conduct an in-depth study on the excavation and integration of red cultural resources in the field of ideological and political education, we must first have a clear understanding of the basic concept of red cultural resources. To promote and inherit the red culture of Guangxi under the conditions of the new era, it is necessary to fully excavate and utilize the red cultural resources and play the role of ideological and political education of Guangxi red culture. Red cultural resources include material and non-material cultural resources from the establishment of the Communist Party, the Land Revolution, the Anti-Japanese Revolution, the War of Liberation to the social construction and reform of each historical period, with rich content and great value.

2.1

The process of text categorization

Pattern Recognition is an important branch of Artificial Intelligence that studies how to make computers recognize and classify different patterns. Pattern recognition techniques can be applied to a variety of fields, such as image processing, language recognition, text categorization, and so on. The main methods include artificial neural networks, support vector machines (SVM), decision trees, random forests, and deep learning.

Automatic classification of cultural texts from regions with a high percentage of red blood cells refers to the process of determining the categories according to the text classification system after obtaining the text content. Text classification mainly consists of two phases: the training phase and the classification phase, the former mainly constructs the classification model through the connection between text samples and categories; the latter mainly classifies the test samples based on the classification model and gives the category identification.

Automatic text categorization uses text data involved in training to first construct a classification rule that can later classify text data of unknown type to determine its dependent categories. Text categorization is a mapping process, i.e., mapping text with undefined categories into existing defined categories. Mapping can be two cases: (1) one-to-one mapping, i.e., a text is associated with one category; (2) one-to-many mapping, i.e., a text is associated with multiple categories. In the actual classification process, the task of the training phase is to represent the text to be categorized as a vector with words as elements, and then the features are extracted and represented by weights. After the above preparations, we can start to train the text vectors (which are generally in the form of wordweights) to obtain the classifier model. In the classification process, the obtained classifier is used to classify the text to be classified represented as word-weight vectors, and finally classify the attributed categories. In general, this process should also include performance evaluation, test optimization, etc.

2.2

Textual representation

In SVM text representation, if we use d to denote the text, t to denote the feature items, and W to denote the weights of the feature items, then the text can be mapped as a feature vector V(d) = ((t₁,W₁),…(t_i,W_i),…(t_n,W_n)), where ti(i = 1,2,…n) is some mutually different entries, which generically refers to the basic linguistic units that represent the content of the text; W_i denotes the weight of the t_i in the text, signaling the importance of the word to the text; d denotes a machine readable record.

In SVM, Sim(d₁, d₂) refers to the degree of content correlation between text d₁ and d₂, i.e., text similarity. Usually, the similarity can be calculated by various methods such as distance, vector inner product or angle cosine, and their deformations, and the commonly used metric formulas are shown in the following formulas, in which W_li means the weight of feature t_i of text d₁, W_2i means the weight of feature t_i of text d₂, and n stands for the dimension of the feature space. 1)

The Euclidean distance, or Euclidean distance for short, is shown in equation (1): 1 $S i m (d_{1}, d_{2}) = D_{E} (d_{1}, d_{2}) = \sqrt{\sum_{i = 1}^{n} {(w_{1 i} - w_{2 i})}^{2}} (1 \leq i \leq n)$ $$Sim\left( {{d_1},{d_2}} \right)\> = \>{D_E}\left( {{d_1},{d_2}} \right)\> = \>\sqrt {\mathop \sum \limits_{i = 1}^n {{\left( {{w_{1i}} - {w_{2i}}} \right)}^2}} \left( {1\> \le \>i\> \le n\>} \right)$$

2)

Vector inner product, as shown in equation (2). The main advantage is the low computational intensity, and the disadvantage is the large error in the calculation results: 2 $S i m (d_{1}, d_{2}) = d_{1} \cdot d_{2} = \sum_{i = 1}^{n} w_{1 i} w_{2 i} (1 \leq i \leq n)$ $$Sim\left( {{d_1},{d_2}} \right)\> = \>{d_1}\> \cdot \>{d_2}\> = \>\mathop \sum \limits_{i = 1}^n {w_{1i}}{w_{2i}}\left( {1 \le i \le n} \right)$$

3)

The cosine of the angle of entrapment, as shown in equation (3). The advantage of the cosine calculation is that the value obtained is exactly between 0 and 1: 3 $S i m (d_{1}, d_{2}) = \cos θ = \frac{\sum_{i = 1}^{n} w_{1 i} W_{2 i}}{\sqrt{(\sum_{i = 1}^{n} w_{1 i}^{2}) (\sum_{i = 1}^{n} w_{2 i}^{2})}} (1 \leq i \leq n)$ $$Sim\left( {{d_1},{d_2}} \right)\> = \>\cos \theta \> = \>{{\mathop \sum \limits_{i = 1}^n {w_{1i}}{W_{2i}}} \over {\sqrt {\left( {\mathop \sum \limits_{i = 1}^n w_{1i}^2} \right)\left( {\mathop \sum \limits_{i = 1}^n w_{2i}^2} \right)} }}\left( {1 \le i \le n} \right)$$

2.3

Text categorization preprocessing

The general process of text preprocessing: first of all, the text to be processed for word separation, word separation is a continuous statement through the separator for dispersion, dispersed words should also have a certain independent meaning, these words with independent meaning constitute a word set. Then the deactivated words in it are removed, and finally the keyword set of the text is obtained. In addition, depending on the source of text data, different steps can be taken for preprocessing. For western languages such as English, words have been automatically segmented from each other by spaces, so the process of word splitting is eliminated. For Asian languages such as Chinese, text preprocessing is a significant factor that affects classification accuracy.

2.4

Feature processing

Feature set is the short name for the set of keywords for text preprocessing, which is actually the initial set of feature words for feature processing. Usually the number of feature words in the feature set is very large, but among these words, not all of them have equivalent meanings, and even some words that do not have much practical significance may end up as noise. These words with little meaning and very few occurrences are generally referred to as low-frequency weakly associated words. On the contrary, those words that occur more frequently and have more meaning are called high-frequency strong associates. The purpose of feature processing is to remove as much of the low-frequency weakly associated words as possible and retain the high-frequency strongly associated words. The specific implementation process is as follows: firstly, use word frequency statistics to count the feature items in the initial feature set and eliminate the low-frequency weak keywords through the dimensionality reduction method, and then use the feature weight function to label the extracted strong associated words.

3

The method of constructing the knowledge map of red culture in the perspective of Civic Politics

3.1

Theory underlying knowledge graphs

This paper combines pattern recognition with knowledge graph technology, which can provide richer information sources for pattern recognition technology and improve its accuracy and efficiency. Pattern recognition technology also plays an important role in the construction of knowledge graph, through which useful information can be extracted from a large amount of data and a high-quality knowledge graph can be constructed.

Knowledge graph is a type of knowledge expression with wide significance. The knowledge graph contains nodes, edges, and attributes. Each node represents an entity in the real world, and each edge represents the association relationship between the entities. Simply put, a knowledge graph is a huge ‘network’ that connects information from different sources and categories that are not similar, and it provides a way to look at a problem from the perspective of entity relationships. Knowledge graph has two construction methods: top-down and bottom-up, which represent the construction method based on structured data such as encyclopedic websites and the construction method of extracting resource information through technological means, respectively. The original data types of a knowledge graph are mainly divided into three categories: structured data, semi-structured data, and unstructured data. Methods for storing knowledge graphs include the Resource Description Framework (PDF), the graph database Neo4j, etc. PDF is characterized by its ease of distribution and sharing, but it does not support entity-relationship attributes, and is mainly used in the academic field.

3.2

Construction of Knowledge Graph

The construction of knowledge mapping generally includes knowledge extraction, knowledge fusion and knowledge processing, etc., which can be personalized and adjusted according to the characteristics of data features and specific tasks. Through the above three links to carry out the construction of the knowledge map of red cultural resources, on the basis of which through the integration of red cultural education to the actual effectiveness of the ideological and political. The general construction process of knowledge mapping is shown in Figure 1. 1)

Information extraction extracts possible knowledge units from various structured information and is the first step in building a knowledge graph. Information extraction is an automated technique for extracting structured information such as entities, relationships and entity attributes from data, which is subdivided into three parts: entity extraction, relationship extraction and attribute extraction. At the end of entity information extraction, a large number of discrete named entities can be obtained. The entities are not related to each other, and the second step of relationship extraction is needed if semantic information is to be obtained. Through the extraction of relations, each entity can be associated with another, and a preliminary semantic network of knowledge is obtained.

2)

Knowledge fusion

The purpose of fusion is to combine the same entities or attributes from several different sources into one. At the same time, after information extraction of useful information can be obtained a large number of information fragments, in which there are redundant and erroneous information, which can be eliminated through knowledge fusion.

3)

Knowledge processing

After knowledge extraction and fusion, entities, relationships, and attributes can be extracted from the original data, and the extracted knowledge is fused to obtain a preliminary representation of the ontology. However, this is not the real knowledge, just the basic unit of knowledge. To obtain a structured knowledge network, it is also necessary to process the knowledge, on which a large-scale knowledge system is constructed. Knowledge processing can be broken down into three main areas: ontology construction, knowledge reasoning, and quality assessment. Ontology construction refers to the creation of templates for the concept of ontology, which can be constructed artificially using manual methods, usually formulated by experts in various fields, and then classified and summarized according to the similarity results through the similarity calculation of entities. However, the manual categorization method requires a large amount of work, the knowledge involved is extensive, and it is difficult to find experts with multi-domain knowledge to meet the demand, so most of the current knowledge graphs are oriented to a single.

4

Research on the effectiveness of the integration of red cultural resources in civic and political education

4.1

Red Cultural Resources Knowledge Mapping Construction

4.1.1

Red cultural resources entity identification

The data source of this paper is mainly unstructured text data from red culture vertical sites such as Zhonghong.com and Red Culture.com. From the local red cultural resources text data obtained through crawlers, suitable texts are selected for corpus construction, and these texts should contain as many red cultural resources entities as possible.

The unstructured text data obtained directly from vertical websites related to red cultural resources contains a lot of meaningless noise data, such as spaces, HTML tags, website URLs, and so on. These data need to be denoised and cleaned before being input into the text classification and recognition system for cultural resources. In this paper, by writing the program, we take the regular matching way to clean and screen the red cultural resources data, and remove a large number of meaningless and noisy data that affect the model output contained in the red cultural resources. After obtaining thecleaned text data, it is inputted into the entity prediction of red cultural resources knowledge map that has been carried out through red cultural resources entity recognition, and the final number of red cultural resources knowledge entities obtained is shown in Table 1: a total of 27,454 red cultural resources knowledge map entities are obtained, and 5,694 red cultural resources knowledge map entities are retained after de-emphasis and manual sorting and categorization operations.

Table 1.

The number of red cultural resources entities

Red cultural resource entity type	Acquisition quantity	Go back and manually organize the quantity
Red scenic spot	2189	860
Red figure	16162	2495
Red culture relic	574	211
Red work	2567	1194
Red mechanism	3526	547
Historical event	2436	387

4.1.2

Red Cultural Resources Entity Attribute Acquisition

The results of red cultural resources entity attribute acquisition are shown in Table 2, using the 5694 red cultural resources knowledge map entities previously acquired and processed through the red cultural resources entity identification model, and adding the seven red spiritual entities known in the knowledge modeling stage to the entity list, setting “URL + entity name” through Python using Selenium for the list to be crawled, i.e., to obtain their encyclopedia URLs, and according to the obtained links of the red cultural resources knowledge entity encyclopedia, the html code of the page containing the red cultural resources entity is parsed by Python’s lxml module to obtain the attribute information of the red cultural resources entity.

Table 2.

The number of physical properties of red cultural resources

Red cultural resource entity type	Attribute acquisition quantity
Red scenic spot	2185
Red figure	16160
Red culture relic	564
Red work	2553
Red mechanism	3521
Historical event	2426
Red spirit	30

After analyzing and defining the object attributes of classes and classes in the red cultural resources knowledge mapping schema, the red cultural resources ontology schema can be obtained as shown in Figure 2.

Using the LTP tool, the corpus containing entities is inputted into the LTP model for analysis, and a total of 14070 results of entity relationships of the red cultural resources knowledge graph are obtained, and 827 results are retained after de-emphasis and manual correction and categorization, and all the resultant relationship types are counted, and a total of 86 types of relationships between entities of the red cultural resources are obtained, and will be grouped and merged in accordance with the relationship types defined in the schema layer, and the obtained Relationships are specifically shown in Table 3, which can be obtained as → red cultural resource entity, relationship name, red cultural resource entity → ternary group.

Table 3.

Red cultural resource entity relationship acquisition results

Relational type	Meaning	Relationship name	Number of relationships
R-PER_EVE	The relationship between red characters and historical events	Attend, hold, lead, command, etc	112
R-PER_ORG	The relationship between red characters and organizations	Serve in, attend, create, join, lead. Open up. Belong to	240
R-ORG_PER	The relationship between the organization and the red characters	Secretary, deputy secretary, commander, deputy commander, member, vice chairman	113
R-PER_ARE	The relationship between red characters and red scenic spots	Home, born, hometown is, live in	6
R-PER_WOR	The relationship between red characters and red works	Writing, singing, choreography, writing, drawing, playing	72
R-PER_WOR	The relationship between red works and red cultural relics	Author, description	8
R-PER_REL	The relationship between red characters and red cultural relics	Save, donate, collect	44
R-PEL_ARE	The relationship between red and red scenic spots	Collection, Get given	26
R-ARE_PEL	The relationship between red scenic spots and red cultural relics	Rehide	11
R-ORG_EVE	The relationship between organizational institutions and historical events	Initiate, launch	9
R-WOR_EVE	The relationship between red works and red events	Describe, Memorialize	4
R-PER_PER	The relationship between red characters and red characters	Son, Daughter, Mother, buddy	182

4.2

Integration of Red Cultural Resources into Practical Teaching of Civic and Political Science Courses

This is taken as an empirical investigation, the use of questionnaires in the form of a survey, selecting the method of simple random sampling survey, X city college sampling survey, analyze the survey data and draw conclusions. This survey is taken in the form of anonymous, with the help of the online network platform questionnaire star survey, take the form of online placement, a total of 623 questionnaires were issued, the number of questionnaires recovered 623, the recovery rate is 100%, the effective recovery rate is 100%.

Satisfaction with the integration of cultural resources from the Latino community into the ideological education of colleges and universities and the impact of this integration. The statistical results of the questionnaire are shown in Table 4, 36.76% of the students think that the school pays more attention to the integration of red cultural resources into the practical teaching of college civic and political science courses, 12.04% of the students think that they pay great attention to it, but there are still 39.65% of the students think that the school pays less attention to the integration of the two. From the statistical results of question 2, it can be seen that more than half of the students are relatively satisfied with the status quo of the integration of red cultural resources into the practical teaching of college Civics and Politics courses, 7.87% of the students said they were very satisfied, and 33.23% of the students said they were basically satisfied. From the statistical results of question 3, it can be seen that the integration of red cultural resources into the practical teaching of ideology and politics courses in colleges and universities helps to improve personality and all-round development (78.17%), can fully understand the history of the Party and set up correct values (69.82%), and can strengthen socialist ideals and beliefs (64.04%).

Table 4.

Integration dimension

Question	Options	Frequency	Percentage
The importance of the cultural resources to integrate into the practice teaching of thinking politics	Pay great attention to	75	12.04%
	Attach importance to	229	36.76%
	Different importance	247	39.65%
	disrespect	72	11.56%
The present situation of the practice teaching of the red cultural resources in the university	Very satisfied	49	7.87%
	Be satisfied with	344	55.22%
	Basic satisfaction	207	33.23%
	unsatisfactory	23	3.69%
The significance of red culture in the practice teaching of university thinking politics	Broaden the red education approach	319	51.20%
	To enrich the content and form of the practice of the university	378	60.67%
	Improve personality and all-round development	487	78.17%
	Understand the party history and set the correct values	435	69.82%
	Provide mental nutrients and thought weapons	353	56.66%
	Learn the ideas of each stage and clarify the advantages of socialism	316	50.72%
	Resolute socialist belief	399	64.04%
	Other	72	11.56%

4.3

Analysis of the effectiveness of the integration of red cultural resources in civic and political education

This section uses the method of correlation regression analysis to analyze the relationship between red cultural resources education and college students’ civic education. Correlation regression analysis is a statistical method to study correlations between things and determine their degree of correlation. This method is used to quantitatively analyze the scores of college students’ value orientation and the effect of ideological and political education in colleges and universities, and at the same time, according to the method of “Value Orientation Distance Index”, college students’ orientation towards red cultural resources is operationalized into quantifiable comparative analysis questions, which are based on the college students’ recognition of the red cultural resources, practice of the red cultural resources, and their recognition of the red cultural resources with Chinese characteristics, From the five aspects of college students’ recognition of red cultural resources, practice, confidence in socialism with Chinese characteristics, treatment of the relationship between personal and social development, and the internal driving force of accepting values education to judge college students’ orientation to red cultural resources, the maximum score for each aspect is 10 points out of a total of 50 points, the higher the score, the more scientific the orientation of the students. From the college students qualified political quality, scientific ideological quality, good moral quality, healthy psychological quality and comprehensive cultural quality of five aspects to measure the effect of ideological education in colleges and universities, each aspect of the maximum score of 10 points, a full score of 50 points, the higher the score, indicating that the effect of ideological education in colleges and universities is better. The “student orientation” is set as the independent variable, noted as x, “the effect of ideological and political education” is set as the dependent variable, noted as y, the following linear relationship can be established: y = A + Bx + K. In the formula, A and B are the parameters to be determined, A is the intercept of the regression line, B is the slope of the regression line, K is the slope of the regression line, A is the intercept of the regression line, B is the slope of the regression line, K is the slope of the regression line, K is the random error term that depends on the effect of ideological and political education. According to the empirical investigation done in the previous period, the data are organized as shown in Table 5.

Table 5.

Effect score table

Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education
1	50	47	11	48	48	21	36	34
2	37	41	12	40	38	22	31	30
3	50	40	13	43	41	23	42	44
4	50	40	14	42	41	24	50	46
5	40	40	15	39	38	25	44	46
6	48	47	16	33	33	26	43	43
7	40	42	17	38	37	27	50	48
8	45	44	18	38	37	28	38	36
9	46	45	19	50	47	29	44	40
10	43	46	20	47	46	30	39	42

According to the above data, a scatter plot can be made as shown in Figure 3: the regression equation R² = 0.65889 (R² is the correlation index, the closer the value is to 1, indicating that the correlation between the two variables is stronger). It can be seen that there is a linear correlation between the two, from which it can be concluded that the two show a high degree of correlation. As the higher the score of students’ personal value orientation, the better the effect of students’ acceptance of the school’s civic education, it can be seen that students’ values play an important role in the effect of civic education, and the education of red cultural resources has an important position in the civic education of college students. Therefore, we can enhance the effectiveness of college students’ ideological and political education by strengthening the education of cultural resources for students.

5

Conclusion

In this paper, to explore the effective strategy of integrating red resources into the cultivation of people in the ideological and political curriculum of colleges and universities, firstly, we have to categorize, screen, integrate, and construct a knowledge map of the data of the red culture vertical site. Secondly, it is necessary to fully develop and utilize the red resources and combine them with the Civics classroom for practical research. 1)

Attribute information of red cultural resource entities can be obtained in 7 categories, i.e., red scenic spots, red characters red relics, red works, organizations, historical events, and red spirit. A total of 86 inter-entity relationships between red cultural resources can be obtained by analyzing the relationships between entity attributes.

2)

Most of the students show a more satisfactory attitude towards the integration of red culture into the practical teaching of Civics in colleges and universities. And they think that integrating the red culture into the teaching of civics can improve their personality and make them develop comprehensively.

3)

Red cultural resources have a very important position in the Civics education in colleges and universities. In the future, the quality of ideological and political education can be improved by strengthening the education of red culture among students.

Language:: English

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Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education
1	50	47	11	48	48	21	36	34
2	37	41	12	40	38	22	31	30
3	50	40	13	43	41	23	42	44
4	50	40	14	42	41	24	50	46
5	40	40	15	39	38	25	44	46
6	48	47	16	33	33	26	43	43
7	40	42	17	38	37	27	50	48
8	45	44	18	38	37	28	38	36
9	46	45	19	50	47	29	44	40
10	43	46	20	47	46	30	39	42

Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education
1	50	47	11	48	48	21	36	34
2	37	41	12	40	38	22	31	30
3	50	40	13	43	41	23	42	44
4	50	40	14	42	41	24	50	46
5	40	40	15	39	38	25	44	46
6	48	47	16	33	33	26	43	43
7	40	42	17	38	37	27	50	48
8	45	44	18	38	37	28	38	36
9	46	45	19	50	47	29	44	40
10	43	46	20	47	46	30	39	42

Research on the Effectiveness of Integrating Red Culture Nurturing Function in Civic and Political Education of Colleges and Universities Based on Pattern Recognition

Yujun Yang

Published Online: Mar 19, 2025

Received: Oct 27, 2024

Accepted: Jan 28, 2025

DOI: https://doi.org/10.2478/amns-2025-0388

KeywordsSVM text categorization, Knowledge graph, Red culture, Regression equation

© 2025 Yujun Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
SVM text categorization, Knowledge graph, Red culture, Regression equation

Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education	Number	Red culture	The effect of thinking on education
1	50	47	11	48	48	21	36	34
2	37	41	12	40	38	22	31	30
3	50	40	13	43	41	23	42	44
4	50	40	14	42	41	24	50	46
5	40	40	15	39	38	25	44	46
6	48	47	16	33	33	26	43	43
7	40	42	17	38	37	27	50	48
8	45	44	18	38	37	28	38	36
9	46	45	19	50	47	29	44	40
10	43	46	20	47	46	30	39	42