bluuebunny/crossref_metadata_2025_split huggingface dataset:
What Teacher Confirmation Means to EFL Learners: High and Low Achievers’ Voices
Authors: Novianti Sri , Kurniawan Eri , Selpia Delpani , Lutfiyana Faza 
Published: February 2024

One of factors leading to the success of students’ learning is interpersonal relationships between teachers and students. One indicator contributing to this relationship is communication; one of its aspects is teacher confirmation, happening in the classroom interaction. The current study aims to reveal students’ perceptions on teacher confirmation as one of elements affecting student-teacher interpersonal relationships and how teacher confirmation affects their learning. This case study involved two high school students - high achiever and low achiever - selected from their English competence judged by the English teacher. Adapting Ellis’ Teacher Confirmation Scale (2000), sequenced and open-ended interview was conducted to both students in order to collect the data. The findings show that teacher confirmation exists in the classroom interaction and is perceived by both participants. Notwithstanding, teacher confirmation does not equally give positive impacts on students’ learning in general. In conclusion, high and low achiever responds teacher confirmation in different ways, resulted from factors which have not yet been explored in this study. Examining related intervening factors and the degree to which they affect how students perceive and respond to teacher confirmation is recommended for future research.
In Vitro Effects of Silver Nanoparticles on Pathogenic Bacteria and on Metabolic Activity and Viability of Human Mesenchymal Stem Cells
Authors: Ptasiewicz Maja , Chałas Renata , Idaszek Joanna , Maksymiuk Paweł , Kister Mateusz , Kister Karolina A. , Kurzydłowski Krzysztof J. , Magryś Agnieszka 
Published: February 2024

Abstract
The rapid development of nanotechnology has led to the use of silver nanoparticles (Ag-NPs) in various biomedical fields. However, the effect of Ag-NPs on human mesenchymal stem cells (hMSCs) is not fully understood. Moreover, too frequent an exposure to products containing nanosilver in sublethal amounts raises widespread concerns that it will lead to the development of silver-resistant microorganisms. Therefore, this study aimed to evaluate the mechanism of action of Ag-NPs on hMSCs by analyzing the cellular uptake of Ag-NPs by the cells and its effect on their viability and to assess antimicrobial activity of Ag-NPs against emerging bacterial strains, including multidrug-resistant pathogens. For metabolic activity and viability evaluation, hMSCs were incubated with different concentrations of Ag-NPs (14 μg/mL, 7 μg/mL, and 3.5 μg/mL) for 10 min., 1 h and 24 h and subsequently analyzed for their viability by live-dead staining and metabolic activity by the MTS assay. The effect of Ag-NPs on bacterial pathogens was studied by determining their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). In conclusion, it was observed that exposure of hMSCs to Ag-NPs of size <10 nm has no cytotoxic effect on the metabolic activity of the cells at the concentration of 3.5 μg/mL, with minimal cytotoxic effect being observed at the concentration of 14 μg/mL after 24 h of incubation. Our findings also confirmed that Ag-NPs at the concentration of 4 μg/mL are effective broad-spectrum bactericidal agents, regardless of the antibiotic-resistance mechanism present in bacteria.
Intelligent Translation System Aiding High-Quality Writing in English in the Age of the Internet
Authors: Wang Lei , Wang Xuemin , Wang Fang , Li Qing 
Published: February 2024

Abstract
This study focuses on applying intelligent translation systems in assisting high-quality English writing in the Internet era. The study analyzes statistical machine translation techniques, especially N-gram language modeling and word alignment techniques, and their crucial role in translation quality improvement. In English writing, the intelligent translation system significantly improves the quality of students’ Writing through word block translation. By analyzing 320 students, the mean value of self-efficacy in writing skills of students in the high-quality writing group was 3.78, significantly higher than that of the low-quality group, which was 2.82. After the experiment, 0.57 students indicated that they would improve their English writing vocabulary with the aid of the Intelligent Translation System, which showed the potential of the Intelligent Translation System to enhance students’ interest in and autonomy in Writing. Average distribution analysis shows that word block usage positively correlates with writing performance, with an R Square value of 0.6726. The intelligent translation system improves students’ English writing and enhances their self-efficacy, which is of great significance to English teaching.
Text Value and Linguistic Characterization in Chinese Language Literature Based on Text Mining Techniques
Authors: Liu Min , Hu Shuling , Qing Wenting 
Published: February 2024

Abstract
This study applies text mining techniques to deeply analyze Chinese language and literature’s text value and linguistic features. The study adopts the methods of textual disambiguation, vector space modeling, semantic network and Labeled-LDA model. Taking the novels of Yu Hua and Ge Fei as an example, it reveals the differences between the two writers in linguistic features such as using punctuation, average word length, and sentence discrete degree. The study provides a comprehensive heat score for the novels based on three dimensions: reading base group, reading gain, and reading discussion. The results show that the frequency of period use in Yu Hua’s works is decentralized, while Ge Fei’s works are more concentrated. Ge Fei’s average word length is slightly higher, showing a tendency to use multi-syllabic words. The novel popularity and heat scores conform to a power law distribution, reflecting the Pareto rule that 80% of the popularity is concentrated on 20% of the hot novels. This study provides a new perspective on Chinese language and literature through the application of text mining technology, and its methods and tools can effectively enhance the effectiveness and efficiency of teaching.
Exploring the Subjectivity of English Academic Discourse in the Context of Big Data
Authors: Pan Ying 
Published: February 2024

Abstract
This study develops a sentiment analysis model for English academic discourse based on word information to effectively understand and analyze the sentiment tendencies in English literary texts. The structure of the model includes word embedding layer, character-level feature extraction, word-level feature extraction and feature fusion and classification layer. The word embedding layer realizes the mapping between word vectors and word vectors by microblogging pre-trained word vectors. The character-level feature extraction session uses a multi-window convolutional layer to capture N-Gram information. In contrast, the word-level feature extraction obtains deeper semantic information through a Bi-LSTM layer and fuses it with character-level information to enhance robustness. The feature fusion and classification layer further combines these features and determines the fusion weights through a linear layer to achieve sentiment classification. In performance tests, the model achieves 92.5% sentiment classification accuracy on the standard dataset, an improvement of about 6% compared to traditional methods. In particular, the accuracy is improved by 5% when dealing with text with sentiment polarity transition, showing good adaptability. In addition, using 657 positive and 679 negative sentiment words as seed words effectively expands the sentiment lexicon and enhances the comprehensiveness and accuracy of sentiment analysis.
The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background
Authors: Shang Jin 
Published: February 2024

Abstract
Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual balance factors affecting the creation of landscape paintings. By applying a vector median filter, this study successfully removes noise from color landscape painting images and uses vectors to represent these noise-reduced images. Lab color model was constructed and combined with cluster analysis technique to calculate the color difference between image pixel points. Binarization of the image is achieved by preprocessing the segmented image. The study estimated and analyzed landscape paintings’ aesthetic characteristics and quantitative values. The results show that among the 30 selected landscape paintings, the percentage of white space area is more than 0.4 is 36.67%, while the rate of less than 0.4 is 63.33%. Among the landscape painting creators who participated in the study, the mean of the total scores of the experienced ones on the four visual psycho-spatial abilities was 83.39, which was 14.217 points higher than that of the inexperienced group. This indicates that the method proposed in this paper is very effective in assessing the visual psychospatial abilities of landscape painting creators, and that the creators can improve their creative skills after long-term training.
An analysis of the value and linguistic features of contemporary literary texts based on knowledge graph
Authors: Zhai Xingyu 
Published: February 2024

Abstract
In this paper, we crawled and analyzed the dataset of contemporary literature works through Python technology, and after preprocessing the data with the use of the word splitting algorithm, we applied the CasRel entity-relationship extraction model based on the Transformer and the BERT model to efficiently extract the entity knowledge in contemporary literature works. Then, a knowledge map of modern literature was established by combining the Neo4j graph database. Several novels by Mo Yan, Jia Pingwa and Chen Yan were selected as cases to analyze the textual values and linguistic features of contemporary literary works. The Analysis reveals significant differences in the linguistic features and connotative values of works by different authors. For example, the average frequency of periods per thousand words in Mo Yan’s works is 0.2187, while the corresponding frequency in Jia Pingwa’s works is 36.06% lower than that of Mo Yan. The cumulative application frequency of monosyllabic and disyllabic words in Chen Yan’s works exceeds 80%, and the lexical density ranges between 67.5% and 71.5%. By clarifying the linguistic features of different contemporary literary works, it can help academic creators better find the resonance point with readers and provide diversified innovative paths for literary creation.
The Construction of Informatization System of Internet Technology Mathematics Teacher Education Course
Authors: Li Yingchao 
Published: February 2024

Abstract
This paper focuses on constructing a regional digital educational resource sharing platform. It applies optimized ant colony clustering technology to support the platform’s personalized service to achieve accurate distribution of educational resources. The research subjects were undergraduate and graduate students majoring in mathematics in teacher training, and by comparing their teaching abilities before and after using and not using the platform, it was found that the average scores of students using the platform were significantly higher than those of students who did not use the platform on each evaluation factor. Undergraduate students’ information literacy competence improved with the academic year, with first-year students’ scores between 20 and 50, while senior students’ scores between 70 and 100. At the graduate level, the difference between first-year graduate students and junior undergraduates was significant, but the difference with senior undergraduates was slight (p-values of 0.042 and 0.053, respectively). The digital education resource sharing platform effectively improved the teaching ability of teacher students. However, most of the students lacked experience in informatization practice activities.
A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion
Authors: Liu Yiming 
Published: February 2024

Abstract
This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.
Research on the Current Situation and Countermeasures of Improving Talent Cultivation Quality by Managing Skill Competitions in Higher Vocational Colleges in the Internet Era
Authors: Lun Zhiguo 
Published: February 2024

Abstract
Skills competition is an essential link in the cultivation of applied talents in higher vocational colleges and universities, and plays a crucial role in improving the quality of practical teaching. This paper focuses on the mode and process of talent cultivation through skill competitions in higher vocational colleges and universities. It conducts an in-depth investigation on the cultivation quality. A comprehensive evaluation model of talent cultivation quality of skill competition is constructed using the particle swarm algorithm to optimize the K-means algorithm and combining with the principal component analysis method. The model was applied to a sample of students from university X. By collecting and analyzing the data, the quantitative assessment of the students’ abilities in each dimension and the hierarchical categorization of the comprehensive skills were carried out. The study results showed that students who participated in the skills competition performed well in their professional skills, averaging 0.312. However, there is still room for improvement in collaboration ability. Students of agricultural engineering, logistics management and finance are outstanding in management practice power and professional competitiveness, with a comprehensive score of more than 0.45. While economics, law, geology and accounting students have low complete scores, which need to be further strengthened for cultivation. To improve the quality of talent cultivation, higher vocational colleges and universities should build a training mode of “integration of competition and education”, deepen the cooperation between schools and enterprises, and improve the guarantee system of skill competitions, to comprehensively promote the enhancement of students’ abilities.
Algorithm Design and Application of Grammatical Structure and Function Learning in Chinese Writing for Intelligent Analysis
Authors: Ma Jingtao , Chen Liang 
Published: February 2024

Abstract
This study utilizes the Larsen-Freeman three-dimensional theory of grammar teaching to quantitatively analyze the grammatical attributes “form”, “meaning” and “usage” in the Chinese writing process. The study uses the PCFG model to extract the syntactic dependency tree and represents the features of the tree structure with the help of TF-IDF. Meanwhile, the Transformer model extracts the semantic and syntactic features of sentences. At the same time, the positional encoder is removed to ensure that the model obtains information from the syntactic level. Further, an unsupervised method for generating grammatical textual recapitulation for Chinese Writing is proposed, as well as a Grammar Tree Generator for generating recapitulated sentences, which efficiently extracts the features of the input grammatical sequences. In addition, the study also includes writing feature analysis based on grammatical function matching, using a hierarchical clustering algorithm to analyze the similarity of 60 grammatical functions. Finally, validation was performed on 30 Chinese writing text collections, each containing 10 compositions, and the results showed high accuracy of unlabeled grammatical function recognition. The LDA model determined the optimal number of writing topics to be 150. This study highlights the potential application of intelligent analysis techniques in improving the quality of Chinese Writing. It provides new perspectives for an in-depth understanding of the interplay between grammatical structures and functions in the Chinese writing.
Effective Application of Multiple Intelligences Theory in Music Education
Authors: Ni Meiying , Yang Fangling , Liu Mei 
Published: February 2024

Abstract
This article discusses an innovative teaching model that integrates the theory of multiple intelligences into music education, including the design of teaching strategies and evaluation frameworks. Using fuzzy theory and credibility mechanism, the article proposes a fuzzy comprehensive evaluation model for assessing the quality of music teaching. The model realizes thorough evaluation by determining the evaluation set and the single-factor judging method. The study takes two classes of music majors as an example to conduct the factor analysis of the Students’ Music Performance Rating Scale and finds four main factors with a cumulative contribution rate of 33.36%. The results showed that the experimental group applying the Multiple Intelligences teaching strategy was significantly higher than the control group in music performance. The post-test after one year teaching experiment showed that the students in the experimental class showed highly significant improvement in bodily-kinesthetic intelligence, musical intelligence, and self-cognitive intelligence (P<0.01), as well as linguistic, spatial, and interpersonal intelligences (0.01<P<0.05). In contrast, logical-mathematical intelligences did not show any significant improvement (P>0.05). The study confirms the practical application of multiple intelligences theory in music education, shows the considerable effect of numerous intelligences teaching strategies in enhancing students’ intelligence, and provides new methods and perspectives for music teaching.
Research on Early Warning Model of Wushu Event Broadcasting Right Operation Risk Based on Big Data XGBoost Algorithm
Authors: Li Xing , Ma Ying , Cui Zhiying , Cui Yongxia 
Published: February 2024

Abstract
Under the background of the development of new media technology, the attention of wushu events in the society is gradually increasing, which makes the competition in the event broadcasting market more and more intense. This paper focuses on the problem of predicting the operational risk of wushu event broadcasting rights, based on the GBRT algorithm, innovatively improves the traditional loss function, introduces the regular term, and proposes the application of XGBoost algorithm in the operational risk prediction of wushu event broadcasting rights. The improved algorithm divides the operational risk of broadcasting rights into two main levels, covering three primary and 10 secondary indicators. In this study, the XGBoost algorithm is applied in the early warning of informing proper operation risk, which is classified into two main levels, covering 3 primary and 10 secondary indicators. The article also conducts an in-depth experimental analysis of the risk of overpremium of the event rights and the risk of matching the audience’s demand. In addition, according to the results of audience analysis, men have become the primary audience of wushu events, with a frequency of up to 401 times. Based on the XGBoost algorithm, the wushu event broadcasting right operation risk warning system can effectively predict and help the event broadcasting platform to avoid the potential operation risk, which provides valuable data support for the market decision-making.
Exploring the Problems and Countermeasures in the Practice of Elderly Wisdom Sports Services in Anhui Province Based on Principal Component Analysis Methods
Authors: Wang Jun , Zheng Zhongwen 
Published: February 2024

Abstract
With the continuous progress and broad application of cutting-edge information technology such as big data, cloud computing, virtual reality and artificial intelligence, senior sports services has ushered in unprecedented development opportunities, especially the rise of senior intelligent sports services. In this study, we applied the principal component analysis method to conduct in-depth screening of evaluation indexes to assess the quality of the practice of senior intelligent sports services in Anhui Province. Through the comprehensive analysis of the eigenroots and the contribution ratio of variance, this paper successfully identified the key factors and realized the effective dimensionality reduction of the data. Meanwhile, we adopted the logistic equation to accurately estimate the relevant parameters. Through the comprehensive analysis of eigenroot and variance contribution ratio, this paper successfully identifies the key factors, realizes the effective dimensionality reduction of the data, and adopts the logistic equation to accurately estimate the relevant parameters. The analysis results show that in the senior intelligent sports service index system, the highest weight value of platform is the operation and management platform, which reaches 0.4754. In the aspect of the demand for sports information of the seniors, the demand for online sports knowledge and skills is the highest, and the demand for online sports knowledge and skills is the highest. Regarding the demand for sports information of the elderly, the online acquisition of sports knowledge and skills has the highest demand of 203, followed by the demand for regular online sports knowledge lectures of 181. In the demand for outdoor bright trails, the elderly strongly want to enter their fitness and energy consumption data for evaluation, with a high degree of demand of 351. In addition, the demand for heart rate testing of the intelligent body testing station is the highest, with a degree of 186, followed by body fat testing, with a degree of 179. The Research in this paper not only focuses on the current situation and problems of intelligent sports services for the elderly, but also explores the methods and strategies to solve these problems, aiming to provide practical reference and guidance for the effective implementation of intelligent sports services for the elderly.