bluuebunny/crossref_metadata_2025 huggingface dataset:
Augmented Reality-based Interactive Typography Experience and Evaluation
Authors: Li Hong
Published: July 2024

This paper explores the innovative integration of Augmented Reality (AR) technology into font design, emphasizing the user experience, visual performance, and spatial experientiality of AR interactive fonts. Central to this investigation is the deployment of AR-based calibration technology, which establishes a spatial coordinate system specifically for AR fonts. When synchronized with the camera model, these virtual fonts are seamlessly registered within real-world scenes. To enhance user engagement, gesture interaction design is incorporated, enabling interactive functionalities within the font system. The effectiveness of this design is evaluated through user testing, where the interactive performance is assessed based on audience experience post-implementation. The findings reveal that interactive fonts are highly effective in content display, achieving a user acceptance rate of 0.7659, and in content retrieval systems, with a rate of 0.7404. Audiences demonstrated a preference for conducting operations independently and showed a particular inclination towards font performance-based projects. The study concludes that the interactive font design project, which leverages augmented reality and gesture interaction technologies, holds substantial commercial potential.
Virtual Reality Technology Applied to the Immersive Experience of Teaching Art History
Authors: Pei Xiaopeng
Published: July 2024

Based on the demand to adapt to the future development of education, the application of virtual reality technology in the field of education is becoming more and more extensive and in-depth. This paper applies virtual reality technology to art history teaching, constructs an art history teaching method using virtual reality technology, and designs a complete teaching process. The EEG signals of students in the teaching process are collected and pre-processed. The features in the EEG signals are extracted by using the SPCNN model with dual convolutional kernels in parallel. The EEG features are output through the convolutional layer and the all-connected hierarchy processing. A support vector machine calculated the maximum distance between the samples and the hyperplane, and the classification and recognition results of EEG features were obtained. The frontal channel TBR change values of each subject student in virtual reality technology art history teaching were significantly lower than those of the traditional art history teaching mode (p<0.01). The frontal F7 channel TBR values of the subject students in different teaching modes were significantly different (p=0.004<0.01). Meanwhile, students gained a strong sense of presence in virtual technology teaching, and the motivation of students to learn art history after participating in virtual reality technology teaching was significantly higher than that of the pre-test. This paper’s teaching model is designed to promote students’ immersion and concentration in art history teaching.
Consumer evaluation mechanisms on e-commerce platforms: reputation management and analysis of influencing factors
Authors: Wang Lidong
Published: July 2024

This paper proposes a regression algorithm to statistically relate the relationship between the relationship variables and proposes a multiple linear regression model to conduct a linear correlation study between a dependent variable and multiple independent variables. The factors that affect the value of merchant reputation in the content of consumer evaluation are regressed and analyzed, and a model for evaluating merchant reputation is proposed based on the regression results. In the merchant reputation evaluation model, the Bayesian algorithm and multi-source data fusion method are used to calculate the merchant weight, the Euclidean distance is used to calculate the platform weight, and the judgment matrix is constructed to calculate the merchant e-commerce reputation score. In the model performance test, 10 Taobao merchants are randomly selected for reputation calculation. The highest merchant reputation scores of 4.582 and 4.584 can be obtained for merchants S9 and S10, and the index scores are more accurate. The original Taobao reputation scores of merchants S4, S8, and S9 are all 4.97. At the same time, the model in this paper calculates them to be 4.404, 4.304, and 4.582, respectively, which are more reflective of the real reputation level of merchants.
Practices and Challenges of Artificial Intelligence-Assisted Teaching in Vocational Undergraduate Public English Classrooms
Authors: Wang Yanhua , Cui Cui
Published: July 2024

This paper first introduces the architecture of the natural language processing system and then analyzes how natural language processing decomposes the probability distribution function by multi-factor form and Bayesian formula to improve the efficiency of function description. Then, NLM is applied to enhance the statistical coefficients of the N-gram model, which solves the problems of data sparsity and dimensionality disaster. By using natural language understanding in English teaching practice and experimenting with the teaching effect, it was found that the overall English achievement of the experimental group increased by 5.4 points, the average Z-score reached more than 3 points, and 96.6% of the students were interested in the teaching method. The new teaching method is demonstrated to have a significant impact on the improvement of English scores.
Integration Strategies of Information Technology in Mathematics Education and Student Motivation Enhancement
Authors: Yan Yunna
Published: July 2024

Integrating information technology with the curriculum has been closely watched by the educational community, and how well the two are integrated determines the effectiveness of mathematics education. This paper proposes an implementation scheme for mapping mathematical knowledge. Firstly, the Word2vec word vector model is used to acquire data from the original mathematical text, and then the Conditional Random Field model is used to extract the knowledge entities, and the Markov Logic Nets are used to extract the relationships between the knowledge entities. Then, the Path Ranking algorithm is utilized on the basis of entity alignment to realize the knowledge inference of the subject knowledge graph and mine the learning paths in the knowledge graph. Through the experimental and questionnaire methods, it was found that the math scores of the experimental class before the comparison experiment increased by 9.4337 points, which was significantly ahead of the control class. The average number of students who thought knowledge mapping teaching greatly improved their interest in math learning was 46.81%, while 31.29% believed that the new method had a relatively positive impact on their motivation to learn. It shows that knowledge mapping can be an effective method of teaching information technology.
Research on Innovation and Entrepreneurship Education Reform of Electrical Engineering and Automation Professionals Driven by Big Data
Authors: Li Ning , Zhao Ziyu , Yang Dongxu
Published: July 2024

The application of big data in various fields has become one of the important trends in the development of the information age, and in this context, educational reform in the context of big data has also become an important issue in the field of education. In this paper, we design a “four-in-one” innovation and entrepreneurship education model and combine the big data-driven algorithms, including association rules and clustering mining, to realize the driving of the education model. Specifically, the systematic clustering algorithm is used to analyze the students’ classroom performance, explore the association rules between the student’s classroom performance and their course grades, and finally, this paper applies the “four-in-one” education model to the classroom teaching of innovation and entrepreneurship courses to study the practical application effect of the model. The results show that after the cluster analysis of the student’s classroom performance data, three categories of students with different levels of classroom performance were formed, accounting for 49.46%, 41.68%, and 8.86%, respectively. After three applications of the new education model, the teacher’s participation in classroom speech, questioning, and silence decreased by 12.75, 5.47, and 3.54 percentage points, respectively. In contrast, the student’s speech and questioning rates increased from 5.86% and 10.98% to 8.37% and 19.84%, respectively. The “four-in-one” innovation and entrepreneurship education model designed in this paper has good practical application effects.
Research on the Innovation of Hotel Talent Cultivation Mode in the Context of Municipal Industry-Education Integration in the Information Age
Authors: Wei Wei , Li Yi
Published: July 2024

In the context of the information era, the integration of industry and education, as a key direction for future educational reform, imposes higher standards on the quality of talent cultivation in colleges and universities. The study develops and investigates the hotel talent cultivation model and operation mechanism based on industry-education integration, starting from industry-education integration’s guiding ideology and principles. Then, in order to confirm the realistic feasibility of the hotel talent training model, the research object is selected, the relevant questionnaire is designed, and after the reliability test of the questionnaire, the questionnaire is distributed. The data are collected, and then the statistical analysis algorithm, including the difference test, correlation analysis, and regression analysis, is used to verify the examples. The data showed that corporate internship time, corporate internship environment, corporate internship management, corporate internship course planning, on-campus supervisors, and off-campus supervision were all significantly and positively correlated with satisfaction (Sig<0.05). At the same time, the regression equation was satisfaction= 0.2989+0.0459*corporate internship time+0.1329*corporate internship environment+0.0641*corporate internship management+ 0.1034*Enterprise Internship Curriculum Planning + 0.1017*Internship Supervisor + 0.0762* Externship Supervision. This study not only ensures the effectiveness, continuity, and standardization of talent cultivation but also helps to promote innovation in hotel talent cultivation modes in the context of industry-education integration.
Innovative promotion of cultural heritage and high-quality integration of the tourism industry
Authors: Zhang Ya
Published: July 2024

In the context of the high-quality economic development imperative, it is both necessary and urgent to expedite and deepen the integration and interconnection between cultural heritage and the tourism industry. This study constructs an evaluation index system for the integration and development of cultural heritage and tourism by reviewing pertinent literature and computing index weight values utilizing the Analytic Hierarchy Process (AHP). Furthermore, this paper introduces a model that assesses the coupling degree and coordination between these sectors by integrating the index system and analyzing the decoupled levels of coupling coordination. Additionally, a system dynamics model is developed to facilitate innovative enhancement of this integrated development. Empirical results indicate that the integration and development level of cultural heritage and tourism in Longji Town is on an upward trajectory. However, it remains at a nascent stage of coordination with a score of 0.6983. Innovation-driven initiatives have spurred growth across all dimensions within the cultural heritage and tourism subsystems in Longji Town. By 2024, the integration level between these two sectors is projected to reach 0.937, signifying a state of high-quality, coordinated development. This research provides empirical support for the innovative advancement of integrated development between cultural heritage and tourism industries. It offers valuable insights for refining and upgrading the strategy for integrated development.
Research on the Integration Strategy of Technology and Teaching in the Construction of Accounting Smart Classroom in Colleges and Universities
Authors: Li Mingwei , Dong Lize
Published: July 2024

In the “Internet+” era, the smart classroom has emerged as a significant focal point in the research on educational informatization. This advancement represents a synergistic melding of novel technologies and academic practices. Accordingly, this study proposes a smart classroom teaching model tailored for accounting courses in higher education institutions. Within this framework, we have developed a specialized model to evaluate teaching efficacy in such environments. This evaluation model incorporates an enhanced version of the basic YOLOv5s model, which has been modified in three key areas to facilitate the detection of students’ facial expressions during lectures. Furthermore, to accommodate low-resolution images, the model integrates an advanced DRN-F face super-resolution reconstruction algorithm, thereby augmenting the accuracy of face recognition. This paper introduces a composite model for assessing the effectiveness of smart classroom teaching by amalgamating the two refined models. Comparative analysis with traditional manual recognition methods reveals that our model aligns more closely with the actual attentive responses of students when their faces are unobscured. A practical study on the teaching application further substantiates the efficacy of the smart classroom approach. Employing an independent sample t-test, the findings demonstrate a statistically significant difference in the post-test scores between the experimental and control groups, with a Sig. Value of 0.001 (p<0.05). This indicates that the learning achievements of students under the smart classroom model are significantly enhanced compared to those in conventional teaching settings.
A Security Assessment Strategy for Corporate Financial Systems Based on Data Mining Techniques
Authors: Wu Jianhong
Published: July 2024

The security assessment of corporate financial systems has become a popular area of research, although few results have been published so far. This research focuses on integrating the association rule algorithm with Bayesian networks to evaluate the security of corporate financial systems. It primarily employs Bayesian networks to calculate the conditional probability, prior joint probability, and posterior probability of each attribute node in the IDRI-Tree to assess the security of the company’s financial system. Additionally, abnormal data from Company A’s financial system were collected to evaluate the method’s effectiveness. The results indicate that Company A’s financial system has data discrepancy compliance rates of 98.92% and 96.50%, respectively. The security assessment scores for monitoring, evaluation, and assessment in financial system security are the lowest, averaging 1.41, while the scores for payment, service, and support are the highest, averaging 3.32. The financial system security assessment method proposed in this paper demonstrates high practical value and offers a reference for the future establishment and improvement of financial system security assessment methods.
Research on Artificial Intelligence and Piano Playing Technology in Colleges and Universities Based on 5g Big Data Networks
Authors: Wang Yibing
Published: July 2024

By analyzing the importance of fingering in piano performance and the challenges posed by existing techniques, this study develops a 5G and AI-based piano fingering automation system. The system leverages deep learning and big data analysis to generate efficient and scientific fingering suggestions. Experiments demonstrate that the system can recommend correct fingering in 98% of cases, achieving 85% consistency with the fingering of professional pianists. The system effectively improves the efficiency and accuracy of students’ learning. After using the system for one week, 25 students experienced a 30% improvement in their playing skills and a 40% reduction in their error rate. The results indicate that the system holds significant potential for application in piano education at colleges and universities, effectively assisting in both learning and teaching.
Exploration of Agricultural Product Packaging Design Innovation in the Context of Deep Learning
Authors: Hu Songyong
Published: July 2024

Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the current era. This study proposes an automatic generation design for agricultural product packaging based on deep learning theory, grounded in the principles of packaging design and market value. To represent the spatial relationship and distribution of color pixels, color moments and color correlation maps are used after obtaining color features of agricultural products through the HSV color space. The grayscale covariance matrix method is employed to get the texture features necessary for the packaging design. Proportion, boundary, pairing, white space, and balance calculations are combined to achieve the layout and typography of feature elements in the agricultural packaging design. The impact of deep learning technology-supported packaging design is analyzed. Data show that in the first group, the average values of the sample group (P001, P002, P004) are 2.98, 3.08, and 2.93, respectively, with an overall average of 2.997, while the overall average of the intelligent group (P003) is 2.96. Overall, deep learning technology-assisted image generation has a positive impact on agricultural product packaging design, contributing to a new level of economic development of farming products.
Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China
Authors: Li Hongbo , Zhou Zhenyong , Du Wei , Lu Lili , Zhang Yang
Published: July 2024

With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher requirements for its quality. In this context, molecular breeding technology for beef cattle has gradually become a research hotspot. This paper designs the entire process of knowledge mapping for molecular breeding technology of beef cattle in China, focusing on the molecular breeding knowledge extraction model based on BERT. It also integrates models and algorithms used in knowledge alignment, complementation, and storage to construct the knowledge graph. After testing the performance of the knowledge extraction algorithm, the development history of molecular breeding technology for beef cattle in China was analyzed. The results show that from 2013 to 2023, research related to molecular breeding technology for beef cattle in China exhibited a slow, then rapid growth trend. The terms “molecular breeding” and “beef cattle” appeared most frequently, with 218 and 165 occurrences, respectively, and had the highest centrality. Keywords with high occurrences include “comparative genes” (11.3068), “functional genes” (9.2376), “genome-wide association” (8.3197), and “genome-wide selection” (6.9012), all closely related to the development of molecular breeding technology for beef cattle in China. The prediction of future trends in molecular breeding technology for beef cattle in China is significantly influenced by this study.
Discussion on the Deep Integration of College Students’ Innovation and Entrepreneurship and College Students’ Civic and Political Education under the Background of Data Technology
Authors: Zhao Panpan
Published: July 2024

Currently, most of the studies related to the integration of innovation and entrepreneurship education into the ideological and political work of college students are theoretical discussions, lacking empirical evidence and rarely quantitative analysis. Based on data technology, this paper explores the deep integration of innovation and entrepreneurship with college students’ ideological and political education. After mining the significance of the integration of innovation and entrepreneurship and college students’ civic and political education, the study utilizes the Bayesian theory and face recognition model to assess the effect of the deep integration of civic innovation and entrepreneurship education from the perspective of emotional change. Its algorithmic model, which is mainly based on classification criteria, recognizes students’ classroom expressions according to the classroom expression classification criteria and the emotion score calculation formula so as to judge the effect of students’ classroom listening. Through empirical analysis, all dimensions of ideological and political education self-efficacy and entrepreneurial values of innovation and entrepreneurship are significantly positively correlated at the level of 0.01. The average value of emotion scores of innovation and entrepreneurship education classes integrating ideological and political education is higher than that of traditional innovation and entrepreneurship classes by 0.04 and by 4 more students. The innovative entrepreneurship classroom integrated with Civic and Political Education has achieved good teaching results among college students.
Exploring the Development Status and Stylistic Characteristics of National Wave Art Based on the Background of Cultural and Creative Economy
Authors: Wang Yan
Published: July 2024

In recent years, the national trend has become increasingly popular, and the art of national trend is the beginning of the production field’s skillful utilization of national art, dedication to category innovation, and making the product have a “root” and a “soul”. This paper combines the sparse principal component analysis method and the entropy weight TOPSIS comprehensive evaluation method to analyze the development level of national tide art in the context of the cultural and creative economy. At the same time, the VGG16 deep learning network is used to extract the depth features of style features in the images of Guochao art-related products, and the Softmax function is used to recognize and classify the features of style features of Guochao art to get the main style features of Guochao art industry. The study shows that the added value of Shaanxi Province’s Guochao art industry accounts for 4.14% of the province’s total GDP by 2023. The cultural, leisure, and entertainment service industry is developing rapidly, providing impetus for the development of the national tide art industry. Meanwhile, it is found that the style characteristics of Shaanxi Province’s national tide art industry appear to change with the development of the sector, and the Shanghai Fine Arts Factory style dominates the style characteristics by 2023 (0.359). This paper sets the foundation for the growth of the Guochao art industry and provides a useful reference for identifying style characteristics in the development of Guochao art.
Research on the Integration of Cultivation of Craftsmanship in Vocational Education and Online Platforms in the Context of the New Era
Authors: Wen Furong , Xu Tian , Dang Xuezhen
Published: July 2024

The rapid ascent of online learning has positioned it as a preeminent method of instruction. This rise prompts a critical examination of how online education can be seamlessly integrated with the development of craftsmanship skills, a pressing issue within the educational realm. This study addresses the challenge by proposing a novel learning analytics model tailored for vocational education. The model, which emphasizes process orientation, continual enhancement, and interactive engagement, is strategically applied to three key areas: identification of learning characteristics, analysis of learning behaviors, and scrutiny of online interactions. Such applications aim to bolster data-driven decisions in nurturing craftsmanship within vocational settings. Further, the research adopts the “Learning by Doing” approach to probe the inherent elements necessary for the endogenization of learning on vocational education online platforms with a focus on craftsmanship. The empirical findings reveal that the integration of online learning with variables such as vocational beliefs, virtues, attitudes, and aspirations—quantified by Beta values of 0.185, 0.142, 0.207, and 0.394, respectively— exerts a uniformly positive influence. These results underscore the efficacy of online vocational education in fostering craftsmanship, thereby enhancing the quality of talent development in vocational disciplines.
Research on Collaborative Educational Mechanism of School-Enterprise Cooperation in Higher Vocational Colleges and Universities Based on Deep Learning
Authors: Chen Fei
Published: July 2024

Although China’s higher vocational colleges and universities have achieved substantial results in school-enterprise cooperation, numerous problems have surfaced in practice. This paper analyzes the influence of strategic synergy, organizational synergy, knowledge synergy, and synergy effects within the context of school-enterprise cooperation. It also examines the role of equivalence matching factors in enhancing these synergy effects. Utilizing relevant data on school-enterprise cooperation, a synergistic education model was developed using a multilayer linear model to capture the interaction of latent variables. Subsequently, a performance measurement analysis was conducted to verify the scientific validity and effectiveness of collaborative efforts in higher vocational education. The results indicate that the synergistic performance in higher vocational institutions engaged in school-enterprise cooperation is 0.655, representing an increase of 0.107 compared to institutions without such collaboration. Implementing school-enterprise cooperation significantly enhances the synergistic performance of higher vocational institutions, thereby maximizing the benefits of this collaboration.
A Study of the Application of Semiotics in News Communication Mechanisms in the Digital Era
Authors: Chen Qianli
Published: July 2024

Any news fact is reproduced as news representation in the form of symbols. In this paper, we first analyze the role of symbols in news representation and propose the common components of news symbols such as subject-object, scene, light color, etc. Construct a model for news symbol analysis that is based on clustering and design a method for segmenting news content based on rules and statistics. The symbolic representation is simulated, and the symbolic ideographic similarity is evaluated after the news content space is transformed into the symbolic representation space, and K-means clustering is carried out. The model was used to analyze the symbolic forms of news from two representative news websites and then calculate the 60 best multi-symbol combinations of “Chinese New Year Festival” news content. A questionnaire survey was conducted to test the degree of audience recognition of the symbolic representations of the news imagery matching. The study found that CCTV's “Chinese New Year Festival” news reports have clear thematic representations, rich symbolic imagery, and are easy to understand, with evaluation scores of 4.61, 4.27, 4.16, and 4.08 for color, character expressions, festive atmosphere, and cultural symbols, respectively. The purpose of this study is to explore and innovate ideas while evaluating the application of semiotics in news communication in the digital age.
Analysis and research on China’s carbon trading market and transaction prices based on signal decomposition model and deep learning model
Authors: Wang Yilin
Published: July 2024

With the continuous development of industrialized society, carbon emissions have become a significant global challenge. Carbon trading, as a crucial measure to mitigate carbon emissions, has garnered substantial attention in the context of market prediction analysis. Addressing the nonlinear and nonstationary nature of carbon trading prices, this study proposes a novel prediction model based on signal decomposition and deep learning. A GUR neural network model, integrated with an attention mechanism, is constructed within a deep learning framework. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to address the issue of non-smooth and nonlinear panel data, further enhanced by the Symbiotic Organism Search (SOA) algorithm. This approach culminates in an advanced price prediction model for China’s carbon trading market. Analysis of relevant data from 2014 to 2022 reveals several fluctuations in carbon trading prices, with transaction prices peaking at 68 yuan. The proposed method demonstrates superior performance metrics, with RMSE, MAE, and MAPE values of 0.512, 0.395, and 1.108%, respectively, outperforming other methods. This study offers an effective approach for predicting carbon trading market prices, providing valuable insights for optimizing and managing carbon market trading and development.
Intellectual Property Protection Issues and Solutions in Cross-Border Big Data Streams
Authors: Zhang Tianyu
Published: July 2024

This paper utilizes the hierarchical analysis method to create an intellectual property protection index model to measure the level and hierarchy of intellectual property protection from the judicial, administrative, and social perspectives and proposes solutions accordingly. From the standpoint of time series prediction, the deep learning theory time series is applied to fully explore the time series characteristics of the flow and a comprehensive assessment of the development status of online intellectual property protection from 2019-2024 is made. According to the findings, pornographic content is the most serious IPR infringement problem in cross-border big data flows, accounting for approximately 38% of all violations. Cases concerning domain name infringement show a gradual upward trend from 2014-2023. The automatic fulfillment index in the judicial protection index is the lowest, at 2.3, and it is important to strengthen this aspect in the future. The overall index of 75.6 in 2019 to 90.1 in 2023 indicates that the trend of intellectual property protection in China is generally positive.
The Interplay of Chinese Monetary Policy, the Offshore Renminbi Market, and Capital Flows: An Empirical Analysis via State Space Models
Authors: Zhang Weiran , Zhang Jiahui , Zhang Hongyang
Published: July 2024

This paper proposes state space modeling and Kalman filtering as the theoretical foundation to explore the impact of China’s monetary policy on the offshore RMB market and capital flows. In the state space model, the relationship between observable and unobservable variables is established, with the Hidden Markov Model representing the state vector as a first-order Markov process to estimate unobservable variables. Additionally, a variable parameter state space model is proposed to alter the relationship between explanatory variables and the dependent variable. Kalman filtering, as the core of the state space model solution algorithm, calculates the likelihood function through prediction error decomposition to estimate unknown parameters. The optimal lag of the model is determined to be 2 after constructing the VAR model in the empirical analysis, and the first-order autoregressive model is used to calculate variable parameters. The elasticity coefficient of the depth of development of the offshore RMB market on capital flows is the most significant variable, with a value of 0.96. Following this, the RMB exchange rate has an elasticity coefficient of 0.78.
Research on Corpus-based Graphic Analysis of Ceramic Science and Technology Texts and Its Application in Multimodal English Teaching
Authors: Jing Wang
Published: July 2024

With the continuous development of English education, the use of multimodal teaching methods in English instruction has gradually increased. This paper analyzes the characteristics of multimodal discourse and explores the principles of multimodal selection in English teaching. On this basis, a corpus-based graphic analysis framework for ceramic science and technology is constructed. A graphic matching model for ceramic science and technology text is established by combining text feature extraction and graphic feature extraction. The model is optimized using word vector distance and angular similarity measure algorithms, and an English-Chinese bilingual corpus is constructed based on web crawling. Empirical analysis shows that the metrics in text matching in the FK dataset have improved by 0.10% to 2.70% compared to CAAN. Image matching has been enhanced by 1.28% to 2.19%. The average value for both MS and FKMS datasets is 75.85%. Textbook A contains 582 more images with equal relationships than Textbook B and 155 fewer images with unequal relationships. This demonstrates that Textbook A provides a better way for teachers to guide students’ interpretation in multimodal English teaching. This study enhances English teaching and student skill development and also verifies the effectiveness of the graphic analysis method applied in English teaching.
The relationship between Cooperative Education and Occupational Aspiration of physical education students in the context of Artificial intelligence: the chain mediating role of employability and self-efficacy
Authors: Zhang Hui , Zhou Yi , Zhang Yuzhong
Published: July 2024

Under the new situation, the state vigorously supports physical education, and the cooperation between industry, academia, and research is increasingly showing an important role in physical education and students’ careers. In this paper, under the background of artificial intelligence, combined with the “government, industry, academia, research and utilization” cultivation mode, we constructed an industry-academia-research cooperative education model of physical education based on a cloud platform. Based on the edge structure model, a mediation effect test model was designed to verify the interlocking mediating roles of employability and self-efficacy in the relationship between cooperative education and physical education students’ career aspirations. Correlation analysis showed that there was a significant correlation (0.816) between physical education students’ acceptance of industry-academia-research cooperative education and career aspirations, and the correlation between cooperative education and employability and self-efficacy was strong. The mediation effect test found that both employability and self-efficacy had a significant mediating effect between cooperative education and career aspirations (P<0.05), and there was a chain mediating effect between the two. The application of the industry-university-research cooperative education model in this paper can promote the formation of good career aspirations among physical education students and provide a new direction for their future employment.
Exploring the Construction of BTI Translation Technology Course in the Context of New Liberal Arts
Authors: Chen Jun
Published: July 2024

Translation technology education represents a critical yet underdeveloped aspect in the training of Bachelor of Translation and Interpreting (BTI) students in higher education institutions. This paper delves into the implications of the emergent liberal arts paradigms on BTI education. It addresses the challenges inherent in existing BTI translation technology courses by proposing a novel instructional system rooted in machine learning. Specifically, the system utilizes a deep learning Sequence-to-Sequence (Seq2Seq) model to facilitate the transformation of source language text sequences into target language equivalents. Furthermore, the system integrates collaborative filtering to tailor content recommendations, thereby achieving functionalities such as real-time translation, personalized content suggestion, and adaptive learning. Subsequent to the system’s validation, the translation technology curriculum was restructured, and its efficacy was evaluated through a controlled pedagogical trial spanning one academic year. Performance metrics from the system’s deployment indicate robust outcomes in both development and test environments, with Bilingual Evaluation Understudy (BLEU) scores of 26.79, 26.34, 24.15, and 24.52, respectively—each surpassing competing models by a minimum margin of 2.5 points. The pedagogical trial further evidenced substantial academic gains; the average score of students in the experimental cohort rose from 12.79 to 17.44, marking an increase of 4.65 points. These enhancements substantiate the efficacy of both the bespoke BTI instructional translation system and the redesigned course framework. This investigation furnishes valuable insights and viable strategies for the amalgamation of information technology with BTI education, as well as for the innovation of translation technology curricula. It contributes to the enhancement of the BTI curriculum architecture within universities and promotes the evolution of translation technology pedagogy.
Application of electrical automation technology in factory production of edible fungi
Authors: Guan Jian , Yan Hui , Wu Qingshan , Lu Jianwei , Liu Xuguo , Shang Wenchao
Published: July 2024

With the gradual increase in the degree of factory production of edible fungi, automated control of the growth environment of edible fungi is particularly important. This paper utilizes the advantages of electrical automation technology to monitor the growth environment of edible fungi and regulate the environmental parameters of edible fungi factory production. Combined with the working principle of automation control systems, the fuzzy PID control algorithm is used to optimize the automatic control of edible mushroom factory production. By controlling and regulating the temperature and humidity of the growth environment of edible fungi, you can differentiate the automatic growth environment of edible fungi. Using fuzzy PID control technology to regulate the environmental conditions required for the growth and development process of PU Mushroom 1, we compared the seasonal mushroom production of PU Mushroom 1 with the mushroom production after applying the control system of the edible fungus growth environment. We compared the growth of the control area and the application area in terms of the rate of infection by stray bacteria at the mycelium stage of the almond abalone mushroom. The indoor temperature profile of the edible mushroom production environment controlled by fuzzy PID changed gradually, and the difference in temperature between day and night was reduced by 4.41°C compared to that outside. Regulating the opening of the greenhouse roller shutter prevented the indoor humidity from rising rapidly with the increase of outdoor moisture, which played a good role in humidity reduction. After applying the edible mushroom growth environment control system, the average yield of fresh mushrooms was 322.377g/bag, which was 33.7% higher than that of seasonal mushroom production. It shows that the application of automatic control technology for edible mushroom factory production can accurately assess and regulate the temperature, humidity, and other environmental conditions during the growth and development of edible mushrooms, which can promote the production capacity of edible mushrooms.
Optimization strategy of mixed-integer linear planning in logistics distribution
Authors: Li Yuhua
Published: July 2024

The evolution of the logistics and distribution industry, notably the express delivery sector, has significantly increased its prevalence in everyday life. This escalation necessitates an ongoing innovation in logistics strategies and enhancements in service quality, positioning these elements at the heart of industry focus. This study initially addresses the vehicular route planning issue within logistics distribution, selecting Company A as a case study to examine inherent logistical challenges. Subsequently, it develops an optimal route planning model under time constraints. It is converted into a mixed-integer linear programming model through techniques such as variable substitution and segmented linear approximation. This conversion facilitates rapid solutions by mathematical planning solvers. The research contrasts the logistics and distribution performance before and after optimization to assess the efficacy of the proposed strategies. Findings indicate that optimized vehicular route planning achieves a more equitable distribution of delivery tasks, along with substantial improvements in the complexity of transportation routes and the reduction of travel distances. Specifically, the optimization results in a significant decrease in the penalty costs associated with the vehicles, with the penalty cost for the third vehicle involved in the study reduced by 66.40%. This evidence supports the implementation of scientific vehicle scheduling strategies by logistics firms, underscoring the model’s practical implications.
Application and effect evaluation of computer simulation technology in ideological and political education
Authors: Sun Guoyang
Published: July 2024

The application of computer simulation technology can help students understand and learn ideological and political education in depth. This paper puts forward the development path of Civic and Political Education on the basis of computer simulation technology. It correspondingly constructs a computer simulation technology framework centered on students’ digital portraits and virtual scene technology. Sentiment analysis algorithm based on the BERT model and improved FastText text classification algorithm is used to extract the characteristic indexes of Civic and Political courses and realize the construction of digital portraits of Civic and Political courses and students. By adding a Gaussian curvature operator and implementing an edge-splitting strategy to optimize the narrow triangular surface, grid simplification of the QEM algorithm can be achieved to improve the consumption problem of computer scene resources. An experiment is being conducted on the effects of using computer simulation technology in Civic and Political Education at X High School in G City, Ningxia Hui Autonomous Region, China. Students in the experimental class supported by this technology improved their overall Civics score to 39.7 after the experiment, which was 4.02 higher than that of the control class. The mean values of all dimensions in the Civics knowledge level of the experimental class were higher than those of the control class, except for the dimension of knowledge transfer, which all showed a significant difference (P<0.05). In Civics Teaching Behavior and Ideal Conviction, each index score is greater than 4, indicating good performance in Civics Teaching.
Model analysis of flow-induced vibration (LIV) in submarine oil and gas pipeline
Authors: Qiu Yu , Xia Dongmei , Xie Jinhuang , Zhang Xuanhuan , Wei Dingwen , Li Yan
Published: July 2024

In order to realistically simulate the flow-induced vibration of submarine oil and gas pipelines, this paper assembles a gas-liquid two-phase flow vibration device, develops a detailed and comprehensive experimental procedure, and sets the flow aperture, flow pressure, flow depth, flow direction, and gas-liquid phase apparent flow rate and other variables. According to the different types of pipelines, the multiphase flow-induced vibration of submarine oil and gas pipelines can be categorized into straight pipe vibration and elbow pipe vibration, and combined with the theoretical knowledge of fluid dynamics and the equations of motion to construct a multiphase flow-induced vibration model of submarine oil and gas pipelines and discuss the vibration mechanism of submarine oil and gas pipelines in two-phase liquid flow. Combined with the corresponding experimental parameters, the vibration phenomenon induced by gas-liquid two-phase flow in submarine oil and gas pipelines is experimentally analyzed. It is analyzed that the first-order intrinsic frequency of the submarine oil and gas pipeline is kept at 23.2 Hz when the value of the Reynolds number of the in-pipe flow ranges from 2.53 × 104 to 1.08 × 107. The first-order intrinsic frequency of the submarine oil and gas pipeline monotonically decreases with the increase of the Reynolds number of the in-pipe flow. In addition, when Ul=0.4m/s, the average standard deviation of the data is only 1.114m/s² with the rise of Ug, indicating that the vibration intensity of the fluid on the subsea oil and gas pipeline increases gradually with the increase of the apparent flow velocity of the liquid phase. By analyzing the multiphase flow problem of oil and gas pipelines, this study is of great significance in improving the efficiency of oil and gas transportation.
Physical properties of two-dimensional black phosphorus and its application in optoelectronics
Authors: Yao Yin , You Haipeng , Bai Jianhui
Published: July 2024

Two-dimensional layered materials have gained significant attention in recent years as novel materials. By fabricating devices from these materials and evaluating their electrical and optoelectronic properties, we can preliminarily assess their potential applications in electronics and optoelectronics. This paper analyzes the physical properties of two-dimensional black phosphorus from both electronic and optical perspectives. Optoelectronic devices based on black phosphorus are fabricated, and their applications in basic photonics and bio-photonics are explored. A tunneling acceleration sensor is developed by integrating two-dimensional black phosphorus with a cantilever beam structure. The sensitivity, repeatability, and response time of the sensor are tested through simulation experiments. The experimental results show that the photocurrent of the 2D black phosphorus-based photodetector increases from about 103
A□W
−1 to about 105
A□W
−1 when the measurement temperature decreases from 250K to 25K at a power density of 10mW/cm2. The sensor was subjected to repeated loading and unloading under the application of 200g weights, and the capacitance output fluctuated back and forth between 40 and 450pF and remained essentially unchanged in the steady state, with stable capacitance performance and a fast response time of about 20S.
Research on Text Classification of Power Work Orders Based on RoBERTa-RCNN and Channel Attention Mechanism
Authors: Yu Yifan , Li Jing
Published: July 2024

Given the scarcity of algorithms for classifying power grid work order texts, coupled with challenges such as sparse features, lack of structure, and large data scale, this paper proposes a text classification model based on the Roberta-RCNN framework that incorporates a channel attention mechanism. The model employs the Roberta model for word vectors, masks entities and word semantic units, and links them to the Transformer encoding layer to encode the word embedding vectors output by the Roberta layer. This approach addresses model overfitting and enhances generalization ability. The RCNN model employs a channel attention mechanism to optimize feature extraction, allowing the model to focus more on learning feature representations from various channels, followed by softmax classification. Experimental results on the THUCNews short text dataset demonstrate improved evaluation metrics compared to existing methods. Comparative experiments on a self-compiled power grid work order dataset show that the proposed algorithm model achieves higher accuracy compared to other models. This indicates its effectiveness in extracting feature information from text and addressing the challenges of power grid work order text classification with promising classification performance.