bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building an Applied Linguistics Corpus for Chinese Language Teaching and Learning
Authors: Ma Yanrong 
Published: June 2024

Abstract
A corpus is a large and authentic language collection that can provide a large number of valuable, statistically significant, and retrievable material examples for Chinese language teaching. In this paper, we first design the processing model for participle and deactivation and then construct a statistically based method for labeling Chinese lexical categories. After building the Applied Linguistics Corpus for Chinese language teaching, it is used in Chinese language teaching to analyze conjunctive use bias. A controlled trial was conducted in the international college of a university using the applied linguistics corpus for teaching the Chinese language to test its practical utility. It was found that the top four conjunctions in terms of the total number of conjunctive biases were “and,” “and,” “because,” “or”. The bias of the word “and” mainly appeared in Japanese and Korean students, accounting for 38.69% and 36.07% respectively. The average score of the pre-test of writing in the experimental class was 51.26, while that of the control class was 50.88. The average score of the post-test of writing in the experimental class was 74.95, while that of the control class was 62.64, and the increase of the score of the experimental class was larger than that of the control class, which verifies the great effectiveness of applying applied linguistics corpus in teaching Chinese as a foreign language. This study can help improve students’ writing level and learning initiative in Chinese language teaching and effectively enhance students’ ability to express themselves in Chinese.
Innovative Reform of Blended Teaching in Statistics Practice Class of Economics and Management under OBE Concept Orientation
Authors: Zhang Hui 
Published: June 2024

Abstract
In the traditional teaching mode, the cultivation of students’ practical application ability and innovation ability is neglected, and the innovative reform of blended teaching in statistics practice class of economics and management is imperative. Using theoretical analysis, the feasibility of the OBE education concept to guide blended teaching is analyzed in depth, and it is pointed out that the OBE concept is embodied in the application of blended teaching in statistics practice class of economics and management. From the three aspects of learning output target, teaching content, and evaluation system, the blended teaching of statistics practical class is designed to integrate the OBE concept, and in order to humanize the blended teaching under the OBE concept, the group recommendation algorithm is proposed to assist the blended teaching under the OBE concept. Its principle and structure are elaborated in detail, which mainly include the learning participation degree, the follower effect, the Attractor algorithm, and the similarity degree. The research object and research tools were established, and a controlled experimental comparison was utilized to evaluate the example of blended teaching of statistics practice that infiltrated the OBE concept. The results show that the P-values of the problem-solving ability in each dimension of the two classes are (0.001, 0.005, 0.003, 0.008, 0.008, 0.002) and less than 0.01, respectively, which indicates that there is a significant difference between the students of the two classes in the problem-solving ability in each dimension after the cluster recommendation algorithm assisted by the OBE concept hybrid teaching mode, which confirms that the proposed teaching mode has a promoting effect on students’ performance and ability improvement.
Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation
Authors: Zou Wendong , Guo Ruiwei , Kao Lijun 
Published: June 2024

Abstract
In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling optimization of the e-commerce logistics supply chain is particularly important. Based on the three-layer supply chain scheduling model, the study combines the production cost, transportation cost, and transportation time of each member of the supply chain with other influencing indexes. It establishes a multi-stage supply chain scheduling model (SCISM) with dual-objective. The rotational algorithm in linear programming is used to solve the model after it has been constructed. The effectiveness of the SCISM model for intelligent e-commerce logistics supply chains is explored through algorithmic and arithmetic validation of the SCISM model. The results show that the SCISM model algorithm determines the optimal solution σ∗ = (1,2,4,6,3,5) and the optimal objective generalized function value J(σ∗) = −49328 in only 0.95 seconds, which significantly improves the solution efficiency. Compared to Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO), the SCISM algorithm quickly achieves the global optimal solution with the minimum number of iterations (100).
On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform
Authors: Zhang Min 
Published: June 2024

Abstract
This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word alignment, translation model training, ordering model training, language model training, decoding, and parameter optimization. Special emphasis is placed on the quality of bilingual word alignment, utilizing both the IBM model and the statistical collocation model to enhance alignment accuracy. The study reveals that this new platform significantly enhances translation efficiency across diverse corpora, such as legal documents, news articles, and parliamentary records. Notably, the translation of parliamentary records demonstrates a remarkable reduction in average translation time by 27.53% and a decrease in the number of decoding iterations by 56.46% when compared to System1 using the Baseline system. Furthermore, a BLEU-based evaluation indicates that the Baseline system consistently surpasses the comparison system in translating various text types. This study underscores the practical effectiveness of the developed translation platform and its contribution to the advancement of natural language processing technology.
Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation
Authors: Zhao Demin , Xiong Wei , Shi Jinjing 
Published: June 2024

Abstract
With the popularization and development of communication technology, the resource allocation problem of satellites has become a hot research topic. This study proposes a model for satellite resource allocation through the development of a beam mobilization system model and problem modeling. Then, on the basis of the linear stochastic gradient descent method, the classification accuracy of the algorithm is improved by adjusting the algorithm-solving method. Then its weight assignment method is improved to get the improved weighted linear stochastic gradient descent method. Using the optimized weighted linear stochastic gradient descent method to solve the satellite resource allocation problem from the perspective of the original problem, a model based on IWLSGD is designed and tested for performance. Through simulation experiments, the beam allocation service value of the satellite resource allocation model in this paper is 193, which is 4.32% and 3.21% higher than that of LSGD and WLSGD, respectively, and iterative convergence is faster, and its operation time and service value performs the best under different numbers of communication time slots. The system revenue, system access success rate, and system satisfaction under the interference environment between LEO and GEO are maintained at 15208, 0.8~1.0, and 85%, and keep around 7500, 1, and 75% under 5G base station interference. The satellite resource allocation model in this paper can effectively improve the utilization efficiency of communication resources and better adapt to dynamically changing interference scenarios.
Design and motion analysis of a space crank rocker biomimetic aircraft
Authors: Yan Shuai , Mei Xianhui , Zhou Yongjun , Wang Dewang 
Published: June 2024

Abstract
In order to better understand the flight performance and kinematics performance of flapping wing aircraft designed and developed based on the principle of bionics, a space crank rocker mechanism was tried to carry out the virtual prototype design of the bionic aircraft in space motion, and on this basis, its movement characteristics were analyzed. Analyzing the flight mechanisms and scale laws of common flying organisms yields various physical estimation parameters that affect flight characteristics. Flight patterns and motion patterns are analyzed based on the design principles of seabirds, and structural designs and mathematical models are established. Secondly, carry out the design of its flapping and transmission structures, and based on this, carry out the overall structural design and establish a three-dimensional model. Here, the kinematics analysis of its flapping amplitude and angular velocity value is carried out by combining its mechanism motion diagram, and the kinematics simulation results are compared with the theoretical results, which proves the correctness of the theoretical analysis and the feasibility of the design structure.
Applications and Challenges of Computer Vision in Autonomous Driving
Authors: Liu Jiahao , Ren Peng 
Published: June 2024

Abstract
The rapid evolution of autonomous driving technology has spurred deeper exploration into its multifaceted domains. This paper focuses on enhancing the intelligence and safety of autonomous driving systems through the application of computer vision. Specifically, the integration of EIoU loss and Varifocal Loss functions within the YOLOv5 algorithm facilitates the training of higher-quality samples at reduced costs. This enhanced YOLOv5 algorithm is adept at detecting traffic entities and incorporates the Matrix NMS algorithm to further refine the loss function adjustments. Moreover, this study employs the proximal policy optimization algorithm to construct a vehicular behavioral decision model for autonomous driving. This model utilizes cubic polynomial equations to depict lane-changing trajectories and introduces a safety distance rule to mitigate collision risks during autonomous maneuvers. Comparative analyses reveal that the modified YOLOv5 algorithm surpasses its predecessors—YOLOv3 and YOLOv4—in terms of detection accuracy and processing speed. Notably, the improved YOLOv5 algorithm exhibits lower network loss values than its original version, achieving faster and more stable convergence. In practical applications, the algorithm successfully identified and labeled 20,025 intelligent vehicular targets, with initial testing accuracy reaching 96.12%. This accuracy improved to 96.36% following the application of EIoU adaptive tuning to reduce risk-free class loss, and further fine-tuning elevated the accuracy to 96.54%. The integration of computer vision into autonomous driving technology thus holds significant promise for advancing the field.
Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis
Authors: Xiao Yanling 
Published: June 2024

Abstract
With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.
Design of Basketball Teaching Aid System Based on Artificial Intelligence
Authors: Xu Xiaohu , Wu Haijuan 
Published: June 2024

Abstract
Artificial intelligence has penetrated every aspect of people’s lives with the rapid development of science and technology. In basketball, how to effectively utilize artificial intelligence technology to improve the effectiveness of training has become a problem worth studying. A Mediapipe-based 3D human posture domain adaptive detection method is proposed in the study by combining statistics and human limb proportion simulation. The modified SVM classification algorithm is used to compare and classify the newly acquired key point data information of the system with the standard basketball action key point data information preset by the system to judge whether the basketball action is up to the standard or not, so as to design the basketball teaching aid system based on artificial intelligence. Based on this basis, after testing the recognition performance of the system, its practical application effect is explored. The total basketball scores improved after the experiment (86.26±2.228 for the experimental group and 81.34±9.672 for the control group), but there was no significant difference (P>0.05). The control group was slower than the experimental group by 0.64 seconds and 0.77 seconds overall in the cross-step breakout time with the ball and the same-side step breakout time with the ball, respectively, indicating that the experimental group was more proficient in basketball technology. The system has been tested in various ways to meet the multiple needs of the users, meet the expected standards, and be able to be applied in real-world scenarios.
Analysis of assembly building quality influencing factors based on deep confidence network
Authors: Chen Jin 
Published: June 2024

Abstract
At present, in the assembly building construction practice, the relevant subjects lack the concept of building quality management and awareness of responsibility, and the rights and duties of the subjects of each link are not clear in the division of responsibility for building quality. In this paper, based on the deep confidence network, for the problem that the accuracy of the traditional DBN model will gradually decrease, a genetic algorithm is introduced to optimize the conventional restricted Morzmann machine, and the number of nodes in the hidden layer of the genetic algorithm optimization DBN node number process is obtained after the improvement. The optimal method for planning building quality assessment is selected based on the comparison results of the established DBN algorithm function. Then, the optimal building quality assessment model is constructed. Then design the evaluation index system for quality influencing factors and verify it with structural equations. Finally, the model is used to quantify the degree of influence of assembly building quality. The study concludes that all path coefficients affecting the quality of assembled buildings are greater than 0.5, the P-value is less than 0.001, and the five proposed hypotheses are all valid. In the assessment of the quality of residential projects, the final results of excellent, good, moderate, and qualified accounted for 0.1952, 0.2299, 0.3086, and 0.2663, respectively, and the quality of the project’s construction was evaluated as a good grade. This study provides a new method for improving the awareness of quality responsibility among relevant subjects in the construction industry and guaranteeing the level of building quality.
Research on Automatic Business English Text Translation Technology Based on Intelligent Computing
Authors: Gao Honggang 
Published: June 2024

Abstract
As global interactions and trade expand, the relevance of Business English has similarly increased, enhancing the scope of its application. Consequently, there is a growing interest in the research of automated translation technologies for Business English texts. This paper provides an overview of the translation requirements for Business English texts, taking into account their distinct characteristics. We propose a novel neural network translation model that integrates an attention mechanism within the sequence-to-sequence (seq2seq) framework. This model incorporates Bayesian optimization to exploit the strengths and features of intelligent algorithms effectively. Furthermore, the model is enhanced by Gaussian regression and an acquisition function, enabling it to simultaneously search for optimal architectural configurations. Experimental analyses demonstrate that the model’s accuracy improves with increased training iterations. In comparative tests across various datasets, the seq2seq translation model augmented with Bayesian optimization and the attention mechanism achieved the highest BLEU scores. Specifically, it exhibited an average improvement of 1.1 points over models using Bayesian optimization without the attention mechanism, indicating a substantial enhancement in translation accuracy. Practical applications show consistent evaluation results across different datasets, with negative evaluations for text translations remaining below 10%. The findings underscore the high quality of the translations produced by our model for Business English texts, affirming its effectiveness and applicability in professional settings.
Research on Informatization Construction of Educational Management Innovation Path in Colleges and Universities in the Intelligent Era
Authors: Li Yongxia , Ding Yanyun 
Published: June 2024

Abstract
The prevailing education management systems in colleges and universities are antiquated, adversely impacting not only the administrative efficiency but also the formulation of faculty teaching plans and class schedules, as well as the management of student electives and credit accrual. This paper presents an innovation and optimization of the existing system, wherein education management processes are categorized into seven distinct modules. It delineates the flow and integration of heterogeneous data sources within the system, leveraging heterogeneous database technology as the foundational framework. Subsequently, we redesign heterogeneous computation scheduling and adaptation, enhancing distributed database allocation and query algorithms. The culmination of this research is the development of a novel pathway for the education management information system in higher education institutions. Upon empirical testing of the system proposed in this paper, we explore its practical implications through IPA analysis. User satisfaction is notably high in areas such as A4 invigilation information, A13 subjective grading of routine assessments, A14 querying and feedback on grades, and A15 remediation of failed courses, with performance ratings of 4.77008, 4.73981, 4.8961, and 4.86558 respectively. These indices predominantly reside in the “continue to maintain” and “oversupply” zones, indicating the substantial efficacy of the innovative college management information system introduced herein. This study significantly contributes to the standardization and intellectualization of college administrative processes, proposing new paradigms and methodologies for the integration of educational management and information technology within tertiary institutions.
Study on the Distribution Patterns and Treatment Effectiveness of Water Body Pollution Monitoring by Remote Sensing Technology
Authors: Lin Zhenjing , Jin Nisha , Dong Yarong , Chen Jing , Wang Lidong , Li Zhiwei 
Published: June 2024

Abstract
With the continuous development of science and technology level, remote sensing technology has a wide range of application prospects in water pollution monitoring. The study combines the Grab-Cut image segmentation algorithm, atmospheric radiation correction, and water pollution identification to construct a remote sensing technology-based method for monitoring water pollution. The research is focused on analyzing the water quality distribution pattern of the upper and lower lakes in the sample lake area using the constructed water pollution monitoring method. On this basis, from the four dimensions of water pollution prevention and control, recycled water recycling, ecological restoration and protection, and environmental monitoring, the comprehensive management measures for water pollution are proposed. The pollutant contents of the upper and lower lakes before and after the management are compared to explore the effect of the comprehensive management of water pollution. The results show that COD contributes 86.77% to the water pollution in the sample lake area. The water quality of the upper and lower lakes is more distributed by class V and class III, which account for 36.67% and 43.33%, respectively. The proposed water pollution monitoring method is able to accurately identify and classify the pollution. After the comprehensive treatment, the COD and ammonia nitrogen content of the upper lake decreased by 30.51% and 37.43%, and that of the lower lake decreased by 35.90% and 39.06%. The effect of water pollution treatment was remarkable.
Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling
Authors: Liu Yan 
Published: June 2024

Abstract
This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.