bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Integration of Curriculum Civics and Politics Construction into the High-Quality Talent Cultivation System for Rural Revitalization in the Internet Era
Authors: Shi Ronglian , Shi Jianlin , Zhang Ruo 
Published: March 2024

Abstract
In promoting rural revitalization strategy, the construction of curriculum Civics and politics is closely related to cultivating high-quality talents. Facing complex and changing social demands, the education system urgently needs to integrate civic and political education with the teaching of professional knowledge to optimize the talent cultivation process. This study analyzes the degree of matching between students’ course selection behavior and rural revitalization practice through data mining techniques. It proposes a curriculum optimization plan to strengthen the connection between the curriculum Civics and politics content and the actual needs of rural revitalization to improve the quality and efficiency of education. The study uses the particle swarm algorithm and the improved tree path matching algorithm to optimize the curriculum by analyzing the student course selection data and the rural revitalization practice data and mining the association rules between them. Through the improved data mining method, the match between students’ course selection preferences and rural revitalization needs can be effectively identified, in which the degree of integration of course civics and politics is increased by 23%, and students’ participation in rural revitalization practice activities is increased by 15%. The hierarchical clustering algorithm-based talent cultivation model is more suitable for rural revitalization. This study shows that optimizing curriculum ideology and politics in the Internet era, combined with data mining technology, can effectively promote the high-quality development of rural revitalization talent cultivation systems.
Research on Innovative Models of Second Language Teaching in the Age of Artificial Intelligence
Authors: Chen Lin 
Published: March 2024

Abstract
This study explores advanced models for second language instruction within the artificial intelligence landscape, spotlighting the integration of mixed quantile regression and Bayesian inference to refine teaching strategies and bolster learning achievements. By adopting mixed quantile regression, this research constructs a model that surpasses traditional assumptions of normality, enabling the handling of complex, multilevel heterogeneous data. Bayesian inference was applied for parameter estimation, enhancing the precision and reliability of our findings. An empirical investigation involving 658 students from College M revealed an average adaptability score in second language learning of 3.663, with all dimensions scoring above 3—learning attitude ranking highest at 3.956. Key factors influencing learning capacity, including motivation, intellectual literacy, self-efficacy, and the availability of resources, demonstrated a positive correlation. These findings suggest the potential of mixed quantile regression and Bayesian inference in assessing learning adaptability and determinants, offering a novel approach to AI-supported second language education.
The effect of functional fitness training on the balance ability of students specializing in the high jump
Authors: Chen Xianbiao , Ding Laijian 
Published: March 2024

Abstract
This study delves into how targeted exercise regimes can bolster static and dynamic balancing capabilities by exploring the nexus between functional physical training and balance proficiency in high jump athletes. Through rigorous evaluation of 80 students using the entropy weight-TOPSIS method, our four-month investigation revealed marked enhancements in balance. Notably, static balance improved by 8-10%, while dynamic balance saw a 12-15% uptick, emphasizing the efficacy of functional training in optimizing athletic performance and mitigating injury risks. The control group’s unchanged balance further underscores the specific benefits of our training regimen.
Research on English Reading Comprehension Strategies Based on Natural Language Processing
Authors: Li Dan 
Published: March 2024

Abstract
This study investigates strategies based on natural language processing to improve the efficiency and effectiveness of English reading comprehension by analyzing and optimizing reading comprehension models. We introduce the main framework of the English reading comprehension task, including the word embedding representation layer, the context encoding layer, the information interaction layer, and the output layer. The article utilizes the Word2vec model and decision tree ID3 algorithm to enhance the efficiency and accuracy of reading comprehension. The research data comprised 800 English articles, with 60% used for training and 40% used for testing. The articles were graded for difficulty and resources were pushed. It was found that the model proposed in this study performs better in difficulty grading (with a rating range of 0.797-0.926) and is more efficient in terms of time overhead (feature extraction time of about 10 milliseconds, and grading time of 1.7-2.6 seconds), compared to the traditional models such as BiLSTM and RNN. The pushed resources significantly correlate with learners’ self-assessed difficulty and actual performance. The NLP-based strategy proposed in the paper shows significant advantages in both efficiency and accuracy, and helps to improve English learners’ reading comprehension.
The application of 5W2H management model in the construction of enterprise economic and strategic management system
Authors: Luo Lei , Hong Qingyu , Zhong Ming 
Published: March 2024

Abstract
The 5W2H management model, rooted in World War II, stands out for its simplicity and effectiveness in enterprise management, offering considerable advantages in strategy planning and execution. This study focuses on the model’s role in enhancing strategic economic management within businesses, combining qualitative and quantitative analyses of various enterprises’ strategic management frameworks. Implementing the 5W2H model led to notable enhancements in strategic definition, implementation efficiency, and tangible achievements. Specifically, businesses observed a 20% improvement in achieving strategic objectives, a 15% faster strategy implementation, and a 25% increase in strategic engagement among employees. Additionally, the model proved instrumental in improving internal communication, fostering better teamwork, and optimizing the allocation of resources. Our findings affirm the significant value of the 5W2H model in refining corporate strategy formulation and execution, highlighting its essential place in modern enterprise management practices.
Functional Analysis of psychological healing based on 3D omni-directional scanning method to simulate the design of natural textured fabrics
Authors: Xu Xiangge , Wang Qijun 
Published: March 2024

Abstract
This paper is centered on applying 3D omnidirectional scanning technology to design natural textured fabrics to explore its functionality in psychological healing. The therapeutic potential of fabric art has been widely recognized due to the prevalence of psychological stress in modern society. This study uses 3D omnidirectional scanning technology to scan knitted textures, optimize fabric texture models, and conduct experimental research and material development using elevation and lighting angles. The size, Angle, color, and other factors that affect the experiment of knitted materials were tested, and eight convenient materials were selected for psychological experiments. A two-week experiment was conducted to record and analyze changes in the psychological state of 30 subjects (15 men and 15 women). It was obtained that the intervention group using simulated fabrics decreased from an average of 38.14 to 27.25 in the level of mental awareness, which was significantly higher than the control group (p<0.01). In terms of mental health indicators, the intervention group showed significant improvement in depression, anxiety, and other dimensions. Using 3D omnidirectional scanning technology to simulate natural texture fabric design positively enhances mental awareness and improves mental health, which has a promising potential for psychological healing.
Application of Algorithm Visualization Techniques in Teaching Computer Data Structure Course
Authors: Zhang Yan 
Published: March 2024

Abstract
With the current development of computer science education, traditional teaching methods can no longer meet students’ needs for in-depth understanding of complex data structures and algorithmic concepts. Using algorithm visualization technology, an emerging teaching aid, dramatically enhances the intuitiveness and interactivity of teaching by presenting abstract data structures and algorithmic processes graphically. Effectively integrating algorithm visualization technology into teaching computer data structure courses to improve teaching effectiveness and students’ learning efficiency remains a significant challenge in today’s education field. The experimental control method in the article compares and analyzes the differences between the teaching practice integrating algorithmic visualization techniques and the traditional teaching methods in improving students’ computer thinking ability, academic performance, satisfaction, and learning effectiveness. After one semester of teaching experiment, the average improvement of computer thinking dimensions such as abstraction ability and decomposition ability of the experimental group is significantly higher than that of the control group, in which the abstraction ability is improved by 21.53% and the decomposition ability is improved by 0.61 compared with that of the control group. In terms of learning achievement, the average achievement of the experimental group is 18.67% higher than that of the control group, and the algorithmic visualization technology significantly enhances learning efficiency and achievement. The algorithmic visualization technology has a significant effect on improving learning efficiency and achievement. Students in the experimental group rated higher than the control group in all dimensions of teaching satisfaction and learning effectiveness, especially in teaching intuition. It can be seen that algorithm visualization technology can effectively improve the teaching effectiveness of computer data structure courses, and enhance students’ learning interest and efficiency.
The Application of Big Data Technology in Teaching College Students’ Mental Health Education in Colleges and Universities
Authors: Tan Jin , Mao Yanli , Li Yaxiong 
Published: March 2024

Abstract
Advances in big data technology herald a new era for mental health education in higher education, offering novel solutions to age-old challenges in student psychological care. This paper investigates how big data’s analytical capabilities can surpass traditional, one-size-fits-all mental health assessments by leveraging detailed student data for personalized care. We applied data mining techniques to the mental health data of 1,200 students from College G, creating a rich database of psychological patterns and a mental health information exchange platform. The analysis led to the identification of 500 instances of psychological distress with an accuracy of 79.5% and unveiled patterns linking academic stress and adjustment difficulties to mental health issues. Our research underscores big data’s role in enhancing mental health interventions, providing the groundwork for more individualized and effective mental health services in academic settings.
Research on the Reform Path of Curriculum Civics Integrating into Online Learning Teaching and Learning for Law Majors in the Era of Artificial Intelligence
Authors: Wan Hui 
Published: March 2024

Abstract
With the rapid development of information technology, online learning of legal specialty in the era of artificial intelligence has become a significant trend. This study explores the pedagogical reform path of integrating course ideology into online learning for law majors. Multiple linear regression models and Apriori association rule algorithms were used to analyze the learning effects of 457 law students. The integration of the course Civics and Politics significantly impacted students’ learning outcomes, especially showing significant differences among students of different genders and grades. Different genders showed significant differences (p<0.05) in course teaching integration and faculty. Significant differences were also observed among students of various grades regarding integration of course materials, teaching, and teacher team integration. The Pearson product-difference correlation analysis of the variables of course civics integration into law teaching found that talent cultivation goal integration, course textbook integration, course teaching integration, and teacher team had significant positive correlations with learning outcomes. These findings were further validated by the association rules mined by the Apriori algorithm. The student learning outcomes are more likely to achieve excellence when the scores for course material integration and teaching integration are high. This study provides an effective pedagogical reform path for integrating civics into online learning for law majors, which significantly enhances students’ learning effects.
Big Data Promotes Social Governance Toward “Social Intelligence”--The Case of Community Corrections
Authors: Xie Manna 
Published: March 2024

Abstract
This paper discusses the application of big data in community corrections and analyzes its contribution to improving the effectiveness of governance, focusing on enhancing the science and effectiveness of community corrections strategies. Using a Random Forest Model and Deep Reinforcement Learning Network, the dangerousness of community correctional officers is analyzed and corresponding strategies are proposed. The random forest model achieved 99.92% accuracy in predicting community correctional officers’ delinquency, and 99.79% in predicting recidivism. The deep reinforcement learning model performs well in correctional strategy recommendations, with an accuracy rate of more than 90% for different strategies. The application of big data technology effectively improves the formulation and implementation of community correctional strategies, which positively impacts modernizing social governance.
Exploring the Path of Integrating Media Literacy into Civic and Political Education of College Students in the New Media Era
Authors: Yang Jian , Li Fangling 
Published: March 2024

Abstract
Media literacy education plays a vital role in college students’ ideological and political education in the new media era. The development and popularization of new media technology and the widespread use of the Internet and social media have significantly changed the way of acquiring, processing and disseminating information, and put forward new challenges and requirements for college students’ ideological and political education. In this context, an in-depth exploration of the effective way to integrate media literacy into college students’ ideological and political education can help enhance their media critical abilities and promote their overall development. This paper uses the three-dimensional media literacy curriculum model (visual culture, text analysis and individual competence) to design teaching strategies for integrating media literacy into civic and political education. Through a questionnaire survey of 2,000 college students and teachers, students’ media literacy scores increased from an average of 3.1159 to 3.4159 after applying the teaching strategy, with the most noticeable improvement in media awareness and comprehension, and Analysis and critical thinking. The study demonstrated a significant improvement in the ability of college students to analyze and evaluate online information by incorporating media literacy. Strengthening media literacy education, primarily through integrating the three-dimensional media literacy model of visual culture, text analysis and individual competence, can effectively enhance the media literacy of college students and provide necessary support for their healthy growth in the information society.
Research on Product Development of Rural Study Tourism Based on the ASEB Analysis Method
Authors: Wang Cunjie , Chen Dongfeng , Kong Wei , Jia Henan 
Published: March 2024

Abstract
As rural study tourism emerges as a favorite among educational stakeholders and families, its role in student development and rural economic revival becomes increasingly significant. Through comprehensive ASEB analysis, this paper proposes a scientific approach to developing rural study tourism offerings, based on data from 320 tourists and 57 rural managers, collected via fieldwork and online surveys. By examining the activity, environment, experience, and benefits framework, and integrating satisfaction and importance metrics through IPA quadrants, we navigate the complexities of product development in rural tourism. Challenges such as lack of experience diversity and competition are addressed, with strategies for highlighting rural cultural identity, enhancing experiential variety, and embracing technology. The goal is to guide rural study tourism towards sustainable growth, ensuring mutual benefits for rural economies and cultural heritage.
The use of thermovision in the detection of asymptomatic facial inflammation – Pilot study
Authors: Mrowiec Aleksandra Małgorzata , Świątkowski Antoni , Wolnica Kamila , Cholewka Armand , Niedzielska Iwona , Wziątek-Kuczmik Daria 
Published: March 2024

Abstract

Introduction: Asymptomatic inflammatory foci resulting from chronic dental infections or the presence of foreign bodies, e.g., in the sinuses, leading to serious local and systemic complications. These conditions, despite the absence of symptoms, are a source of bacterial, toxic, allergic, and neurogenic effects. Despite the developed radiological diagnostics in the form of pantomographic images and cone beam computed tomography (CBCT), thermal imaging is increasingly mentioned as an auxiliary tool in detecting and treating inflammatory foci of the craniofacial region. It is due that thermal imaging can bring indirectly metabolic information about studied tissues and thus allow the detection of early inflammatory lesions yet not invisible on X-ray diagnostics. The purpose of the study was to apply and validate a protocol for identifying asymptomatic inflammatory foci in the maxillofacial area using thermal imaging.

Material and methods: Patients referred to the institute were examined for the presence of inflammatory foci. Analysis of radiographs revealed asymptomatic foci in the maxillary sinus, which were associated with the presence of a displaced foreign body. Thermographic examinations were performed using a FLIR T540 thermal imaging camera.

Results: The obtained results showed a significant temperature difference between the suspected and opposite sinus. It was also confirmed that after the foreign body was removed from the sinus the temperature showed a dynamic decrease in time.

Conclusions: According to the study, thermography has the potential to become an essential diagnostic tool for inflammation of odontogenic origin. It offers non-invasiveness, safety, real-time imaging, painless and completely safe.
Inflammatory biomarker-based clinical practice in patients with pneumonia: A systematic review of randomized controlled trials
Authors: Dimitropoulos Dimitrios , Karmpadakis Michalis , Paraskevas Themistoklis , Michailides Christos , Lagadinou Maria , Platanaki Christina , Pierrakos Charalampos , Velissaris Dimitrios 
Published: March 2024

Abstract

Objectives: Biomarker-based clinical practice is currently gaining ground and increasingly affects decision making. A variety of biomarkers have been studied through the years and some of them have already an established role in modern medicine, such as procalcitonin (PCT) which has been proposed to reduce antibiotic exposure. We purposed to systematically review all biomarkers examined for guiding the clinical practice in patients with pneumonia.
Methods: A systematic review on PubMed was performed on April 2023 by two independent researchers using the PRISMA guidelines. Randomized trials which enrolled patients with pneumonia and compared biomarker-guided strategies to standard of care were included.
Results: 1242 studies were recorded, from whom 16 were eligible for this study. 14 studies investigated PCT as a biomarker. From these, 8 studies reported on community acquired pneumonia (CAP), 2 on ventilator associated pneumonia (VAP), 1 on aspiration pneumonia, 1 on hospital acquired pneumonia (HAP) and 2 on exacerbation of chronic obstructive pulmonary disease (ECOPD). There was 1 study, referred to VAP, that investigated interleukin-1β (IL-1β) and interleukin-8 (IL-8) and 1 study that reported the role of C-reactive protein (CRP) in ECOPD. In a total of 4751 patients in 15 studies, the biomarker-based approach did not lead to increased mortality [OR: 0.998 (95%CI: 0.74-1.34, p value: 0.991). I2:19%]. Among different types of pneumonia and time-points of assessment, biomarker-guided practice appeared to improve antibiotic-related outcomes, such as rate of antibiotic prescription, duration of antibiotic therapy and rate of antibiotic exposure, while 5 studies reported a possible decrease in antibiotic-related adverse effects. Biomarker-guided practice did not seem to lead in an increase in other adverse outcomes such as need for hospitalization and duration of hospitalization. However, the included studies have high risk of bias mainly due to improper blinding of participants/personnel and outcome assessors.
Conclusion: Biomarker-guided clinical practice improves provided healthcare, in terms of reduced antibiotic consumption with no inferiority to mortality, relapses and exacerbations in patients with different types of pneumonia. Thus, such approaches should be further evaluated to achieve personalized medicine.