bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction and Application of Online Learning Community for Teacher-Student Relationship Based on Online Classroom
Authors: Wang Haihong , Feng Lichun , Zhang Wen
Published: November 2024

The application of information technology in the education business makes the traditional education mode gradually withdraw from the stage of history, and the online learning classroom begins to emerge in the teaching field. This paper builds an online learning community classroom for teacher-student relationships by combining MVC architecture technology with various online learning modes in the online classroom. FIAS interaction analysis and multiple regression models are used to quantitatively analyze interactive behaviors and changes in teacher-student relationships. The online learning community classroom model of this paper improves the average grade of the experimental class by 3.15 compared with the control class, with a p-value of 0.015, which is less than 0.05, and there is a statistically significant difference in the data. The teaching model of this paper can adjust the structure of teacher-student speech in teaching, and the teacher changes from “teaching alone” to “discussing with teachers and students”, and the students’ speech improves by more than 10%. In addition, this paper finds that for every one unit change in subject and teacher, the teacher-student relationship changes by 0.135, 0.261, and for every one unit increase in the teacher’s “creation of learning groups for grouping tasks”, the teacher-student relationship improves by 0.364. Similarly, for every one-unit increase in the teacher-student verbal interaction, the relationship between the teacher and the student increases by 0.142, 0.364, and 0.364, respectively. In summary, through the design and practical application of the online learning community classroom model in this paper, the functions and effectiveness of the system have been verified, which basically meets the needs of teacher-student interactive teaching.
In-depth Analysis of the Positive Impact of the Organic Integration of Nonheritage Aesthetic Education and Art Healing on College Students’ Mental Health in the Age of Artificial Intelligence
Authors: Zheng Sheng , Chen Yuqun , Zou Jinding
Published: November 2024

In the increasingly competitive situation, college students are facing various pressures such as study, life, emotions, etc. It is highly probable that these pressures will have negative effects on both physical and mental health. In this paper, non-heritage aesthetic education and virtual interactive technology are respectively used as the driving force and model mechanism to construct an effective path for the organic integration of non-heritage aesthetic education and art healing, and the virtual interactive space integrating non-heritage aesthetic education is designed by using 3D max modeling technology and unity3d development platform. The changes in the mental health of college students were quantified by biofeedback measurement, happiness index scale, and psychological distress scale. The results showed that the alpha wave of the brain of the college students with low levels of psychological distress before and after the virtual interaction experiment gradually increased within 30 minutes, and the systolic and diastolic blood pressure decreased, indicating that they showed a state of relaxation and concentration during the interactive process of experiencing the non-heritage. In addition, there were highly significant differences (P<0.01) in the scores of study anxiety, loneliness tendency and physical symptoms of college students before and after the experiment of the subject college students, and the other dimensions of psychological distress were also well regulated. This paper shows that the integration of non-heritage aesthetic education and art healing has a positive effect on promoting the psychological health of college students to a certain extent and is of profound significance for contemporary colleges and universities to cultivate socialist successors with positive energy and responsibility.
Research on Intelligent Scheduling and Resource Optimization for Elderly Patient Care
Authors: Gui Haifeng , Ren Wenjing
Published: November 2024

The increasing demographic aging of society has led to the increasing demand for home health care services for the aging population. The planning and scheduling of home care pathways is a challenging problem in in-home medical care services. To this end, this paper proposes an improved hybrid immune genetic algorithm (HIGA) to design a least-cost medical care human resource scheduling strategy for the home care needs of elderly patients so as to provide high-quality home care services for elderly patients. It is tested with a genetic algorithm, modal factorization algorithm and simulated annealing algorithm to compare the arithmetic cases, and then HIGA is applied to find the optimal solution of the path and scheduling planning scheme of home care personnel for elderly patients. In all cases, HIGA has the best optimization effect on the model, and its optimal route cost is lower than the other three algorithms. Its variance is smaller than the other algorithms except for the R121_120 algorithm. This implies that the solutions generated by HIGA are superior and more uniform. The solution time of HIGA is less than 6 seconds in the path and scheduling planning problem of home caregivers for elderly patients in region M. The average total cost of caregivers to serve a single client point is 95, and the proportion of the time window penalty cost to the total cost is only 3.9%, which indicates that it is fast and the quality of its solution is higher, which is in line with the expectations of the decision-makers. This paper is of great significance in designing an effective care resource scheduling method to reduce the operating costs of home care agencies as well as to improve the quality of medical care services for elderly patients.
Research on the Application of Intelligent Algorithms in Preventive Damage Prediction and Diagnosis of Power Cable Channels
Authors: Guo Kang , Li Qian , Wang Siying , Zhang Jun , Zhang Zexin
Published: November 2024

In this paper, according to the definition of power cable channel damage and the actual situation, the vibration signals of mechanical excavation, crushing hammer construction, manual excavation and heavy vehicles passing by are selected as the initial data for the prediction of preventive damage of power cable channels. After completing the data collection, Variable Difference Modal Decomposition (VMD) is applied to reduce noise and obtain the IMF components. The eigenvalues such as energy entropy, cliff factor, waveform factor, center of gravity frequency and frequency standard deviation of each IMF component are extracted to form an eigenvector set, thus forming the data set for the study and analysis. The data set is divided into a training set and a test set according to the ratio of 8:2, and the long and short-term memory neural network is used to study the power cable channel damage prediction and diagnosis. The MAE in the single-step prediction of LSTM is 1.08, the MRE is 2.69%, and the RMSE is 1.39, and the prediction model in this paper is much better than the control prediction model, which indicates that the LSTM network can well predict the vibration of the damage of the power cable channel. It shows that the LSTM network can accurately predict and diagnose the signal trends and fluctuations caused by damaged power cable channel vibration.
Cross-border e-commerce oriented international logistics supply chain management model construction
Authors: Jia Shengqiang , Zhang Ke
Published: November 2024

This paper first introduces the lean supply chain international logistics management model and then determines the optimal conditions of the logistics supply chain for B2C enterprises through logistics service integrators and logistics service subcontractors. The paper then employs a combination of the factor analysis method and the factor rotation method to identify 17 key indicators that significantly influence the service quality of the international logistics management model for cross-border e-commerce. It then analyzes these relevant indicators and proposes targeted improvement countermeasures. The results revealed that the respondents have a high level of recognition for the 17 indicators that impact international logistics management, and their mean and standard deviation are stable. The results demonstrate that the first four factors can effectively explain the majority of the original data. Their eigenvalues are 8.157, 2.338, 2.132, and 1.045, and the variance contribution rates of their factors are 47.98%, 13.75%, 12.54%, and 6.14%, respectively, with the cumulative variance contribution rate explaining 80.41% of the original data. By combining the characteristics of the indicators, we identified four key factors: procurement, logistics and customs clearance, after-sale, and overall supply chain links. This study helps to better assist in the development of an international logistics supply chain management model oriented towards cross-border e-commerce.
Research on the application of data analysis technology and the mechanism of teaching effectiveness enhancement in sports training in colleges and universities
Authors: Cao Haibo
Published: November 2024

Data analysis technology can deeply analyze and fully mine the physical measurement data of college students to extract valuable information, thus providing data reference for teachers to improve teaching effectiveness. The article first researches the algorithmic process of the Apriori association rule algorithm, combines the transaction compression and hash technology and the Apriori algorithm to further optimize and improve Apriori, and finally applies the mechanism based on the improved data analysis technology in college sports teaching. This paper uses the improved Apriori algorithm to analyze physical test data of students in a college. In the 2021-2022 association rule, data mining results found that the “total score grade” passing students accounted for 74% of the students tested in that year, which can be obtained, the majority of the student’s physical test scores for the passing grade. After a period of a teaching experiment, the p-value of the four dimensions of students’ learning interest is 0.015, 0.048, 0.014, and 0.000, respectively, which is significantly different, thus indicating that the experimental group of students’ learning interest is significantly better than the control group.
Research on the application of neural network modeling in the assessment of traditional sports training and coaches’ quality in colleges and universities
Authors: Chen Dan , Lin Li
Published: November 2024

Scientific monitoring in sports training in colleges and universities is particularly important, which is an important symbol of the scientific level of sports training, and has a role in promoting the ability of athletes and coaches. In this paper, we collect data based on the evaluation index of coach quality and then preprocess the collected data of coach quality and athlete performance into the BP neural network model. The firefly algorithm is used to optimize the prediction performance of the BP neural network, and based on the model, a visualization system for sports training is constructed to display the predicted performance and quality assessment data in real-time. It has been found that the average prediction error of the BP neural network model based on the firefly algorithm is 0.73%, which can be used in sports training. The test scores of athletes in all aspects assisted by the visualization system were significantly better than those of the traditional training group, and the quality assessment scores of coaches in this model were significantly higher than those of the traditional group (P<0.05). This paper forms a systematic method for monitoring college sports training, which can enhance the scientific level of sports training monitoring and help improve the ability of athletes and the quality level of coaches.
Application of Data Analysis and Hierarchical Analysis in Project Management Supported by Resilience Theory
Authors: Chen Zhiwei , Dong Pingping
Published: November 2024

With the development of modern construction, the scale of construction has been expanding. Construction projects in the construction stage gradually exhibit high risk characteristics, and the complexity and difficulty of project management have increased greatly. Based on the perspective of resilience, this paper carries out an evaluation study of project management systems, taking four projects of building construction projects as cases to analyze the degree of resilience of construction project management systems. The study constructs an evaluation index system for project management that considers four aspects: stability, redundancy, efficiency, and adaptability. The network hierarchy analysis method is again proposed on the basis of the hierarchy analysis method, and the ANP-can-top cloud combination evaluation model applicable to project management is constructed by combining the advantages of the network hierarchy analysis method as well as the topable cloud model. The constructed resilience evaluation index system and evaluation model for project management are applied to the research cases to verify the practicality of the indexes and the rationality of the model. It has been found that stability has the greatest impact on building construction projects. In the reliability analysis of the topable cloud model, it is most reasonable when the number of cloud drops is taken in 1800. Based on the evaluation results, this paper proposes a strategy for improving the toughness level of the project management system, which provides a certain reference significance.
Quantitative Analysis and Strategy Research on the Improvement of Human Resource Allocation Efficiency in Big Data Environment
Authors: Gong Hui , Zhou Wenbin
Published: November 2024

In this paper, for the problem of optimal allocation of human resources in multiple R&D projects, the traditional genetic algorithm is introduced into the Tabu taboo search algorithm, which is able to jump out of the local optimal solution while ensuring the diversity of the population, so as to achieve the purpose of the global optimal solution. Three software system development projects of J Software Service Company have been selected as practice cases to explore the advantages of the improved genetic algorithm in optimizing human resource allocation. Fuzzy comprehensive evaluation and questionnaire surveys are also used to explore the efficiency and satisfaction of optimized solutions. According to the experiments, the enhanced genetic algorithm that is based on the enhanced genetic algorithm performs better than other algorithms. The efficiency value of human resource allocation in the project of J Software Service Company is 71.6, and more than 90% of the employees surveyed in the survey of the optimized scheme of staffing are considered to be satisfied.
A fast short circuit capacity calculation model based on regression neural network in complex grid environment
Authors: He Siyang , Zhao Shiqin , Wang Shuai , Huang Juncheng , Zhang Changzi , Wang Huaiyuan
Published: November 2024

The current short-circuit capacity level of local area power grids is close to the rated value of the existing equipment. To address this dilemma, this paper proposes a calculation method that most closely aligns with the principles of the short-circuit capacity calculation model and the short-circuit capacity calculation method in a pure AC power system, as well as the original definitions of the short-circuit ratio and the effective short-circuit ratio index. Next, we calculate the maximum short-circuit capacity of each bus in the system using the short-circuit capacity calculation method, which is based on the current. We select the generators and active output of the loads that contribute most to the short-circuit capacity by calculating their relevant sensitivity under the typical current. We then use these generators as feature vectors to establish the training samples. Finally, we train a generalized regression neural network to calculate the short-circuit capacity in a complex grid environment. Based on this, a fast calculation model for short-circuit capacity in a complex grid environment is constructed, which can be applied to quickly scan and calculate the short-circuit capacity level of buses in an operating grid.GRNN consistently achieves the lowest MAE and RMSE values in the two datasets, B0005 and B0006. The absolute time of the calculation of several methods, namely, ICA-GPR, LS-SVR, AST-LSTM NN, and ICA-RCV, is much longer than the absolute time of GRNN. GRNN, and the absolute time of GRNN in B0006 is only 0.0077 s. It shows that the GRNN model not only optimizes the speed of calculating short-circuit capacity in complex grids but also improves the accuracy and stability of the calculation. The feasibility and effectiveness of the fast calculation model for short circuit capacity based on a generalized regression neural network have been verified.
A Practical Study of Translation Wisdom Teaching Based on Semantic Association Network Modeling
Authors: Hu Juan , Cui Cui
Published: November 2024

The traditional translation teaching mode is obsolete and inefficient. This paper designs a model for assessing translation quality based on a semantic association network and innovates the translation teaching mode accordingly. Firstly, a special semantic association processing layer is introduced for the RNN translation quality assessment system based on a cross-language pre-training model with a self-attention mechanism. Then, the semantic correlation between the original text and the translated text is enhanced by the similarity-enhanced splicing mechanism to improve the accuracy of quality assessment so as to construct a translation quality assessment model based on a semantic correlation network. The model is employed in a university’s translation classroom. It is found that the semantic association network similarity enhancement splicing mechanism designed in this paper can improve more than 10 percentage points on the basis of the rest of the algorithms, and the Pearson correlation coefficients are 72.81% and 70.79% compared with the competing models, which are significantly better than the optimal models UNBA (EN-DE)/UNBA (EN-RU). After applying the model, the average score of the experimental class in the semester post-test was 69.19, which was 22.7 points higher than the pre-test. There is an obvious tendency for the distribution of scores to move to the high score range, and the densest distribution of scores becomes 68-70, with 22 students scoring 70 or more and 5 students scoring 80 or more, which is an obvious improvement compared with the semester pre-test. After practicing in a translation classroom, the model in this paper realizes intelligent teaching, saves teachers’ time, and improves the quality of translation teaching.
An Exploration of the Application of Natural Language Processing Technology in the Quality Enhancement of Cross-Cultural Language Conversion
Authors: Xie Nina , Zhao Hui
Published: November 2024

In the context of the globalization era, language conversion plays an important role in cross-cultural communication. Natural Language Processing (NLP) technology enables computers to automatically convert text from one language to another, i.e., to achieve cross-language communication and understanding through machine translation. In this paper, we first extract webpage content through distributed crawlers to construct a parallel corpus of cultural contexts in English and Chinese. We then conduct an empirical analysis of cross-cultural translation in English-Chinese bilingualism using the improved Transformer translation model. If you compare the PPL and HM values of the Transformer improved model to the baseline model, they are -54.18 and 24.79 points lower than the baseline model. This is better than the improved values of Transformer (+BD+DE), which were -38.41 and 15.63 points lower. This indicates that the Transformer improvement model outperforms other models in terms of language conversion quality, specifically the translation effect. Therefore, we have verified the effectiveness of NLP technology in enhancing the quality of cross-cultural language conversion.
Research on the Application of Intelligent Analysis Model for Educational Content in Higher Education Physical Training
Authors: Lin Li , Chen Dan
Published: November 2024

Intelligent technology brings brand new possibilities for education and teaching by virtue of its powerful computing and learning capabilities. Aiming at the problems of low efficiency and poor error correction effect of the current means of correcting sports training errors, this paper uses intelligent analysis technology to design a sports training error detection model to automatically measure the training process so as to realize the measurement and analysis of training errors. The model divides the video image as candidate foreground and candidate background by combining the Bayesian decision-making method and inter-frame difference method and sets the initial value of this background as the main component analysis method to obtain the real-time detection results of target posture. The analysis of the model’s sports posture detection shows that the recognition rate of this paper’s algorithm for different sports is more than 81%, and the error rate and accuracy are 0.038% and 95.9%, which has a better sports training error detection accuracy. After applying the intelligent analysis model to actual sports training, the 100-meter run and standing long jump scores of the students trained with the intelligent analysis model are 3.52 points and 0.9 points higher, which proves that the sports training error detection model promotes sports training.
Exploration of Bassoon Quartet Teaching Methods and Training Methods under the Background of Informationization
Authors: Zhang Yuzhe
Published: November 2024

The continuous development and progress of the times have promoted the diversification of teaching methods in colleges and universities. Based on the teaching and training methods of the bassoon quartet, this paper proposes an evaluation model for their teaching and training methods, in combination with an informational background. Specifically, after extracting features from the audio signal, a two-way LSTM neural network and Softmax classification algorithm are used to evaluate the quality of bassoon quartet training audio. The purpose of the teaching practice for the bassoon quartet teaching method is to explore its practical utility and support information technology conditions. The results show that the evaluation model of bassoon quartet training has a high evaluation accuracy, and the values of each index are between 0.80 and 0.94. The level of bassoon quartet of the students was significantly improved after the application of the teaching method, and the percentages of “excellent” and “good” in the formative evaluation were 12.5%-22.5% and 42.5%-50.0%, and the number of students with scores above 75 points increased by 18% compared with that of the conventional teaching form. The number of students with scores of 75 or above increased by 18.96% compared to the conventional teaching format. In this paper, the teaching method of the bassoon quartet based on information technology can promote the teaching level of the bassoon quartet and enhance students’ mastery of the bassoon quartet.
A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training
Authors: Shi Mingming
Published: November 2024

As a new type of sports training, the application of sports information technology can effectively improve the athletes’ sports level and sports performance. In this paper, based on the way and effect of sports information technology application, a system for sports teaching and training assistance is designed. Through sports training intensity monitoring, the training intensity of the athletes is obtained, and a high load alarm is triggered. At the same time, the improved hybrid Kalman filter is used to solve the posture of training movements, and the DTW algorithm is used to recognize the training movements and compare them with the standard movements to obtain the training scores. Coaches can be assisted in formulating personalized teaching strategies by using the system’s obtained sports training data. This paper’s sports teaching and training assistance system has a significantly shorter response time than the two comparative systems, which suggests it has better response performance and practicality. The RMSE values of the improved hybrid Kalman filter are 0.92, 0.84 and 1.47 for the roll, pitch and yaw angles, respectively, which are better than that of the extended Kalman filter, indicating that it is closer to the original data and has better data fusion performance. Compared with the control class using traditional teaching strategies, the students in the experimental class using the personalized teaching strategies of this paper have higher skill improvement, indicating that the strategies of this paper have an important guiding role for personalized physical education teaching.
A data-driven model for human resource strategy development in higher education in a machine learning framework
Authors: Yang Linlin , Liu Yuwang
Published: November 2024

Various fields in real life have increasingly utilized machine learning methods and data mining technology in recent years. This paper creates a data-driven model to implement intelligent human resource management in colleges and universities. The model utilizes the fuzzy decision tree algorithm to assist colleges and universities in selecting the most suitable talents and completing the talent recruitment and selection process within a short timeframe. Additionally, it utilizes the ID3 algorithm to filter out potential staff departures and factors, enabling targeted decision-making to prevent talent loss. After the implementation of the human resource management strategy based on the model, the completion rate of the recruitment plan increased by 15.1% compared with the pre-implementation rate, the turnover rate decreased by 3%, and 91% of the employees agreed with the management of the Human Resources Department. This paper’s model provides a reliable personnel decision-making and management strategy for human resource management in universities.
A study on visualization of kindergarten teaching content assisted by big data technology
Authors: Du Xiyao
Published: November 2024

Preschool education is an indispensable and important part of education in China. The study collects the educational content of kindergarten teachers from five kindergartens in Shanghai as survey data, uses text mining methods to visualize and analyze the teaching content of kindergartens, screens the core words of the collected text data, and then uses the FW-K-means clustering algorithm to analyze the correlation between the core words, and then uses the LDA theme model to realize the visualization of the content of the teaching theme of young children. The results show that kindergarten teaching themes are divided into three major clustering themes: game teaching theme, physical education teaching theme, and art teaching theme, among which “interest cultivation” has the highest high-frequency word probability of 0.0217 and the lowest high-frequency word probability of 0.0098, which indicates that kindergarten teaching themes have the highest high-frequency word probability and lowest high-frequency word probability. 0.0098, indicating that kindergarten teaching content lacks attention to “creativity”, so kindergarten teaching needs to take this as the main cultivation direction. Based on big data-assisted text mining technology, we can perform visualization research on kindergarten teaching content.
A Computerized Visual Analysis of Form and Technique under Constructivist Theory in Musical Theatre Music Performance
Authors: Yu Xiaolin
Published: November 2024

A computer vision model is constructed to recognize the expressions and movements of musical performers so as to analyze the changes in the forms and techniques of musical performances under the constructivist theory. The model needs to perform pre-processing, such as grayscale conversion, thresholding, background subtraction, noise reduction, and so on, on the related video before recognizing expressions and actions. The performer’s movements are recognized based on TC-HGCN, while the expressions are recognized based on CSACNN. The average accuracy of TC-HGCN in recognizing dance movements in musicals reaches 87.95%. The facial expression recognition method based on CSACNN can overcome the shortcomings of commonly used expression recognition models. The use of computer vision modeling analyzes the constructivist theory on the form and technique of the next musical performance, which enhances the performance effect and has more infectious and artistic charm.
Research on the Strategy and Implementation Path of Digital Transformation of Traditional Sports Games in Modern Tourism Industry
Authors: Li Ming , Gao Zixuan
Published: November 2024

This paper employs panel data regression analysis to conduct empirical research on the impact of digital transformation on the high-quality development of the sports and games tourism industry, thereby verifying the driving path and role of this development. The Yellow River Basin’s scenic spots undergo further research on the development of the tourism industry, market feedback, tourist satisfaction, and other related issues following digital transformation. The results show that digital transformation promotes the efficiency of the sports game tourism industry, and the enhancement dimension reaches 1.164. Digital transformation has a positive impact on the overall development level of the high-quality sports tourism industry. The regression coefficient is 0.6021. Following the digital transformation, the tourism industry in the Yellow River Basin scenic area continues to grow, with a year-over-year increase in tourists of 29.89% in 2023. About 77% of tourists display positive emotions in their emotional image analysis. Therefore, this paper proposes a strategy for the digital transformation of sports and games tourism, which is useful for the future development of sports and games tourism.
Transformation of the Accounting Function in Supply Chain Finance Innovation in the Perspective of Intelligent Algorithms
Authors: Sun Meijiao , Gong Chunyan , Pan Cheng , Ye Zhenxiong
Published: November 2024

In the era of artificial intelligence, the transformation and reshaping of financial functions have become inevitable for the development of the accounting industry. Based on the background of the intelligent era, the study analyzes the transformation of the accounting function in supply chain finance and integrates intelligent algorithms to showcase their application in the accounting function. We construct the enterprise credit risk assessment model based on RAGA-PP, combining the evaluation index system and the accelerated legacy algorithm to optimize the projection tracing model. Then, using the XGBoost model, design the method for identifying corporate financial fraud. Empirical analysis reveals that the sample enterprises’ average projection value is 0.8277, indicating a medium-high credit risk, thereby confirming the feasibility of the RAGA-PP model. The XGBoost model exhibits superior stability and recognition accuracy in identifying financial fraud, with its fitness value and loss value reaching 0.483 and 0.688, respectively. Its recognition accuracy surpasses that of comparative algorithms by 12.23% to 14.43%. With the support of intelligent algorithms, supply chain finance can use intelligent models to assist in management and decision-making and provide more value for enterprises.
Research on Physical Fitness and Health Improvement Strategies for Youth Basketball Players Based on Big Data Analysis
Authors: Ge Chenxuan
Published: November 2024

This paper aims to address the limitations of the traditional K-mean clustering algorithm, which does not account for the influence of both extremely poor and excellent physical fitness classmates on abnormality detection and the issue of high false detection rate. It bases its approach on three abnormality detection methods: K-mean clustering, distance, and density. These methods are used to determine the physical fitness test data outlying index (PFT-OI) and identify abnormal data. We used this algorithm to conduct research on the physical health of youth basketball players from a big data perspective. The differences between the mean values of height, weight, and BMI pre- and post-test scores of adolescent basketball players before and after the experiment were not significant (P > 0.05). The p-values of the pre- and post-test scores of 50 meters, standing long jump, and 1000 meters of male athletes after the experiment were 0.000**, 0.005**, and 0.029*, respectively. The absolute values of the pre- and post-test scores of female athletes differed by 1.05 seconds, 0.2 meters, 5 meters, and 8.12 seconds, respectively, except for the pre- and post-test differences of seated forward bends, which were not significant (P < 0.05). In the teaching of basketball, male adolescent basketball players can focus on practicing the 50-meter run and standing long jump and, at the same time, controlling body weight in order to maximize the possibility of achieving an excellent overall assessment score. Female athletes should prioritize 50 meters, 800 meters, and the standing long jump in their daily training regimen.
Application Scenarios and Framework Construction of Artificial Intelligence Technology in Higher Vocational Education
Authors: Gao Feng , Xiao Xiong
Published: November 2024

In this paper, 114 higher vocational schools in X city are selected as the research object, and a questionnaire survey is conducted to find out the current situation of the teaching application of the key technology of artificial intelligence and the existing problems. The application of AI in higher vocational education is discussed in depth, focusing on personalized learning, automatic assessment of big data, and deep learning, and an AI education framework based on the three levels of the data layer, the deep learning layer, and the application layer. The model was utilized to implement educational practices and evaluate the effectiveness of teaching. After analysis, it can be seen that only 10% of teachers will adjust the purpose of teaching due to AI, and AI is mainly embodied in the form of tools in the teaching methodology scenario. AI is believed by 60.98% of teachers to enhance teacher-student interaction, and it has a significant impact on teaching evaluation. The teaching performance of AI was significantly better than regular teaching (Sig.=0.043).
Quantitative Analysis within Language Studies: An Analytical Views Based on the Bibliometrics Method
Authors: Nugraha Danang Satria
Published: November 2024

Abstract
Background: While qualitative research methods have provided insights into linguistic structures and processes, the recent decades have witnessed a growing emphasis on quantitative approaches.
Methodology: This study utilizes a bibliometric analysis based on the PRISMA guidelines to explore research trends in “Quantitative Analysis within Language Studies” from 2014 to 2024 by analyzing the metadata of 53,575 journal articles retrieved from a reputable database, i.e., Dimensions.
Findings: The findings show that (1) there was a steady rise in publications throughout the period; (2) the total citations increased by over 113,000, representing a nearly 100-fold growth (1,141 citations in 2014 compared to 41,203 citations in 2024); (3) according to the ANZSRC 2020 classification, the field of Language, Communication and Culture has the highest number of publications (53,575), followed by Linguistics (32,121) and Language Studies (18,272); (4) there was the top 10 journals from 2014 to 2024; (5) there was a detail into the publication productivity and citation impact of 20 leading researchers; and (6) it has been extracted 1,028 terms concerning the examined query that classified into eight clusters.
Conclusion: By integrating the insights gleaned from publication trends, citation analysis, leading authors, journal distribution, map-based visualizations, and cluster analysis of terms, this study provides a comprehensive and detailed picture of research trends within the search query.
Originality: While current several research in quantitative language studies tends to delve into specific subfields, offering piecemeal understandings of the overall landscape, this study takes a distinct approach. By examining 53,575 articles content and metadata, this study aims to provide a more comprehensive perspective compare to previous fragmented insights.
Indonesian Teachers’ Perspectives on Gender Representation in EFL Textbook Visual Images for Elementary Schools
Authors: Rusmawaty Desy , Hermagustiana Istanti , Majewska Marysia , Ahada Ichi , Sucahyo Didik
Published: November 2024

Background: The use of gendered visual imagery in English as a Foreign Language (EFL) textbooks has raised concerns about perpetuating gender stereotypes and the potential effect on students' conceptions of gender roles. Despite the widespread use of mandated textbooks in primary classrooms in Indonesia, less is known about instructors' perceptions of gendered visual imagery.
Methodology: This qualitative research employed a mixed-methods approach, incorporating quantitative and qualitative data collection and analytic methodologies. An investigation was conducted in Samarinda East Kalimantan to collect the perspectives of 41 teachers who teach English in elementary school regarding the gendered visual representations present in the mandatory EFL textbooks. Furthermore, 12 teachers were chosen to partake in exhaustive interviews to present more informed and profound perspectives.
Findings: They merely focused on how to teach the materials rather than scrutinizing the gender bias represented through texts and visual images. The study revealed that a significant proportion of teachers (80%) believed that gendered visual imagery in textbooks was not impartial and could influence the gender roles of pupils. Furthermore, a significant majority (75%) said they often adjusted or avoided using these pictures in their teaching methods. The interviews highlighted the teachers' concern about the possible negative impact of these pictures on students' social relationships and self-image. However, they merely focused on how to teach the materials rather than scrutinizing the gender bias represented through texts and visual images
Conclusion: The findings suggest that elementary school teachers in Indonesia know the potential negative repercussions of gendered visual images in EFL textbooks and have implemented measures to mitigate these effects. The study underscores the importance of incorporating diverse and inclusive visual materials into educational resources to promote gender equality and positive gender representations. This concept is new to them. They agree that gender equality needs to be addressed and discussed as part of the content in classroom practices to raise students’ awareness of gender bias.
Originality: Previous studies have investigated the frequency and occurrence of gender bias in textbooks, found in images and texts. However, there is a lack of investigation into teachers' awareness of gender stereotypes in textbooks. This study enhances the existing corpus of literature by examining the perspectives of teachers on the use of gendered visual images in EFL textbooks in elementary schools in Indonesia. It emphasizes the importance of teachers and policymakers advocating for more inclusive educational resources and considering the impact of visual materials on students' gender perceptions
The Needs of Teaching English Using Brain-Based Learning in 21st Century Era
Authors: Winantaka Binar , Katrina Herda Rozanah , Sulistyowati Nina , Margana Margana , Mukminatun Siti , Aguilar Principe Regine , Yingfan Miao
Published: November 2024

Background: In the context of 21st-century learning, effective engagement of students and accommodation of many learning styles depend on brain-based learning (BBL). This approach improves understanding, memory, and adaptability by matching instruction with cognitive science and educational needs. Therefore, BBL emphasizes matching teaching approaches to how the brain processes and remembers information, which improves language acquisition, as teaching English entails employing tactics that address cognitive skills such as memory, attention, and motivation. Methodology: This quantitative study involved 50 English teachers in Yogyakarta, Indonesia, aim to reveal the teachers’ needs to teach English using BBL in this 21st-century era. The instrument of data collection was a questionnaire designed using the 5-Likert Scale that was interpreted using mean scores for each statement.Findings: The finding of this study revealed teachers need BBL in applying teaching scenarios, as well as the BBL represents Project-Based learning supported by audiovisual and critical thinking integration during the learning process. All those components are fitted with the characteristics of the balance of the right and left-brain sides.Conclusion: It is deemed essential since it directly affects the instructors' ability to effectively nurture student engagement and achievement, and the teachers should be consulted regarding the need for BBL. Incorporating educators into this conversation also guarantees that the tactics used are realistic and adapted to actual classroom situations. The knowledge and expertise of educators are crucial for improving brain-based learning strategies.Originality: This study can be a pioneer in exploring more opportunities for the needs of BBL and its relationship with learning success. Moreover, knowing brain balance contributes to a more inclusive and effective learning environment by accommodating different learning styles and boosting overall student well-being and academic achievement.
Türkiye ve İstanbul Enflasyon Oranları Arasındaki Granger Nedenselliğin Araştırılması
Authors: Fenkli Mesut , Özçalık Melih , Özdil Tuncer
Published: November 2024

Türkiye’de son yıllarda yaşanan yüksek enflasyon en önemli makro ekonomik problemlerden biri haline dönüşmüştür. Yaşam maliyetlerindeki artış tüm ülkenin yanı sıra en çok da nüfus ve sermaye yoğunluğu bakımından Türkiye’nin en büyük şehri ve hatta küresel metropollerden biri kabul edilen İstanbul’da gerçekleşmiştir. Bu bakımdan Türkiye ve İstanbul enflasyon oranları arasındaki ilişki araştırılmaya değer görülmüştür. Bunun için 2014 (İstanbul için ulaşılan en eski veriler)-2024 yıllarını kapsayan Türkiye ve İstanbul aylık enflasyon oranlarını (124 gözlem) baz alan zaman serileri üzerinden Engel Granger Koentegrasyon modeli tahmin edilmiş ve gerekli koşulları sağlamasından ötürü kabul edilerek her iki değişken arasında uzun dönem koentegrasyon ilişkisi olduğu ortaya çıkmıştır. Sonrasında iki değişkenli VAR model tahmin edilmiş, tahmin edilen VAR model diagnostik testleri geçerek kabul edilerek diğer zaman serisi analizleri yapılmıştır. Araştırma sonuçlarına göre, her iki değişken arasında uzun dönemli ilişkinin varlığına karar verilmiş olup, 2014-2024 yılları aralığında İstanbul enflasyonunun Türkiye enflasyonu için Granger nedeni olmasının yanı sıra İstanbul enflasyonunda yaşanan bir birimlik (standart sapmalık) şokun Türkiye enflasyonuna, Türkiye enflasyonunda yaşanan bir birimlik şokun İstanbul enflasyonunda pozitif (artış) yönde etkiye neden olduğu tespit edilmiştir.
Tiltwing eVTOL Transition Trajectory Optimization
Authors: Panish Leo , Bacic Marko
Published: November 2024

This paper studies transition trajectory optimization for a tiltwing electrical vertical takeoff and landing (eVTOL) aircraft configuration and evaluates the benefits of equipping it with fluid injection active flow control (AFC). Using a three-degree-of-freedom model of the aircraft’s longitudinal dynamics, both hover-to-cruise (forward) and cruise-to-hover (backward) transition are investigated for a range of center-of-gravity (CG) positions to determine minimum-energy trajectories that achieve zero altitude variation while avoiding wing aerodynamic stall. It is demonstrated that constant-altitude transition can be achieved in the forward direction (with or without AFC), whereas the use of AFC in backward transition reduces the total altitude change by 24%. A comparative constant-altitude backward transition solution using a different airfoil with a higher stall limit suggests that a redesign of the tail wing of this unique tiltwing aircraft could better leverage the benefits of using AFC in this application. Additionally, it is also shown that, in the case of forward transition, AFC can be used to decrease the total transition time and increase the range of CG positions where constant-altitude transition can occur.
Фінансові інструменти для підтримки сталого розвитку туристичної інфраструктури в Україні
Authors:   Національний університет «Чернігівська політехніка», Зеленська Олена , Руснак Денис ,   Національний університет «Чернігівська політехніка»
Published: November 2024

У статті доведено, що ефективне функціонування економіки України значною мірою залежить від стану туристичної інфраструктури, яка потребує регулювання і стимулювання на державному та регіональному рівнях. Встановлено, що розвиток інфраструктури в туристичному бізнесі характеризується такими важливими властивостями, як динамічність, керованість, системність, упорядкованість, циклічність, адаптивність, конгруентність і результативність, які дозволяють підприємствам туристичної інфраструктури ефективно адаптуватися до змін ринку, покращувати якість послуг та підвищувати їхню конкурентоспроможність. Виявлено, що розвиток інфраструктури зумовлений специфікою економічної діяльності туристичних підприємств, яка включає сезонність, територіальну розгалуженість, взаємозв’язки з іншими галузями, орієнтацію на клієнтів із середнім та високим рівнем доходів, а також значний соціально-культурний аспект. Аргументовано, що загострення конкуренції на туристичному ринку диктує необхідність активного розвитку інноваційних підходів, запровадження яких сприяє максимізації прибутків, удосконаленню бізнес-процесів, підвищенню якості послуг, автоматизації технологічних процесів та впровадженню раціонального природокористування. Ідентифіковано концептуальні характеристики та особливості інноваційної діяльності інфраструктурних підприємств туристичної сфери, що дозволило визначити та структурувати основний інструментарій управління. Запропоновано класифікацію управлінських інструментів, яка формує базу для ефективного керування інноваційним розвитком у туристичній галузі. Було визначено, що для підтримки інноваційного розвитку інфраструктури туристичної галузі в Україні можуть бути ефективно використані різні фінансові інструменти, такі як банківські кредити, інвестиції, лізинг, гранти і субсидії, а також публічно-приватні партнерства.
Необхідність застосування інновацій в управлінні підприємствами галузі ветеринарної медицини
Authors:   Національний університет «Чернігівська політехніка», Сакун В’ячеслав
Published: November 2024

Стаття присвячена обґрунтуванню необхідності та аналізу застосування інноваційних підходів в управлінні підприємствами у сфері ветеринарної медицини. Більш поглиблено розглянуто роль маркетингових досліджень у галузі ветеринарних послуг. Також проаналізовано та здійснено порівняльну характеристику сучасних програмних продуктів для управління у ветеринарних клініках. Доведена доцільність поширення серед керівного складу ветеринарних підприємств посилення маркетингової складової в інформаційно-аналітичному забезпеченні прийняття управлінських рішень, а також підвищення рівня автоматизації та використання інформаційних технологій для обслуговування та комунікації з клієнтами.