bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the use of AI-generated AI-based drawings and music in Bipolar affective disorder interventions
Authors: Peng Cheng , Zhang Zhinan 
Published: December 2024

Abstract
This paper focuses on the application effect of the rehabilitation care means of integrating AI drawings and music on patients with Bipolar affective disorder. Analysing the symptoms of Bipolar affective disorder, combining pictures and music in terms of audio-visual effects on Bipolar affective disorder to ease the role of AI music and AI pictures to help music therapy and psychological counselling, respectively. Describe the music generation problem, introduce multi-label classification technology, and propose models for rhythm and melody generation based on LSTM networks and reinforcement learning algorithms. Compare the advantages of picture counselling and traditional psychotherapy, and propose a multi-frame HDR generation network based on generative adversarial networks to optimize AI pictures. Unifying AI drawings and music to implement rehabilitation intervention for Bipolar affective disorder, the changes in negative emotions, self-esteem level, and disease shame were counted before and aftercare. After the rehabilitation intervention, the four dimensions of stigma (social exclusion, economic discrimination, intrinsic shame, and social isolation) of the observation group were better than those of the control group, and there was a statistically significant difference between the two groups (P < 0.05). The rehabilitation intervention of AI-generated pictures and music can provide better psychological guidance to patients with Bipolar affective disorders and can effectively help patients with Bipolar affective disorders recover their health.
Analysis of Web-based Student Learning Behaviour and Teaching Adjustment Strategies in a Blended Teaching Model
Authors: Hao Yihui 
Published: December 2024

Abstract
In the context of the rapid development of Internet technology and the economy, the blended learning model has been supported by the environment, and web-based learning has begun to spread widely. In this paper, after using the Z-score data standardisation method to process the students’ online learning behaviour data under the blended learning mode, the correlation between each learning behaviour characteristic and performance is calculated by the correlation analysis method. Then, the SMOTE-XGBoost-FM model is proposed to predict students’ academic performance based on behavioural characteristics, and the performance prediction results are used to assist teachers in making teaching adjustments and interventions and obtain good teaching results. It was verified that the correlation between homework scores and academic performance among the online learning behavioural features was the highest (0.91), and the accuracy, precision and recall of the fusion model’s performance prediction were all above 0.95. It has also been found that after analyzing learning behavior and adjusting teaching strategies, the frequency of students’ online behavior significantly increases, and they can actively access various resources on the platform during the learning process. This paper explores the mechanism and strategy of differentiated teaching adjustment based on online learning analysis, which achieves accurate diagnosis, multi-dimensional follow-up and effective adjustment and intervention for students by making full use of behavioural analysis to diagnose and analyse students.
Identification and Research of Learning Behaviour Patterns in College English Classroom Based on Big Data Analysis
Authors: Jiang Min 
Published: December 2024

Abstract
Classroom learning behavior analysis is an important means for teachers to understand the classroom situation, which helps teachers grasp the learning situation of each student, adjust teaching strategies, and then improve teaching quality. The article first collects lecture videos from real English classroom teaching in universities, summarizes six typical classroom behaviors, and designs and constructs a student classroom learning behavior dataset for the classroom learning behavior recognition model. In order to better integrate the spatio-temporal features of students’ classroom learning behaviors, an end-to-end student classroom learning behavior recognition model based on the dual-attention mechanism integrating spatio-temporal features (DA-YOWO) is designed. Through experimental comparison with YOLOv5 and FasterR-CNN models, it has been verified that the model in this paper has a high behavioral recognition accuracy for student learning behaviors in the English classroom. Then, in the real classroom scenario, the comprehensive weights of the student behavior indicators were determined by principal component analysis and factor analysis, and the analysis of student learning engagement was carried out. According to the results of the analysis, the learning status of each student in the English classroom was clarified so that the teachers had enough time to reflect on the deficiencies and problems in their teaching process according to the student’s performance in the classroom, which helped the teachers to better grasp the whole classroom.
Research on the Teaching Reform Path of Automotive Service Engineering Based on Artificial Intelligence Application
Authors: Liu Jia , Li Ye , Shi Yan 
Published: December 2024

Abstract
This paper applies relevant research technologies, such as intelligent tutoring systems, automated assessment systems, educational robots, natural language processing, and intelligent robots, to the reform of automotive service engineering teaching. After teaching the automotive service engineering teaching course with artificial intelligence, the teaching experiment is carried out for two classes, and the teaching effect is analyzed through assessment, questionnaire survey, and other forms at the end of the experiment. The results show that by comparing the effectiveness of task work order teaching practice between the experimental group and the control group, the experimental group’s scores are mainly concentrated in the range of 75 to 90 points. In terms of operational skills, the experimental group scored 0.8 points more than the control group. The experimental group’s theory and practical training scores are superior to the control group’s excellence rate. Students’ acceptance and satisfaction with teaching artificial intelligence is over 65%.
A Study on the Network Effect of Chinese Speech Features in Cross-cultural Communication Based on Semantic Networks
Authors: Liu Qinglian 
Published: December 2024

Abstract
In this paper, a semantic network-based topic word extraction model for Chinese language features is constructed by combining the theoretical knowledge of the conceptual semantic network. Chinese language texts and videos on social networks are selected to build a lexicon and a knowledge base, and the forward maximum matching method is used for lexical segmentation. After calculating the weights of all the candidate subject words using the vector space model, the words are sorted from largest to smallest in terms of weight, and then the top N are filtered out as the final subject words to analyse their influence in cross-cultural communication. It can be seen that among the lexical features of the linguistic landscape, the word “shop” has the largest weight value of 0.2326, and among the lexical features of the linguistic landscape, “gold shop” and “medicine shop” are the most common. The most common word features in the linguistic landscape are “gold bank”, “medicine bank”, etc. The mean value of harmonic Chinese network buzzwords is 3.0078, which is the most widely spread. The number of likes and shares of video content without a series is generally high. In the event of Chinese language fever, it is important to investigate the means of international dissemination of Chinese language features based on semantic networks.
Synergistic integration of community-based gerontological education and governance in the context of active ageing
Authors: Liu Xiaopei 
Published: December 2024

Abstract
This paper builds a theoretical research framework for collaborative governance from theories related to active ageing, collaborative governance, and other relevant theories. To study geriatric education. Taking Z city as an example, it takes the current situation of the development of synergistic integration of elderly education and governance as an entry point, and adopts the idea of empirical analysis and countermeasure research to carry out the study. The results show that in 2023, the elderly population of 60 years old and over 65 years old in Z city has reached 2,047,700 and 1,471,500 people. The distribution of the elderly population is mainly concentrated in four districts, A, B, C, and D. The teaching staff of a community school for the elderly in City Z is relatively large. There are 192 types of courses available for the elderly at the school. In the 2020-2023 calendar year, the activity room for the elderly in city Z, the elderly education institutions, and so on are in a steady development trend. There is a significant positive correlation between the willingness of the elderly to participate and literacy level, economic status, the amount of money they are willing to spend each year, the completeness of senior education venues and facilities, and the importance of senior education activities (P<0.05).
A Study of Teacher Behavioural Pattern Recognition Techniques in Music Education and Their Impact on Teaching Effectiveness
Authors: Wei Zehui , Zhao Zhou 
Published: December 2024

Abstract
This paper proposes a method for recognizing teacher behavior patterns using a graph convolutional neural network. This method extracts the human skeleton point information by using a single-frame image from a teaching video as a unit. Then, a graph convolutional neural network is used as a framework to aggregate multi-frame image information and identify the teacher’s behavioral patterns. In order to verify the effectiveness of the method, the article constructs a video dataset collected from public music teaching videos on Catechism.com, which contains six categories of teacher behavioral patterns and conducts comparative experiments. The experimental results show that the teacher behavior pattern recognition technique based on a graph convolutional neural network can effectively exclude the interference of irrelevant information within the classroom scene and make full use of the information generated between the skeleton points in the multi-frame image to accurately identify the typical teaching behaviors of the teacher, with an accuracy of more than 0.96 and stronger robustness. This study can respond to teachers’ teaching status in a timely and effective manner, which can help teachers optimize their teaching behaviors and improve their teaching effectiveness in music classrooms.
Research on Ancient Text Recognition and Translation Assisted by Artificial Intelligence
Authors: Zhang Na 
Published: December 2024

Abstract
With the rapid development of artificial intelligence technology, the use of information processing technology for the study of ancient writing has also attracted more and more attention from the academic community. In this paper, we propose a technology for recognizing and translating ancient texts, aiming to assist in the advancement of ancient text research. Based on the network model SiameseWord and the twin network based on the deep residual network Resnet50, the image pair approach is used as the input of the network, and the feature vectors of each picture in the image pair are extracted by the network to realize the recognition of ancient characters. The mask matrix strategy is introduced into the BERT model, and the internal fusion and dynamic weighting of the multi-attention mechanism are used to construct an improved Masking-BERT model and improve the translation performance of the model. The Shang and Zhou gold text dataset is used as a research object to carry out ancient text recognition and translation practices. This paper’s method maintains a high recognition accuracy of 88.55% even when the number of Shang and Zhou gold text categories reaches 550. This paper’s translation method achieves a translation overlap rate of 25.26%, surpassing that of the comparative CUDA and PSO models, and it also achieves the highest contextual fitness, with a fitness of 60.07%.
Study on Strategies for Improving the Anti-interference Ability of Relay Protection Equipment in High Altitude Areas
Authors: Ma Haifeng , Li Yuan , Liu Dongping , Zhang Hongbo , Tai Xianghua , Zhang Lin 
Published: December 2024

Abstract
The traditional power system relay protection technology in high altitude areas is more inefficient. This paper analyzes the main sources of interference of relay protection equipment in high altitude areas and proposes a targeted strategy to improve the anti-interference technology of relay protection. The mathematical model of relay protection failure is established, and the support vector machine algorithm model is used to realize the construction of the relay protection state algorithm model so that the macro data in the operation process of relay protection equipment is converted into microdata, which improves the management ability of relay protection equipment. Subsequently, the mathematical model of anti-interference calculation of relay protection devices is used to process the collected interference signals, eliminate the interference signals after accurate identification, and improve the anti-interference ability of relay protection equipment. The relay protection device undergoes testing after the construction of the relay protection state algorithm model and anti-interference calculation model. The experimental results show that the equipment failure rate under the relay protection state algorithm is less than 0.8%, the annual failure rate of equipment components is less than 0.2%, and the overall analog output error rate amplitude is only half of that under the original anti-jamming technology when adopting the mathematical model of anti-jamming calculation of relay protection device, which proves that the anti-jamming ability enhancement strategy proposed in this paper is capable of reducing the relay protection in high-altitude areas caused by false and refusal to act. Equipment losses in high altitude areas.
Algorithm-based Optimisation of Students’ Personalised Learning Path Design in University English Teaching Models
Authors: Peng Xiaoxue 
Published: December 2024

Abstract
The current application of intelligent technology in the field of education is becoming more and more widespread, especially in personalized English teaching, which shows enormous potential and value. After combining the concepts of personalized learning paths with algorithm optimization, the study gathers online learning behavior data from university students as an example and investigates the factors that influence English learning through correlation and regression analysis. Afterward, the K-mean clustering algorithm is optimized by improving the method of determining the number of clusters, and the learner analysis is completed based on the learning behavior data to divide the sample students into clusters. On this basis, the personalized learning path under the university English teaching mode is designed and applied to the teaching practice to explore the students’ learning effects after the path is applied. The sample students can be divided into three categories: low-invested, medium-invested, and high-invested, which account for 10.19%, 25%, and 64.81%, respectively. The English scores of the students who applied the personalized learning path were significantly improved, with 7.037 and 6.985 points for freshmen and sophomores, respectively, and there was also a substantial improvement in English learning attitudes and learning strategies, which verified the practical effect of the designed personalized English learning path.
Research on Automation and Intelligence of Accounting Data Processing in Big Data Environment
Authors: Xing Ruolin 
Published: December 2024

Abstract
The automated accounting data processing system under the big data environment can realise the automation of data collection and confirmation, effectively improve the efficiency of accounting data processing, provide detailed accounting data of economic matters, and meet the increasing internal and external information needs. This paper constructs a mathematical model of accounting data processing, sets the correlation matrix of balance and incurred amount, respectively, and determines the current value after the matrix operation using the formula. A complete accounting data processing system is established from eight aspects, such as statement transformation and analysis, principal-volume-profit analysis, and so on. Add the BP artificial neural network structure, visualize the final output vector produced by it, and identify the components of automated accounting data processing. Set the experiment-related parameters and use a combination of comparative experiments and empirical analysis to evaluate the effects of automation and intelligence on accounting data processing. After completing the construction of accounting data processing automation, the annual manual accounting efficiency days of the subject enterprise decreased from 3.875 in 2017 to 1.92 in 2022, and the efficiency of manual cost accounting was improved. The gearing ratio diminishes annually, from 72 in 2017 to 68 in 2022, and the test enterprise’s long-term solvency is on the rise.
DESENVOLVIMENTO DE CALCULADORA AMBIENTAL PARA ANÁLISE DO REUSO NO MERCADO DE AUTOPEÇAS DE REPOSIÇÃO
Authors: Hernández Francisco del Moral , Yoshiyasse Ciro Seiji 
Published: December 2024

Objetivo: Este artigo tem como objetivo demonstrar através do uso de uma calculadora estratégias efetivas de Ecodesign do REUSO para extensão da vida de autopeças e redução de impacto ambiental, a partir da manutenção de componentes para sua reintrodução à cadeia produtiva e de consumo. Um componente relevante é escolhido de um motor elétrico que seria descartado para em seguida comparar o impacto energético, hídrico e de emissões de CO2 com as alternativas que o mercado oferece: utilizar uma peça original, uma peça reparada ou um similar importado. O objetivo é identificar escolhas ambientalmente mais amigáveis e validar um modelo indicativo de cálculo.

Referencial Teórico: O referencial téorico descreve as condições de contorno de Ecodesign: Reuso de autopeças Emissões de CO2, Pegada hídrica, Embodied Energy como mediador estratégico; Produção primária e Avaliação de Ciclo de Vida (ACV).

Método: Quantitativo.Utiliza dados relacionados aos materiais envolvidos descritos na literatura de ACV a partir de três indicadores ambientais: pegada de carbono, hídrica e energética. Comparm-se os dados de impacto entre as alternativas definidas em condições similares às práticas usuais do mercado de reposição de autopeças.

Resultados e Discussão: É realizado cálculo aproximado através da calculadora ambiental da intensidade de energia de uma mercadoria industrializada. A comparação dos resultados (com reuso e sem reuso) de materiais produzidos e reparados localmente e produzidos em outros países mostra: quanto à massa dos produtos, o potencial de impacto do produto reintroduzido reformado é próximo de zero; na pegada hídrica de produtos similares produzidos por processos similares a pegada hídrica revela volume similar e a quantidade de energia incorporada será similar.
Implicações da Pesquisa: Realizar escolhas ambientalmente amigáveis associadas a: Políticas públicas para incentivo à operacionalização e normatização do parque industrial local voltado para a reposição de componentes automotivos. Indicação de beneficios econômicos e ambientais. O tratamento de incertezas poderia, em desdobramento futuros, se servir de lógica paraconsistente.

Originalidade/Valor: Um Eco Indicador envolvendo as pegadas hídricas, de Carbono e de Energia incorporada, toma relevo a partir de uma estratégia de visualização simultânea de indicadores ambientais de forte peso na cadeia produtiva.
Sodium nitrite ingestion in a suicide attempt: First reported case in the Middle East
Authors: Ahmed Lamees , Ba Fatimetou , Al-khaleefa Farah , Al-saadoon Abrar , Merie Mhd 
Published: December 2024

Sodium nitrite (NaNO2) is a water-soluble, white-yellow-colored crystalline powder with various practical applications including use as a food preservative, antimicrobial, and coloring agent. The use of sodium nitrite has recently emerged as an increasingly popular method of suicide. The first reported case of intentional sodium nitrite ingestion occurred in 1979. Two cases were reported in Tokyo, Japan, one in 1996 and another in 2000. Between 2000 and recent years, there has been a distinct lack of cases with only 1 case in New Zealand in 2010 reported. However, since 2019 the number of reported suicides using sodium nitrite has significantly increased compared to previous years. Sodium nitrite ingestion can be fatal, depending on the dose ingested. It causes ferrous iron to be oxidized to ferric iron (Fe3+), thus losing its ability to bind oxygen and resulting in a greater affinity for oxygen in the remaining Fe2+ heme groups that are present in the same hemoglobin structure. Thus, hemoglobin is transformed into Methemoglobin (MHb), which has a lower capacity to transport oxygen and release it into the tissue. Methemoglobin cannot bind oxygen which results in impaired oxygen transport, subsequent hypoxia, and lactic acidosis. We report the first case to ever be seen in the middle east, in the UAE, Sharjah. A 21-year-old male known case of depression on treatment presenting with an acute onset abdominal pain and multiple episodes of vomiting. When the patient arrived at our ED, he had a low GCS with severe central cyanosis and hypoxia. Fortunately, the patient&apos;s first degree relative informed the team that she had witnessed him ingest sodium nitrite powder as an attempt to commit suicide, which hastened the identification of the causative toxin and thus patient&apos;s management and the delivery of its antidote, methylene blue, which led the patient to a full recovery. In this case report we would like to highlight and discuss early identification and delivery of antidote in such patients, the clinical and lab findings that support the diagnosis and the possible complications that can arise.