bluuebunny/crossref_metadata_2025_split huggingface dataset:
A prospective study of the randomized forest approach to predict the effectiveness of art healing in the treatment of depression
Authors: Ma Di , Ma Tianhe 
Published: October 2024

Abstract
Art healing methods have gradually become a new outlet for the treatment of depression in recent years. In this paper, an expressive art healing method is proposed based on the condition of depression, and the treatment effect is predicted using the randomized forest method. The direction of influencing factor selection for depression is proposed from three perspectives: demographics, physical and cognitive health status, and mental health. After determining the influencing factors, the samples of depression were extracted, and the classification tree model was constructed using the C4.5 decision tree algorithm. The random forest mode classifier’s generalization error is identified, and the model’s overfitting problem is prevented by restricting the random forest’s convergence. Finally, the evaluation index of the model is proposed, and the prediction accuracy and effect of the random forest model are investigated through the combination of simulation experiments and practical application. The area under the ROC curve shows that the number of cases correctly categorized by the random forest prediction model before and after art healing is 456 and 468, respectively, and the AUC values are 0.8145 and 0.8265, respectively, which makes the model’s prediction ability better. From the prediction results of the random forest model, the intervention group has a significant difference from the control group in two aspects of paranoia level and horror mood, with p-values of 0.01 and 0.001, respectively, after the prediction of the random forest model has a better effect on the intervention of depression.
Research on real-time data analysis system of sports training based on reconfigurable data mining technology
Authors: Ye Shuai 
Published: October 2024

Abstract
This paper utilizes smart wearable devices to quickly capture, record, and store performance information (calorie consumption assessment, pedometer, and heart rate monitoring), location information, and biochemical data of sports training populations through the sensory layer. Based on the reconfigurable data mining-related technology, combined with the data and test scores of the sports training population, data mining analysis is carried out to form valuable physiological, physical, and other characteristic information, which provides a scientific and referable strategy for sports training, thus also achieving the purpose of improving the training effect of athletes. The results show that, from the visualization and analysis results of the decision tree model for boys and girls, the girls’ long-distance running event (800 m) and boys’ pull-ups are located in the root node of the decision tree, which has the most significant effect on the performance level. Consequently, sports training programs were created that were more logically based on the distinction between male and female students. In individual exercise energy expenditure, by 420s, the limiting energy expenditure rate of 5.6 kcal/30s exceeded that of the athletes. Moreover, with time, energy expenditure continues to decline. The accuracy of this test can be 90%.
Analysis of International Political Economy and Regional Synergy in the Age of Network Informatization
Authors: Pei Ling 
Published: October 2024

Abstract
The world has entered the network information age driven by the information technology revolution, and the international political and economic order is facing turbulent changes. As an important issue in international political economy, this paper builds a model to measure the level of regional synergy development. In the prediction model, the GM(1,1) gray prediction model is chosen to establish the first-order differential equation to predict the trend of synergy development. In the measurement model, the synergistic development index system is built, the entropy weight method is used to determine the weight of the indexes, and the coefficient of variation method is applied to measure the development gap of different cities and territories in the region, and the comprehensive development level of the region is measured. The analysis of regional synergistic development is carried out in the Beijing-Tianjin-Hebei region of China, and the static measure of the overall synergistic degree of the Beijing-Tianjin-Hebei region reaches 0.073 in 2023 and the dynamic measure of the region in 2015-2023 is 0.0911. Beijing is ahead of Hebei and Tianjin in the five regional synergistic dimensions, which are economic, social, ecological, governmental governance, and coordination.
Dynamic Color Design Model Construction Considering Ambient Light Changes
Authors: He Bin 
Published: October 2024

Abstract
The study takes dynamic color design as the main research object, firstly, based on the atmospheric scattering model and the improved dark primary color a priori algorithm, designing the image defogging algorithm based on the ambient light variation, which is used to carry out the optimization processing of images. Aiming at the lack of the user’s visual cognitive semantics of color matching in the process of color design, it is proposed to construct a dynamic color design model based on the theoretical foundation of the image translation model, combined with the visual aesthetics data flow. The model obtains the user’s visual aesthetic cognition of the optimized image samples through eye tracking technology and, at the same time, applies the image translation model to the color design process to generate a color design scheme that is more in line with the human visual characteristics by learning the color matching law of the samples and their visual cognitive semantics. The experimental analysis shows that the image-defogging algorithm based on ambient light variation has a better image-processing effect and operation efficiency, exhibiting a higher average gradient (11.83), information entropy (7.86) and visual fidelity (2.99). Meanwhile, the color design scheme generated by the method of this paper is highly recognized by the subjects, with evaluation scores of more than 4 points in each dimension. The dynamic color design model constructed in this paper, by considering ambient light changes, can generate color schemes that are both rational and comfortable.
Research on Optimization and Control Technology of Transmission Grid for Low Carbon Economy
Authors: Yang Peng , Dong Jingyuan , Rong Shiyang , Liang Jifeng , Wang Huijuan 
Published: October 2024

Abstract
The low-carbon upgrading of transmission grids can be effectively realized by trend optimization techniques. This paper improves the traditional trend calculation based on the distributed power supply controlled by droops. Each node’s power balance equations are outlined based on the established distributed power supply with droop control and other models. The improved Niu La method is used to establish the trend calculation model, and the active sag coefficient and reactive sag coefficient in the droop control are normalized. Virtual impedance is utilized to regulate the reactive power classification between distributed power sources in the transmission network to minimize transmission network losses. The carbon and economic benefits of this paper’s method are analyzed using the MATLAB platform, and the carbon emissions of line and node active losses are reduced by 72.30% and 50.67%, respectively, before and after the optimal flow control. The economic benefits of saving line losses after performing optimization and control are 4.8739 × 105 yuan, 7.8593 × 105 yuan and 1.1283 × 106 yuan, respectively.
Practical Exploration of Big Data Technology in Improving the Efficiency of English Education Resource Allocation and Utilization
Authors: Liu Li 
Published: October 2024

Abstract
In order to make full use of educational big data for English education resource allocation and utilization, this paper is based on the standard Bottle Sheath Optimization Algorithm, which generates a random number through the sine-cosine function to update the random positions of individual Bottle Sheaths to improve the randomness and population diversity of the leader search process, improve the Bottle Sheath position updating method, and enhance the algorithm’s ability to perform global search. This paper develops a resource allocation model that can be employed for English education resource allocation. For the vast majority of single-peak and multi-peak functions, the success rate of SCSSA’s solution is 100%, and the performance of SCSSA’s search is significantly better than that of the comparison algorithms (p<0.05), and all of its convergence curves have the fastest decline. After using the model for allocation, the student-teacher ratio of the five districts and counties was improved, with a cumulative decrease of 86.72% in the student-teacher ratio, and the gap in the student-teacher ratio between districts and counties was reduced. However, the model’s optimization effect on the allocation of English books is limited, suggesting that further support of human and financial resources is needed in the allocation process.
Redundancy Analysis in the Translation of Scientific and Technical English Texts Combined with Information Entropy
Authors: Jiao Yuling , Duan Liqing 
Published: October 2024

Abstract
Insufficient redundancy or excessive redundancy of information in parallel corpora can interfere with the semantic relevance analysis in the process of machine translation and affect the quality of machine translation. For this reason, a neural machine translation model has been designed that incorporates information entropy to determine base weights and SVM to eliminate redundant samples for scientific and technical English texts. The results of simulation experiments show that the present method (RNNSearch +IE+ SVM) improves the BLEU value by 1.06 BLEUs compared to the baseline model in the English-German translation task. SVM excels at parsing both under-redundancy and over-redundancy in binary classification experiments for similarity. The research has the potential to significantly enhance the efficiency of neural machine translation for scientific and technical English texts and achieve excellent experimental results, paving the way for new ideas and methods for neural machine translation research.
Research on the integrated structure of electrical automation AC drives and analysis of equipment reliability
Authors: Fu Weiqin 
Published: October 2024

Abstract
This paper is based on the theoretical knowledge of electrical automation and the composition and basic principles of AC drive systems to determine the overall design of the system integration structure in this paper. The reliability of the system equipment is assessed jointly by combining the Weibull distribution, least-squares regression fitting, and Bayesian network after completing the system integration design. The results show that the fitted regression equation for the equipment failure data is y = 0.3372x − 3.0521, which explains that this data can be used for reliability analysis. The highest a posteriori probability value (0.00899) for the AC motor in the Bayesian network model of the AC drive system indicates that the AC motor should be prioritized for overhauling in the course of system failure to achieve the purpose of intelligent detection and control of the system.
Human Resource Management Impacts on Real Estate Banking
Authors: Sarihaddu Mario Neel Kamal , Linde Ivars 
Published: October 2024

Abstract
Human resource management helps real estate banking by guaranteeing that the particular organization has highly skilled workers, which helps to accomplish strategic goals. The research aimed at examining the human resource management factors affecting real estate banking to increase efficiency and profitability. This research provides valuable information regarding how human resource management practices enhance performance and innovation in real estate banking. The authors adopted a quantitative methodology to analyse a large sample population. To gain a detailed understanding of the investigated topic, a digital survey was conducted to acquire primary data, and a literature review analysis was performed to gather secondary data. The sampling method opted for was convenience sampling, and a total of 416 individuals participated. The gathered data was then analysed using Chi-squared test and Spearman correlation to identify the relationship. The identified factors are technological aspects (0.790), talent management (0.780), recruitment and selection (0.710), training and development (0.580), employee engagement (0.520), and reward and recognition (0.505).
Application of Gan Embroidery (Yuzhang Embroidery) in Neo-Chinese-style Woolen Clothing with the Inheritance of Intangible Cultural Heritage
Authors: Lin Yanping , Li Hongkun , Dai Ying , Zhang Zhenzhen , Yang Chen , Zhang Liyang 
Published: October 2024

Abstract
This paper investigates the emergence of Neo-Chinese-style clothing and the integration of Gan Embroidery techniques in the design of Neo-Chinese-style woolen clothing in the context of intangible cultural heritage. Neo-Chinese-style clothing represents a contemporary evolution of traditional Chinese attire, amalgamating elements of Chinese traditional dress culture and making a notable presence in the modern fashion industry. This study initiates by presenting the current status of Neo-Chinese-style clothing, encompassing its styles, patterns, techniques, and its influence on the international fashion stage. Subsequently, this research places a significant emphasis on the embroidery craftsmanship within the design of Neo-Chinese-style woolen clothing, emphasizing the importance of embroidery in conferring distinctive three-dimensionality and cultural aesthetics to the clothing. Through the synthesis and analysis of design cases from both domestic and international brands, this study illustrates the application of Gan Embroidery in Neo-Chinese-style clothing design. Finally, this research summarizes the primary contributions of the study, including an enhanced comprehension of the development of Neo-Chinese-style clothing and the burgeoning role of embroidery craftsmanship in the international fashion industry. Additionally, this research explores potential future research directions to deepen our understanding of Neo-Chinese-style clothing and traditional craftsmanship.
Forecasting the Profitability of the Textile Sector in Emerging European Countries Using Artificial Neural Networks
Authors: Pîrvu Daniela , Bondoc Maria-Daniela , Apostol Luiza Mădălina 
Published: October 2024

Abstract
This study analyzes a set of key performance indicators for listed companies in the textile industry in emerging European countries: EBITDA margin, operating margin, pretax ROA, pretax ROE. Several statistical-econometric methods (dynamics analysis, structural analysis and regression) were used to provide an overview of the evolution of the public companies studied for the period 2012–2022, as well as a number of forecasts for the period 2023–2025. GMDH Shell software was used for public companies' pretax ROA forecast analysis in the textile industry in emerging European countries. The factor regression models that were constructed are valid for eight of the nine countries studied.
The role of linguistic perspectives in the language description
Authors: Sebők Szilárd 
Published: October 2024

Abstract
The paper discusses the language of linguistic description (metalanguage), in particular the question of how the language of description can influence the language that is the object of the description. I approach the topic from the aspect of the use of linguistic terms and their personification. I summarize the results of my three previous case studies, in which I tracked the formulations associated with the selected terms. I focused on the actions that, according to the literal understanding of the formulations, are performed by the terms. Based on these case studies, I concluded that the more frequently a term is used, the more likely it is that it will “perform” an activity and, if this happens, the further away they go, the more their “competencies” expand, i.e. they perform progressively more complex acts. I have characterized this phenomenon as the perspective through which we view linguistic reality through terms (the perspective of the term), and in contrast to it, I have presented the speaker’s perspective, through which linguistic reality is perceived from the perspective of everyday speakers. I have pointed out that the perspective of the term can affect the description of language in a way that shifts the linguist’s attention from the real behaviour of speakers to the “behaviour” of abstract linguistic entities.
Estimation of Blood Calcium and Potassium Values from ECG Records
Authors: Babur Sebahattin , Moghaddamnia Sanam , Bozkurt Mehmet Recep 
Published: October 2024

Abstract
The identification of diseases caused by changes in ion concentration is quite difficult and yet plays a decisive role in the success of clinical care, diagnosis and treatment. The clinically proven approach to diagnosing electrolyte concentration imbalance is blood tests. There is a need to provide a non-invasive diagnostic method that is not of a temporary nature. Bio-signals such as the electrocardiogram (ECG) can be used to meet this demand and become diagnostic tools that facilitate home monitoring of electrolyte concentration on a permanent basis. This study investigates the feasibility and efficiency of methods based on machine learning (ML) and ECG recordings in monitoring critical levels of existing potassium and calcium concentration. Morphological, frequency and frequency-time domain features were extracted to automatically estimate calcium and potassium levels. Furthermore, the potential of estimates based on modeling approaches will be demonstrated to gain insights into relevant clinical findings and improve the performance of monitoring approaches. Using the hold-out validation method, the best results in terms of mean square error (MSE) and R for estimating the calcium value are 0.7157 and 0.57347, using fuzzy inference systems (FIS). Here, R represents the proportion of the variance in the calcium value that is explained by the model.
The Interpretation of Public Administration Controlling From an Economic Perspective
Authors: Kerekes Etelka , Szőke Brigitta , Módos-Szalai Szilvia , Malatyinszki Szilárd 
Published: October 2024

Objective: The primary objective of this study is to assess the adherence of state/municipal economic companies to the requirements outlined in Government Decree 339/2019 (XII. 23.) concerning the use of budgetary funds. Furthermore, the study aims to evaluate the effectiveness of internal and external control systems and propose measures to enhance the efficiency and transparency of these companies' financial management. Theoretical Framework: The research builds on the principles of public sector governance, focusing on the role of internal control systems and external monitoring in ensuring the proper use of public funds. Government Decree 339/2019 serves as the regulatory basis for evaluating performance, while theories on financial control, transparency, and accountability in public administration are employed to interpret the findings. Method: A qualitative approach was employed, including an audit of selected state/municipal companies. The study utilized data from the ÁSZ (State Audit Office) reports, internal control mechanisms, and management reviews. Performance evaluations were conducted in light of the companies' compliance with regulatory requirements, with a focus on identifying systemic shortcomings and proposing improvements. Results and Discussion: The audit revealed that while the companies generally complied with Government Decree 339/2019, several shortcomings were noted, particularly in the areas of internal control and economic management. External monitoring by the ÁSZ and existing internal control systems contribute to ensuring the proper use of budgetary funds. However, there is a need to strengthen these systems further to address the observed issues. The study suggests the implementation of a management-oriented controlling system, which would emphasize economic decision-making and improve overall performance and transparency. Research Implications: The findings have significant implications for state and municipal economic companies. By adopting a more economically focused internal control system, these entities can enhance their financial management practices, optimize the use of public funds, and foster greater transparency and efficiency in their operations. This approach would not only benefit public administration but also improve service delivery to the general public. Originality/Value: This study contributes to the existing literature by highlighting the importance of a management-oriented controlling system based on economic principles in the public sector. It provides practical recommendations for enhancing the internal control and audit processes of state/municipal companies, offering valuable insights for policymakers and public administration professionals striving for greater fiscal responsibility and transparency.
Challenges and Opportunities of Artificial Intelligence in Public Education: A Case Study in Barão Dos Cocais – MG
Authors: Souza Érika Márcia Assis de , Fabri Erika Silva , Araújo Wagner dos Reis Marques , Souza Wagner Cavalare de , Resende Sara Isabel de Melo , Diniz Hélio Augusto Goulart 
Published: October 2024

Objective: Considering the use of Artificial Intelligence (AI) by teachers in public high schools in Barão de Cocais faces challenges, such as the lack of specific training and inadequate infrastructure, along with concerns about pedagogical autonomy, the objective is to investigate the impacts of these technological tools on the teaching-learning process.

Theoretical Framework: This research presents the main concepts and theories that underlie the work. The use of artificial intelligence in public education, the ethical and pedagogical challenges of using artificial intelligence in teaching and the infrastructure and public policies for artificial intelligence in education stand out, providing a solid basis for understanding the context of the investigation.

Method: A qualitative methodology is applied, based on semi-structured interviews with teachers, aiming to capture their experiences and perceptions regarding the use of AI in their pedagogical practices.

Results and Discussion: The results obtained revealed that it is essential to incorporate digital skills development into teacher training curricula, in addition to promoting public policies that encourage the conscious and appropriate use of AI in the educational context.

Research Implications: Content analysis allows the identification of patterns and recurring themes, providing a broad understanding of the implications of this technology in education. It is observed that AI is seen by teachers as a promising tool, particularly for automating tasks and personalizing learning. However, there is a significant gap in teacher training and in the schools’ infrastructure, hindering more efficient adoption.

Originality/Value: This study contributes to the emerging literature on the implementation of AI in Brazilian public education, offering valuable insights into the challenges and opportunities surrounding the adoption of these technologies in educational institutions in specific regions, such as Barão de Cocais.