bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Activity of Styrax tonkinensis Essential Oil in vitro and in situ
Authors: Verešová Andrea , Čmiková Natália , Vukic Milena , Ban Zhaojun , Elizondo-Luevano Joel H. , Kačániová Miroslava
Published: October 2024

Among the genera in this family, Styrax is unique in that it yields benzoin resin, a resinous substance. Usually, when sharp items lacerate the bark, this resin is secreted. Because of its fragrant qualities, it has been used for centuries in cosmetics and fragrances all over the world. Additionally, Styrax species have long been employed in herbal remedies for a variety of ailments. The antibacterial characteristics of Styrax tonkinensis essential oil (STEO) were the focus of this study. The antimicrobial activity was compared to bacteria, both Gram-positive (G+) and Gram-negative (G-), using inhibition zones in agar media, minimum inhibitory concentration (MIC) bioassays and in vapour phase on fruits and vegetables model. The findings showed that STEO was very successful in inhibiting bacteria that were G+ (Bacillus sutbtilis subsp. Spizizenii CCM 1999, Bacillus thuringiensis CCM 19, and Priestia (Bacillus) megaterium CCM 2007) as well as G- (Citrobacter koseri CCM 2535, Enterobacter aerogenes CCM 2531, Escherichia coli CCM 3954). The range of maximal inhibition zones and MIC values was determined to be 4.67 to 8.33 mm and 3.49 to 7.71 mg.mL-1, respectively. Furthermore, B. thiriangensis, P. megaterium, and E. coli were all susceptible to the antimicrobial effects of the (STEO) on the fruit and vegetable model. According to research findings, STEO is a valuable source of organic chemicals that have the potential to be innovative antibacterial agents against microbes.
A Study on the Impact of Economic Policies on Consumers’ Behavioral Intentions on Cross-border E-commerce Platforms
Authors: Jiang Meixia , Yu Jing , Zhao Jing
Published: October 2024

Given the expanding high-level opening to the outside world, there is a potential correlation between consumers’ behavioral patterns on cross-border e-commerce platforms and economic policies. This paper proposes a model for extracting consumer behavioral willingness features based on a convolutional neural network with sentence vector improvements, which optimizes the effect of cluster analysis on consumer behavioral willingness. The extraction model is utilized to extract consumer behavioral willingness features, and the outcomes demonstrate that the proportion of consumption and recommendation willingness is 47.65% and 52.05%, respectively, with a positive clustering effect. The regression results show that consumers’ behavioral willingness to cross-border e-commerce platforms increases with their preference for free trade and mixed trade policies, with an impact coefficient of 0.095 and 0.096. The degree of free trade simultaneously influences consumers’ willingness to recommend, with an impact coefficient of 0.154. However, the import substitution policy has an impact coefficient of −0.078, indicating that consumers are less likely to recommend cross-border e-commerce platforms to others.
Optimization of Higher Education Teaching Method System Based on Edge Intelligence
Authors: Wang Sheng , Zhang Shanjun
Published: October 2024

Nowadays, educational information technology is widely used in higher education teaching, but various teaching resources are not reasonably utilized and effectively managed. In order to solve such problems, this paper introduces edge computing in the basic cloud computing environment to innovate and optimize the multimedia teaching mode of higher education, proposes a VR edge cloud classroom teaching mode, and proposes a collaborative transmission and resource allocation strategy based on DIBR vision synthesis technology to optimize the teaching experience. The VR Edge Cloud Classroom teaching model is designed from three aspects: teaching methods, teaching content, and teaching evaluation. Through practical teaching, students believe that the advantages of this teaching mode are mainly rich in learning resources, conducive to learning-related knowledge and that they will achieve gains in expanding their knowledge, improving their independent learning ability and cultivating their analytical and problem-solving ability. Comparing and analyzing the teaching effect of VR edge cloud classrooms with the traditional teaching effect, there is a significant difference between the two in terms of the achievement of the total objectives (P=0.015<0.05). The teaching effect of a VR edge cloud classroom is significantly better than that of a traditional classroom.
Simulation study of military command and control system based on system dynamics
Authors: Ha Da
Published: October 2024

The command and control system, as the “central nervous system” of the military combat forces, directly affects the combat effectiveness of the troops and plays a vital role in the success or failure of the combat. Based on the characteristics and requirements of military command, this paper constructs a simulation control system for military command based on a system dynamics model. It discusses the setting of relevant essential parameters. Simulation system analysis results show that the red side uses the simulation system for command business processing times to obtain an advantage in military combat. The military combat victory was achieved by losing about one-third of the combat force (333C) on the 29th day. In addition, the rate of military coordination in the simulation system is positively proportional to the number of correctly arranged combatants, and the rate of military intelligence collection has a more significant impact on the completeness of the development of military command programs in the initial period. The simulation system proposed in this paper can meet the needs of military command and control systems.
Research on the Model of Shooting Probability in Basketball Sports Competition
Authors: Chen Guining
Published: October 2024

This paper combines the regression model based on nonlinear least partial squares and the decision tree model to more comprehensively excavate the influencing factors of the shooting percentage of basketball games from the perspective of both basic data and higher-order data. It also calculates the weight coefficients and comprehensive scores of the indicators of the four teams’ shooting techniques in the restricted area through the fuzzy comprehensive evaluation method and studies the hitting rate of different shooting methods. After analysis, the regression coefficients of core strength training and core strength training time are 1.325 and 1.086 in the basic data and high-order data, respectively, which have a significant positive influence on the winning and losing of the tournament, and the decision tree intuitively demonstrates that the core strength training is a key factor in the hitting rate of the game shooting. The weight coefficient of “rebound” is the highest, with a weight coefficient of 20.0271, which has the greatest influence on the shooting percentage, and “traveling backhand shot” has the least influence on the shooting percentage.
Research on the Quality Improvement of Automatic Tone Adjustment Technology for Movie Images Using Computer Vision Algorithms
Authors: Cui Bingshuoshi
Published: October 2024

Color plays an indispensable role in the formation of movie style, and hue, as an important part of color, plays a pivotal role in the study of the quality of movie pictures. The study is based on the basic theory of the color and color space of the movie picture, equalizing the histogram of the movie picture, and then combining the HistoGAN network and Laplace algorithm of computer vision algorithms to realize the enhancement of the hue of the movie picture, and then adjusting and improving the quality of the movie picture automatically. Taking 10 comedy movies by Wes Anderson as an example, the movie tones are measured and counted. According to the results, the movie screen tones adjusted have a strong ability to convey image details and have better contrast and clarity enhancement effects while maintaining image fidelity. As the year progresses, the brightness of the adjacent period in Wes Anderson’s comedy movies becomes more frequent and larger after the movie screen hue is adjusted. The overall of his movies is the color matching law of warm tones with larger saturation with cooler tones with smaller saturation, and the hue adjustment gives the movie screen a more advanced sense of beauty.
Using Big Data Analysis to Study Pedagogical Strategies for Conjunctive Bisyllabic Verb Errors in Chinese Second Language Learners
Authors: Song Jingjing , Zhang Xinying
Published: October 2024

Conjugate diphthongs are a class of words with specific metrical morphology and stylistic features in the written formal corpus of modern Chinese, which need to be combined with a disyllabic constituent in the bare-bones form to form the syllable pattern of “double+double,” which has been under-attended by the teaching community. Within the framework of metrical grammar and stylistic grammar, we statistically analyze the usage bias of conjunctive bisyllabic verbs in the HSK Dynamic Composition Corpus and find that these kinds of words, due to their metrical patterns, stylistic functions, and relatively abstract semantic features, cause a large proportion of metrical syllabic biases that are easy to gather. The bias is caused by both the difficulty of vocabulary acquisition and issues with textbooks and teaching, as well as subjective reasons like learning strategies. In this regard, it is suggested that existing textbooks should be adapted, teachers’ teaching methods should be improved, and learners’ morphological awareness should be enhanced.
Study on the effect of dissemination of Luo and Yue rice culture in multimedia technology-assisted rural cultural revitalization
Authors: Mou Lian
Published: October 2024

Algorithmic recommendation is an important means of using multimedia technology for cultural dissemination and helping rural cultural revitalization. In order to realize the effective dissemination of Luo Yue rice culture, this paper constructs a hybrid recommendation algorithm that recommends videos about Luo Yue rice culture according to user preferences, which uses the cultural content relationship derived from LDA to replace the recommendation list generated by collaborative filtering and crosses it with the historical rating recommendation list to solve the problems of recommendation accuracy and cold start. The analysis of the dissemination effect shows that the average MAE value of this paper’s algorithm is 0.671, the proportion of users who watch relevant videos “occasionally” and more frequently has increased by 5.84%, and users recognize the recommendation method. The algorithm proposed in this paper has a good recommendation performance and helps to disseminate rural culture, as demonstrated by the above results.
Impact of combining party building activities and digital transformation on distribution system performance
Authors: Zhang Hui
Published: October 2024

How to effectively combine modern digital technology and the results of the experience accumulated in the traditional party building work, effectively solve the problems existing in the traditional party building work, so that the party building work carried out transparently and efficiently has become a new issue and challenge. In this paper, the overall framework of an intelligent party building system is designed based on the necessity of digital transformation of building activities and performance requirements. The system is divided into four modules: party building management, service management, statistical analysis, and system management, and the functions of each module have been designed and integrated. Based on the principles of database design, a conceptual structure design is proposed, and the association rules and FP-growth algorithm are utilized to perform association mining on party-building activity data. The results of the association rule mining test show that the fluctuation range of confidence and support is between [0.9148, 0.9587] and [0.9048, 0.9348], respectively, and the system passed the association rule mining test. The majority of people believe that using the party building information platform is beneficial for conducting party-building activities. 80% of the users think that the party building system platform is better and meets their needs for use.
An analysis of the impact of content-generation-based AI design tools on the visual arts
Authors: Pan Bo , Ke Yukai
Published: October 2024

With the development of artificial intelligence, the use of generative adversarial networks in deep learning can generate good visual art based on content, and at the same time, it can reduce the problems of noisy texture, transition migration, and image distortion that exist in content-based image generation. The article proposes a multi-view image generation architecture (DrawGAN) based on generative adversarial networks and builds an AI design tool on this basis to explore the effect and analysis of the design tool for content generation based on generative adversarial networks on visual art. Through experimental testing, the model proposed in this paper, after the constraints of the relevant loss function, the generated image content is compatible with the input image, the style is consistent with the style of the target image, and the image generated by the base DrawGAN has a high degree of reproducibility. In addition, the average score of visual art images generated using the model proposed in this paper is 8.25, which is 2.26 points higher than that of images generated by traditional methods. To sum up, the DrawGAN model proposed in this paper is a valuable tool in the generation of visual art images.
Research on Innovation and Entrepreneurship Talent Cultivation Mode Innovation with Artificial Intelligence Technical Support
Authors: Guo Xiaochun
Published: October 2024

With the rapid development of artificial intelligence technology, colleges and universities pay more and more attention to innovation and entrepreneurship education. The path of artificial intelligence technology is proposed to empower the high-quality development of innovation and entrepreneurship education in colleges and universities from multiple perspectives. An optimization strategy is proposed for an innovation and entrepreneurship education resource-sharing platform based on a collaborative filtering algorithm. The proposal is for a collaborative filtering recommendation algorithm (CFUR) that is based on user feature weight confidence. Finally, experiments have shown that the EP value of the improved algorithm in this paper is significantly higher than that of other recommendation algorithms. Through teaching practice, students’ satisfaction with the educational resource-sharing platform based on the improved algorithm outperforms that of the traditional teaching method, and the mean value of test scores for innovation and entrepreneurship ability surpasses that of the traditional teaching class by 17.07. It shows that the optimized resource-sharing platform has a significant impact on students’ innovation and entrepreneurship abilities, which helps to cultivate innovative and entrepreneurial talents.
Deep reinforcement learning based reactive power regulation and its optimization in power grids
Authors: Zhou Yi , Zhou Liangcai , Sheng Xu , Gu Dongjian , Shen Weijian , Chen Qing
Published: October 2024

The study applies the Markov game to grid reactive power regulation based on deep reinforcement learning theory, constructs the Markov game for grid optimization problems, and optimizes it using the HAPPO algorithm to explore real-time grid optimization strategy based on multi-intelligence body reinforcement learning. On the basis of the optimization strategy, the grid power management method based on deep reinforcement learning is explored through the Markov decision process and the improved deep deterministic policy gradient method, and the grid operation optimization model based on deep reinforcement learning is constructed. The model is then examined in terms of arithmetic cases. The maximum error of the model in this paper is less than 5%, and the accuracy of the fitting is high. The node voltage has a maximum voltage offset of 0.0025, resulting in high voltage quality. The real-time optimization solves for an average voltage offset that is 97.9% lower and a maximum voltage offset that is 75.4% lower compared to the long-term scale reactive power optimization. The average running cost and standard deviation of the model increase with greater communication impairment. The model approach in this paper performs the best in terms of optimization cost, reducing it by 1.12%, 6.67%, 10.93%, and 0.94% compared to the other four approaches.
The Influence of Folk Sports Tourism on the Construction of City Brand Image
Authors: Zhang Xuchang , Wu Wenqiang
Published: October 2024

Folk sports, as a carrier of inheriting and promoting national culture, can not only show the unique charm of the city but also enhance the city’s popularity and reputation, providing a new opportunity for city brand building. To explore the relationship between folk sports tourism and city brand image, this paper uses the factor analysis method to extract the male factors that affect the city brand image. The model is validated through various validations, and finally, the components of the brand image of the tourism city are determined. Among them, “city tourism function image” and “city culture” are closely related to the development of folk sports tourism. Based on the Stimulus-Organism-Response (S-OR) model, we constructed a model of the influence mechanism of folklore sports perception factors on city brand image and city brand identity. Relevant hypotheses are proposed to explore the relationship between the four factors: folk sports perception factors, immersion experience, city brand image, and city brand identity. Finally, the research findings are rationally explained in the light of reality, and relevant suggestions are put forward for the construction of a city’s brand image.
Role Analysis of the Construction and Application of Folk Dance Knowledge Graph in Cultural Transmission
Authors: Zhou Wenjing , Chen Weigen
Published: October 2024

This paper first introduces ontology construction, knowledge extraction, and graph database in the process of knowledge graph construction based on folk dance. Secondly, the semantic similarity algorithm is used as the basis to construct the conceptual similarity (MD4) model, and the five types of similarity algorithms in the MD4 model are introduced. After that, based on this model, we will conduct a comparative study on the number of folk dance publications, keywords, and hotspots in China and abroad. Finally, suggestions are made for creating a knowledge graph that is based on folk dance. The annual volume of folk dance research published in China is generally increasing. In contrast, the amount of foreign publications on folk dance is generally low, and its maximum number of publications is only 67. In China, 1021 keywords finally got 156 nodes, and the total link strength of the total number of occurrences was 3942. 1352 keywords finally got 137 nodes, and the total link strength of the total number of occurrences was 1983. Among all the keywords in China, the top three co-occurrence-ranked keywords and hot words were folk dance, value, and development status, with a number of 817, 633, and 607, respectively. The foreign countries include folk dance (453), value (233), and performance (146). The cultural inheritance of folk dance can be greatly impacted by the knowledge map’s ability to summarize the development of folk dance.
A Study on the Dual Benefits of ESG Practices on Corporate Sustainability and Investment Returns Based on Big Data Analytics
Authors: Shen Wangqi
Published: October 2024

With the development of the green economy and the increasingly serious global ecological problems, the ESG concept is rapidly emerging. This paper proposes an evaluation model of corporate ESG sustainable development performance and investment efficiency based on big data analysis, which provides an in-depth discussion on the dual benefits of corporate sustainable development and investment return. Based on big data, the weights of corporate environmental E, social responsibility S, and corporate governance G rating values are defined, and the evaluation index system of corporate sustainable development performance is constructed through machine learning modeling technology to establish the corporate ESG portrait model. Based on the DEA evaluation model, input and output indicators are selected to construct the investment efficiency evaluation index system. The ESG sustainable development performance scores of enterprises in different industries have obvious differences, with the difference between the largest and smallest values being greater than 60 or more. In 2019-2023, listed companies in China had a sustainable development performance of 0.32, 0.37, 0.41, 0.37, 0.44, which showed an upward trend. Except for 2019, the annual investment returns of companies under ESG practices in 2020-2023 outperformed the CSI 300 index, reaching 17.75%, 28.32%, and 36.27%, respectively.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.
DEFORMASI POSTSESISMIC DAN INTERSEISMIC SECARA SPASIAL DAERAH BENGKULU DENGAN PENGAMATAN GPS PERIODE 2007-2016
Authors: Ashar Muda Lubis  , Edi Purwanto
Published: October 2024

Daerah Bengkulu merupakan daerah yang rawan gempa bumi. Gempa bumi besar terakhir terjadi di daerah ini pada 12 September 2007 (Mw 8.4), setelah sebelumnya juga terjadi pada 4 Juni 2000 (Mw 7.9). Mengingat fenomena ini sering berulang, maka pengamatan terhadap proses siklus gempa bumi sangat penting untuk dilakukan. Siklus ini meliputi fase interseismic, preseismic, coseismic, postseismic dan kembali ke fase interseismic. Penelitian bertujuan untuk menentukan segmentasi deformasi postseismic dan interseismic di daerah Bengkulu dengan memanfaatkan teknologi Global Positioning System (GPS). Data GPS tahun 2007-2016 dari stasiun GPS KRUI, MNNA, SLMA, CBKL, UNBE, LAIS, dan MKMK diolah dengan menggunakan perangkat GAMIT/GLOBK. Hasil pengamatan menunjukkan bahwa deformasi postseismic terjadi pada stasiun MKMK dan LAIS. Sebaliknya deformasi interseismic terjadi pada stasiun CBKL, UNBE, SLMA, MNNA dan KRUI. Selanjutnya, stasiun LAIS mengalami deformasi postseismic yang paling besar setelah gempa bumi 12 September 2007 (Mw 8.4) yaitu 21.3 cm menuju arah barat daya.  Sebaliknya, stasiun MNNA mengalami deformasi interseismic yang paling besar yaitu 13.5 cm menuju arah timur laut. Dari penelitian ini ditemukan bahwa daerah segmentasi postseismic dan interseismic berada diantara daerah Lais dan daerah Kota Bengkulu. Penelitan secara kontinu diperlukan untuk memahami fase siklus gempa bumi yang lebih baik untuk keperluan mitigasi bencana gempa bumi di daerah Bengkulu.
Arthroscopic meniscus transplant using autologous semitendinosus with bone marrow aspirate-derived fibrin clot along with anterior cruciate ligament reconstruction – A case report
Authors: Gupta Himanshu , Jhamb Jai Aditya , Das Joydeep , Mishra Pallav
Published: October 2024

Subtotal and total meniscectomy is known to advance osteoarthritis in patients. Meniscus transplant using semitendinosus autograft is a viable option in young patients. We have presented the early follow-up, along with technical video, for arthroscopic meniscus transplant with autologous semitendinosus graft with bone marrow aspirate-derived fibrin clot along with anterior cruciate ligament (ACL) reconstruction, in the primary surgery, in a patient of ACL tear along with near-complete loss of medial meniscus due to auto-meniscectomy. The patient showed good functional and radiological outcome at 9 months.