bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of College Students’ Consumption Behavior Characteristics Based on ADDIE Model under the Background of Ideological and Political Education
Authors: He Zihao 
Published: February 2024

Abstract
This paper provides an in-depth study of the consumption characteristics of college students, which is not only related to the high-quality development of the economy, but also vital to students’ physical and mental health. The study uses the ADDIE teaching model, combined with the Apriori association algorithm and the K-means++ algorithm to analyze the intrinsic correlation of college students’ consumption behaviors. While applying the traditional association rule’s confidence and enhancement calculation methods, the study adopts a horizontal weighting method with downward closure nature to obtain the weights of the maximum weighted items in the item set. In addition, based on the distance between the initial clustering center and the sample point, the probability of the sample point becoming the next clustering center is calculated to enhance the clustering effect. After the model is built, the performance test is conducted firstly. Then the ADDIE-based model for teaching Civics in colleges and universities is designed and analyzed using the model of this study. It is found that after the occurrence of follower consumption behavior (C), there is a 100% probability of shifting to differentiated consumption (E). There is a 94.7% likelihood that personalized consumption will occur after the follower consumption behavior (C). There is an 85.67% likelihood of excellence for those students who spend low amounts of money, less frequently, and more off-campus. The survey revealed that 11.57% of the student population falls under this consumption profile. These findings have important implications for understanding the consumption behavior of college students and its impact on education and the economy.
Research on the Influencing Factors and Countermeasures of Cultivating Financial Innovation Talents under the Background of Big Data
Authors: Xu Feng 
Published: February 2024

Abstract
The lack of high-quality financial innovation talents has become an obvious shortcoming in the development of financial innovation, and the call for the cultivation of talents to adapt to the increasingly changing new economic environment has become increasingly urgent. This study extracted the key factors affecting the cultivation of financial innovation talents by preprocessing the relevant data and eliminating the unreasonable information, and used factor analysis to remove the key factors influencing the cultivation of financial innovation talents, and used Pearson’s correlation coefficient method and correlation rule analysis algorithm to analyze the interrelationships between these factors in depth. The analysis results show that the correlation coefficients between the level of economic development and educational background and technological innovation reach 0.69 and 0.58, respectively, while the correlation coefficient between the level of human capital and the level of technological innovation is 0.46. Analyzing the level of financial innovation talent cultivation, we find that financial innovation development grows from about 22.5% to about 37.5% from 2016 to 2023. At the same time the amount of financial innovation talent grows from 490,000 to 880,000 people. However, the growth of financial innovation talent is gradually slowing down, pointing to the urgent need to develop talent. The strategies and suggestions put forward in this study provide essential references for cultivating financial innovation talents in the future, aiming to promote the sustainable development of financial innovation and the optimization of talent structure.
Construction of Physical Education Teaching Reform Innovation Guarantee System Based on Multivariate Statistical Analysis
Authors: Yang Hong 
Published: February 2024

Abstract
To promote the innovative reform of physical education teaching, this paper adopts the multivariate statistical analysis method to construct a set of multivariate evaluation system. A comprehensive assessment and classification of teaching quality was realized through principal component analysis, flower-pollinated cluster analysis and robust factor analysis model. The teaching quality assessment system of the physical education option course designed in this study includes four primary indicators and 13 secondary indicators. The results showed significant differences in teaching quality among teachers, and the three retained principal factors could reflect 78.15241% of the student performance information, which provided a vital quality control reference for physical education teaching.
Innovation and Informatization of Civics Teaching Mode in Colleges and Universities under Integrated Transfer Learning Strategies
Authors: Zou Liegang 
Published: February 2024

Abstract
This study addresses the problem of predicting student performance by proposing a heterogeneous transfer learning method based on FMBCC-FT, which aims to accurately predict students’ Civics knowledge mastery. The study achieves effective knowledge transfer by defining university Civics teaching as the source domain and considering the university’s demand for students’ Civics dynamics as the target domain. After collecting and standardizing students’ Civics education data, this study constructed a knowledge base model of Civics teaching based on the source domain data and migrated it to the target domain. To compensate for the bias in the migration, the study designed a three-stage iterative optimization migration weight learning method, which significantly improved the quality of Civics teaching by updating the parameter weights of the source domain model. The process was verified through the experimental control group pre and post-tests. The results showed that the total score of critical thinking ability pre and post-tests was −3.39. The students’ innovative consciousness score in the experimental group was 3.96, which was significantly higher than that of the control group, which was 0.18. In addition, the scores of the quality dimensions of the students in the control group and the experimental group ranged from 3.22 to 3.40 and 3.43 to 3.68, respectively. This empirical result effectively confirms that the Civics teaching model proposed in this paper can effectively improve students’ critical thinking ability, innovation consciousness and overall quality, which is of great guiding significance to the current practice of Civics teaching in colleges and universities.
Research on key technology of transmission and OPGW line hidden danger prediction based on neural network
Authors: Wang Xin , Liang Gang , Li Qing , Cui Limin , Hu Changyue , Wang Xiaozhen 
Published: February 2024

Abstract
This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research basis. A Faster-R-CNN network structure model is constructed to realize end-to-end target detection by combining RPN and Fast-R-CNN network structure. To further improve the detection accuracy, the BAM algorithm is introduced to enhance the Faster-R-CNN, to realize the accurate detection of hidden dangers in transmission and OPGW lines. This paper also compares the performance of the traditional and improved algorithms, and explores the practical application effect of the constructed model in depth. The experimental results show that the enhanced Faster-R-CNN algorithm significantly improves the correctness of observation in the sky and land regions, with an average accuracy mean value of about 26%, especially when observing field villages, factories, playgrounds, urban areas and swimming pools. Therefore, the improved algorithm proposed in this study effectively enhances the detection capability and accuracy of hidden safety hazards in transmission and OPGW lines.
Research on the Interaction Design of Urban Public Environmental Facilities under the Background of Digital Intelligence
Authors: Wei Liang 
Published: February 2024

Abstract
With the in-depth development of data intelligence, the design of public environmental facilities has gone beyond mere aesthetics and practicality, and is moving towards a deeper integration with interaction design. This paper addresses this trend, discusses the strategies and principles of interaction design for public environmental facilities, and proposes an innovative single-facility siting problem model (MP) and multi-facility siting problem model (MWP). The article solves and analyzes the optimal siting problems in these models using the advanced new projection contraction algorithm (PC). To practically test the effectiveness of these designs, two sets of empirical experiments are conducted to explore them in depth. The results show that the optimized interaction design of the public facilities substantially increased the frequency of users, especially in the period from 15:00 to 15:10 on rest days, the number of users was as high as 43, of which the highest number of users was 46, mainly among the middle-aged and elderly people, especially the middle-aged group. In addition, this interaction design is excellent in satisfying people’s necessary, spontaneous, and social activities, with satisfaction levels of 0.78, 0.71, and 0.62, respectively. The interaction design strategy proposed in this paper effectively enhances the efficiency of the use of the public facilities and profoundly satisfies the users’ daily needs.
Research on the artistic aesthetic value of violin performance under the integration of information technology
Authors: Du Pan 
Published: February 2024

Abstract
This study utilizes the ERP model of aesthetic cognition to reveal the neural basis of aesthetic experience, pointing out the complex cognitive processes including emotional escalation. Advanced techniques are used to pre-process violin sound signals and multiple algorithms are applied to extract critical features for accurate emotion recognition. The developed DBM-LSTM model combines the advantages of multi-layer stacked RBM and LSTM, which substantially improves emotion recognition accuracy with a success rate of more than 0.85. Finally, through a questionnaire survey, it is found that the public’s aesthetic preference of violin performance is significantly correlated with emotion perception, indicating that the more the performance conforms to the public’s aesthetics, the more it can stimulate positive emotions. This study not only provides theoretical and practical guidance on the combination of violin performance art and information technology, but also opens up a new way for the emotional communication of artistic performances and enhancing public aesthetic experience.
Innovation of Civics Teaching Mode and Informatization Teaching in Colleges and Universities Based on Knowledge Mapping System
Authors: Li Jie 
Published: February 2024

Abstract
This paper discusses the construction and application of the knowledge map of the Civics curriculum. Firstly, the courses’ ontology concept and hierarchical relationship are identified, and their attributes and relationships are defined. Then, BiLSTM-CRF model was used to extract the Civics entities, and relevant data were obtained from websites using crawler technology and regular expressions. The keyword screening technique was used to ensure the Civic politics relevance of the data. The data storage adopts RDF triples and Neo4j graph database to construct a visualization system for Civics teaching. The experimental results show that the BiLSTM-CRF model performs well in extracting Civics entities, mainly in specialized knowledge, and achieves high accuracy. The study effectively improves the efficiency and quality of Civics learning through knowledge mapping and visualization techniques.
Suggestions for Promoting a Better Public Service System under the Low Fertility Dilemma Based on VAR Modeling
Authors: Liu Xiuqing , Xu Lixin , Chen Weiwei , Wu Qingshan 
Published: February 2024

Abstract
This paper provides an in-depth analysis of the rapid decline in fertility and population aging. Using LASSO penalized Poisson regression and TVP-VAR model improved by MCMC algorithm based on Bayesian network, the factors affecting the fertility level and their interrelationships are studied. It is found that fertility and education levels affect each other with probabilities of 0.0806 and 0.0825 respectively at 10% significance level. The positive shock of social security level on fertility level increases the response value of fertility level from −1.5 to about 7.5 in 10 lags, showing a positive effect. In contrast, the response of fertility level to house prices gradually decreases from 5 in period 1 to −2.5 in period 10, indicating a negative impact. These analyses provide a scientific basis for constructing a public service fertility support system that can help mitigate the current challenges of low fertility levels.
Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity
Authors: Basharat Mahmoud , Omar Marwan 
Published: February 2024

Abstract
In the rapidly advancing digital age, the surge in malware complexity presents a formidable challenge to cybersecurity efforts, rendering traditional signature-based detection methods increasingly obsolete. These methods struggle to keep pace with the swift evolution of malware, particularly with the emergence of polymorphic and metamorphic variants designed to bypass conventional detection. This study introduces a groundbreaking approach to malware detection by utilizing GPT-2, a cutting-edge language model developed by OpenAI, specifically for the purpose of feature extraction. By applying GPT-2’s deep learning capabilities to the EMBER and Drebin datasets, this research explores the model’s effectiveness in identifying malware through the intricate patterns present in binary data. Contrary to its original design for natural language processing, GPT-2’s application in this context demonstrates a significant potential for enhancing malware detection strategies. The model’s proficiency in extracting complex features from binary sequences marks a notable advancement over traditional methods, providing a more adaptive and robust mechanism for identifying malicious software. However, the study also acknowledges the challenges associated with the interpretability of deep learning models and their susceptibility to adversarial attacks, underscoring the imperative for ongoing innovation in the field of cybersecurity. This exploration into the unconventional use of GPT-2 for feature extraction in malware detection not only showcases the model’s versatility beyond language tasks but also sets a new precedent for the application of unsupervised learning models in enhancing cybersecurity defenses.
Seeding Density and Foliar Application of a Calcium and Potassium-Based Organomineral Fertilizer in Oat Crops
Authors: Coppetti Karin , Da Silva José Antonio Gonzalez , Carvalho Ivan Ricardo , Magano Deivid Araújo , Panozzo Luís Eduardo , Da Rosa Juliana Aozane , Peter Cibele Luisa , Diel Pedro , Schünemann Lara Laís 
Published: February 2024

Purpose: To present the benefits of increasing oat sowing density on productivity, uniformity, soil quality and reduction of pesticides and the use of an organomineral product based on calcium and potassium as an alternative to lodging control, promoting more sustainable cultivation systems.

Theoretical framework: Increasing plant density can promote productivity, rapid soil coverage, reduction of weeds and greater harvest uniformity, reducing the use of pesticides. However, increasing seeding density can promote lodging. Lodging is the phenomenon in which plants leave the vertical position and fall to the ground, affecting the quality of the grains. The commercial product registered to control lodging is highly toxic to the environment. Organomineral sources with potassium and calcium represent an alternative for controlling lodging with reduced environmental impacts.

Results: Benefits of increased seeding density to promote grain yield have been found for several crop species, reinforcing the need for adapting seeding density for oat crops. The use trinexapac-ethyl as a growth regulator, it poses risks of phytotoxicity and environmental contamination. In this context, several studies have indicated that potassium and calcium favor cell wall rigidity, potentially contributing to oat stem strength.

Originality/value: This study provides significant contribution to the advancement of research on oat in Brazil, bringing new information benefiting farmers, technical assistance agents, and the food industry and improving the production system to ensure food security and care for the environment.
TINJAUAN YURIDIS PERUBAHAN PENGATURAN JAMINAN HARI TUA BERDASARKAN PERATURAN MENTERI KETENAGAKERJAAN REPUBLIK INDONESIA NOMOR 19 TAHUN 2015
Authors: Mohammad Farhan Fajari  , Andari Yurikosari  
Published: February 2024

Penerbitan Peraturan Menteri Ketenagakerjaan Nomor 2 Tahun 2022 mendapat penolakan di masyarakat. Merespon hal tersebut pada tanggal 16 Maret 2022 Menteri Ketenagakerjaan, Ida Fauziyah menjelaskan bahwa aturan pencairan Jaminan Hari Tua (JHT) dalam proses revisi, sehingga pencairan JHT saat ini mengacu pada Peraturan Menteri Ketenagakerjaan Nomor 19 Tahun 2015 (Peraturan sebelumnya). Permenaker 19/2015 setelah Permenaker 2/2022 diundangkan adalah berstatus tidak berlaku dan telah dicabut dengan Peraturan yang baru (Permenaker 2/2022), lantas bagaimana status hukum Peraturan Menteri Ketenagakerjaan Nomor 19 Tahun 2015 jika di mengacu kepada landasan dan pedoma peraturan sesuai dengan Hierarki Peraturan Perundang-undangan, Apa telah sesuai dengan Peraturan Pemerintah Republik Indonesia Nomor 12 Tahun 2011 tentang Pembentukan Peraturan Perundang-undangan. Metode penelitian yang digunakan merupakan penelitian normatif yang bersifat dekriptif dengan bersumber pada data primer dan data sekunder, yang dianalisis secara kualitatif dengan penarikan kesimpulan secara deduktif.
Comparative efficacy and therapeutic positioning of biologics in hidradenitis suppurativa: A systematic review with network meta-analysis of randomised trials
Authors: Husein-ElAhmed Husein , Husein-ElAhmed Sara 
Published: February 2024

Background
Hidradenitis suppurativa (HS) is a challenging inflammatory skin condition. Recently, many different biologics have been tested for HS, but the paucity of head-to-head comparative trials makes it difficult to determine the real value of each biological intervention. We aimed to determine the relative efficacy among biologics in treating moderate-to-severe HS throughout a network meta-analysis (NMA) and, to identify which pathogenetic pathways may be the most appropriate to target.

Methods
We comprehensively identified studies in 3 databases and clinicaltrials.gov. The eligibility criteria included randomised controlled trials (RCTs) reporting data on the efficacy of moderate-to-severe HS.

Results
The NMA comprised 13 studies comprising 14 interventions on 2,748 participants in the network. The NMA showed the odds of achieving the clinical response were significantly superior with adalimumab (RR: 0.37, 95% CI = 0.06–0.63), adalimumab QW (RR: 0.63, 95% CI = 0.43–0.87), MAB1p (RR: 1.33, 95% CI = 0.03–3.12), secukinumab (RR: 0.25, 95% CI = 0.11–0.47) and secukinumabQ2W (RR: 0.24, 95% CI = 0.1–0.46) compared to placebo.

Conclusion
Based on the NMA, inhibiting tumour necrosis factor (TNF)-a with adalimumab appears to be the best strategy, followed by the blockade of IL--17 with secukinumab. Data for bimekizumab and CJM112 are promising. Infliximab has inconsistent clinical response, and more data are necessary to confirm this molecule as a potential third-line therapy in HS. The blockade of IL-23 and CD5a pathways is not relevant, or at least the current evidence is insufficient to recommend further investigation of guselkumab, risankizumab, and vilobelimab in phase III trials.
Relationship between Social Support and COVID-Related Common Mental Disorders Incidence in Pregnant Women in Banyumas, Central Java
Authors: Rahman Fadel , Wibowo Yudhi , Riana Purwa 
Published: February 2024

Background: Social restriction and distancing were enforced, to contain the viral transmission of Coronavirus Disease-19 (COVID-19). Therefore, normal social activities are stagnant and give rise to common mental disorders (CMDs) in pregnant women, especially in the first trimester of pregnancy. The purpose of this study was to analyze the relationship of social support with COVID-related common mental disorders (CMDs) in pregnant women in Banyumas Regional General Hospital.Subjects and Method: A observational analytic study using a Cross-sectional study approach targeting pregnant women in the Banyumas Regency Central Java, from May to December 2022. The independent variable is social support. The dependent variable is common mental disorders (CMDs). The sampling technique used snowball sampling of approximately 1543 respondents. This study employed the WHO-SSQ-6 and WHO-SRQ-20 questionnaires. The data analysis technique made use of the Chi-Square test.Results: Pregnant women who have low social support have a risk of Common Mental Disorders (CMDs) and are statistically significant (PR= 1.74; 95% CI = 1.83 to 1.87; p<0.001).Conclusion: Social support has a significant correlation with COVID-related CMDs.Keywords: COVID-19, COVID-related CMDs, Pregnant women, first trimesterCorrespondence: Yudhi Wibowo, Department of Public Health and Community Medicine, Faculty of Medicine, Jenderal Soedirman University. Jl. Dr. Gumbreg No.1, South of Purwokerto, Banyumas Regency, Central Java. Email: dryudhiwibowo2005@gmail.com. Mobile: +62 811-2621-904.Indonesian Journal of Medicine (2022), 08(02): 142-149https://doi.org/10.26911/theijmed.2023.08.02.03