bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pemanfaatan SIG untuk Mengurangi Risiko Bencana Banjir di Kota Bandar Lampung
Authors: Septiana Trisya 
Published: April 2024

Kemajuan teknologi dalam bidang informasi dan komunikasi dapat digunakan untuk mengoptimalkan penanggulangan dan mitigasi bencana, Salah satunya adalah pengembangan sistem informasi geografis. Sistem ini memberikan kemudahan dalam menyampaikan informasi dan memecahkan persoalan yang berkaitan dengan permukaan bumi terutama data mitigasi bencana mengenai daerah yang rawan banjir sehingga dapat tertangani lebih dini. Bencana banjir merupakan suatu bencana alam yang selaluterjadi di banyak kota dalam skala yang berbeda dimana air dengan jumlah debit yang berlebih berada di daratan rendah yang kering. dimana menurut data Badan Penanggulangan Bencana Daerah (BPBD) terdapat beberapa daerah yang merupakan titik rawan bencana terutama di kota Bandar Lampung yang diambil dari data bencana alam yang sering terjadi dalam kurun waktu beberapa tahun terakhir diantaranya Teluk Betung Selatan, Teluk Betung Timur, Kedamaian, Rajabasa, Sukarame, Sukabumi dan Bumiwaras. Untuk meminimalkan dampak bencana banjir ini dengan tersedianya pemetaan daerah rawan banjir yang dapat digunakan untuk perencanaan, pengendalian dan penanggulangan dini. Sistem Informasi Geografis (SIG) merupakan salah satu alternatif dalam pemetaan daerah rawan banjir untuk cakupan daerah yang luas dengan waktu yang relatif singkat. Pemetaan risiko banjir terdiri dari komponen data spasial dan atribut yang kemudian divisualisasikan menggunakan WebGIS
THE IMAGE OF TIGRAN THE GREAT IN VIVALDI'S OPERA
Authors: MARTIROSYAN MADONA 
Published: April 2024

Tigran the Great is one of the most powerful Armenian rulers, whose image was of most interest to Greco-Roman authors and served as a source of inspiration for European cultural figures. Rich evidence of this is the artistic and historical manifestations of the image of this outstanding ruler in European musical art.
The object of research is the image of Tigran the Great.
The purpose of the study is to analyze the comprehensive influence of the image of Tigran the Great in European musical art.
Served as the methodological basis of the research historical-comparative-chronological, descriptive, analytical-comparative, source-scientific (Justin, «Philip's Histories» Book XXXVIII, Francesco Silvani La virtu trionfante dell'amore e dell'odio overo Il Tigrane drama) methods.
The study found that European musicians created 41 operas, many of which featured the Armenian king Tigran the Great as the main or participating character. The composers were interested in the historical figure of Tigran the Great, inspired by the materials in the works of Greek-Roman authors. The article specifically presents the presentation of the historical figure of Tigran the Great by the famous composer Antonio Vivaldi, the basis of which is the work "Parallel Biographies" by the historian Plutarch.
A Study on Improving Tactical Strategies in Sports Games Using Optimization Algorithms
Authors: Xiao Yakang 
Published: April 2024

Abstract
Sports are dynamic, unpredictable battles that require teams to develop strategic plans for making use of their advantages and adjust to changing conditions during play. In this study, we use optimization algorithms to improve the tactical methods which are used in sports games. Tactics are the intentional, premeditated moves, choices, and configurations made by groups or individuals to accomplish particular goals and outplay their rivals. The number of corresponding solutions addresses the problem of designing security strategies optimally using a game theory simulation. In this simulation, players choose to attack the structure based on certain unpredictable attack scenarios, while the attacker selects the way to defend the system by managing the available options for strategic solutions. The unpredictable nature of human performance is a barrier to the advancement of sports strategy tactics, since players may depart from pre-planned plans as a result of weariness, injuries, or emotional states. We suggested the self-regulated immune cat swarm optimization (SRICSO) strategy as a solution to these issues to improve tactical strategies in sports. A National Basketball Association (NBA) dataset was gathered for this study’s evaluation. The collected dataset underwent preprocessing using the minmax normalization technique, and feature extraction was accomplished through the use of principal component analysis. We compare our method’s precision (87%), accuracy (97%), F2-score (95%), and recall (92%) with more traditional methods such as the optimized convolutional neural network (OCNN), naïve Bayes (NB), and random forest (RF). According to the findings of an assessment conducted with a dataset from the NBA, the SRICSO approach was shown to have higher performance in terms of improving tactical strategies in sports.
Research on the Influence of College Ideological and Political Education on Students’ Mental Health Based on Deep Learning
Authors: Pan Gao , Yan Guo 
Published: April 2024

Abstract
Psychological health education is the main battlefield for promoting the mental health of college students. This article studies the impact of ideological and political education in universities on the mental health of college students. Research on Student Mental Health Based on DL. A student emotion recognition model based on DL (deep learning) has been established. The separable CNN (Convolutional neural network) is used to detect the face in the image, and the face is located and tracked through the tracker. After normalizing the facial image, it is input into a deep separable CNN for classification. The results indicate that the regression equation can explain 74.106% of the total variation. The political characteristics of Demographics characteristics have a significant impact on college students’ political entity identity, while gender, grade, and major have no significant impact on college students’ Party identification. Social practice and campus culture have a significant positive impact on the political entity identity of college students. Compared with traditional machine learning-based emotion recognition methods, the classification accuracy of the proposed method in this paper has been improved by 8.036%, and research based on DL technology has significant advantages in accuracy.
Optimizing the Synergistic Coexistence of Built Environment and Waterfront Public Space, a Quantitative Study in Shenzhen Bay Area
Authors: Jiao Hong , Liu Yuwei 
Published: April 2024

Abstract
Waterfront public spaces and urban built environments are focal points in urban planning, yet more scholars need to focus on the coupling relationship between the two. Only through the coordinated development of these two can urban sustainability be promoted. Using vitality as a medium and drawing from the theory of coupling in physics, the study quantitatively analyzes the coupling and coordination degree between the built environment of 62 research units in the Shenzhen Bay waterfront area and the vitality of the adjacent waterfront public spaces. This is achieved by leveraging the population location big data from Baidu Huiyan, and vector data from Amap. The study finds that: (1) The overall coupling coordination degree between the public space vitality and built environment in the Shenzhen Bay waterfront area is relatively high, although the spatial distribution is uneven. (2) The built environment systems and waterfront public spaces of slightly and moderately discoordination units are predominantly in the process of development and construction, which manifests as a need for more completeness in the overall urban development. (3) Research units in an elementary coordination state mainly display that a moderate-intensity built environment has not spurred the vitality of waterfront public spaces. More units in a moderate coordination state show a relative lag in the urban built environment system. (4) Advanced coordinated and optimally coordinated study units are coordinated and co-developed with each other but can have a siphoning effect, leading to a less coordinated phenomenon in neighboring regions.
Research on the Governance form Mechanism of Platform Enterprise Monopoly Behavior Based on Evolutionary Game Theory
Authors: Haixin Huang , Wang Zhang 
Published: April 2024

Abstract
Platform enterprises, which aggregate numerous and scattered resources, connect multiple interdependent parties and facilitate interactions and transactions, also confront significant monopoly market challenges. These issues constrain the platform economy’s healthy development and severely disrupt the normal market order. This paper constructs a tripartite evolutionary game model comprising the government, platform enterprises, and users, to investigate the governance mechanisms of monopoly behavior in platform enterprises and employs Matlab for numerical simulation. The results indicate that government rewards, additional profits, monopoly penalties, and user collaboration positively impact the evolution of platform enterprise strategies toward non-monopolistic practices. Together, these four mechanisms constitute the governance mechanism for monopoly behavior in platform enterprises. Specifically, providing honorary rewards for non-monopolistic behavior in platform enterprises over a certain period is essential, as it represents an effective means for the government to encourage innovative development in internet platform enterprises. The profit mechanism is the main driving force behind platform enterprises adopting monopolistic strategies and facilitating their survival and development. To a certain extent, the penalty mechanism can suppress monopolistic behaviors in platform enterprises, but it cannot fundamentally eliminate them. Instead, this mechanism accelerates the evolution of platform enterprises towards non-monopolistic behaviors, marking it as a positive governance tool. The user collaboration mechanism, a form of collaborative governance, serves as a beneficial complement to the reward, penalty, and profit mechanisms. This is because users primarily engage with platforms as consumers, legally entitled to services, and their regulatory role constitutes only a part of their responsibilities, not the main economic activity on digital platforms.
Design and application of airport data storage under an information security optimization system
Authors: Li Kai 
Published: April 2024

Abstract
With the advancement of technology, the “partial construction” model is no longer able to meet the current information requirements of the aviation industry. Therefore, it is necessary to establish a complete “global informatization”, that is, an airport data center, which comprehensively includes multiple production management systems within the airport group, achieves information sharing, data analysis, and intelligent decision-making, thereby enabling the airport to achieve more accurate and reliable comprehensive economic and socially sustainable development. The current network storage technology often generates massive amounts of data when processing it, which leads to an increase in storage cycles and a decrease in storage performance. On this basis, the FPGA interface in FLASH chips was studied and analyzed in detail. On this basis, the FPGA receiving module is used to accurately receive and filter the massive data obtained, and convert it into high-quality FLASH format for daily use and maintenance. By analyzing the three-layer architecture, a large-scale network data storage solution based on the three-layer architecture is proposed. Finally, an implementation scheme for a new distributed storage system based on this model is presented, and simulation results are provided.
Assessment of e-business instructional quality based on BP neural network in new media environment
Authors: Ying Pan 
Published: April 2024

Abstract
Instructional quality assessment is a systematic project involving a wide range. Restricted and influenced by many factors and conditions. How to accurately and effectively assess the instructional quality of teachers. The determination of the assessment system and method is very important. Some people think that the quality of teaching is “the total of the characteristics of tertiary education that can meet the obvious or implicit needs of individuals, groups, and society. These characteristics are shown through the goals, standards, and achievement levels required by the educated, educators, and social development. In this article, a back propagation neural network (BPNN) is used in classroom instructional quality assessment, and a classroom instructional quality assessment model is constructed. The experimental results of this article show that the accuracy rate of the algorithm based on BPNN reaches 89%, and the learning rate reaches 93%.
Analysis and Prediction of Students’ Psychological Characteristics of Law Breaking and Crime Based on Support Vector Machine Algorithm
Authors: Yang Bo , Zhou Hui , Shi Sen , Qin Xueqing , Li Quan 
Published: April 2024

Abstract
In recent years, some people with high intelligence and high quality have violated the law and even committed crimes, which has ruined their bright future. It is really sad. A school is a place where teenagers learn skills and improve their quality. It should be a pure land. The frequent occurrence of juvenile students’ crimes seriously affects the normal teaching order and endangers the safety of students’ lives and property. With the rapid development of support vector machines (Abbreviation: SVM), it has also become one of the main data fusion technologies. SVM is a creative machine learning method based on statistical learning theory. It is a learning method specialized in finite sample prediction. This paper studies the psychological characteristics of students’ crimes based on the SVM algorithm. From the aspect of proportion, the result of the SVM algorithm is ideal, and the proportion of the training set is up to 33.35%. The above algorithm has good expansibility for such sample data, but when the parameters are not appropriate, the proportion is only more than 20.12%, which once again explains the importance of the SVM algorithm. Only through the SVM algorithm to improve the efforts of the whole society, can we put an end to the generation of criminal psychology of higher vocational students from the source and cultivate more high-quality talents for the country.
Exploring the Influencing Factors of Students’ Fire Behavior Awareness Based on the Theory of Internal Drive Reduction
Authors: Die Meng , Beibei Ma , Mengting Liu , Shiying Li 
Published: April 2024

Abstract
Here argued that human behavior is primarily governed by habit, not by biological drive. When the need is satisfied, the drive generated by the need will decrease. This process is also the stimulation and reinforcement of the connection between the problem-coping behaviors, that is, the enhancement of the strength of the habit. This paper applies Herner’s drive theory to explore the influencing factors of college students’ fire awareness to strengthen college students’ behavioral potential in the face of fire danger. Based on the current situation of fire education, this paper analyzes the three influencing factors of the driving force, inducement, and habit intensity through the theory of Hull drive reduction, and discusses how to strengthen the habit intensity of college students in the face of fire danger and strengthen the behavioral connection between fire risk and safety countermeasures, which is conducive to exploring the characteristic fire safety education mode of colleges and universities.
Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology
Authors: Cheng Huimin , Chang Congmin , Yin Yu 
Published: April 2024

Abstract
English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.
The Application of Big Data Technology in the Educational Model of Psychological Parenting in Colleges and Universities in the Context of Social Media
Authors: Li Wei 
Published: April 2024

Abstract
In today’s rapid development of information technology, social media has become an indispensable part of the daily communication of students in colleges and universities. The issue of student mental health is increasingly receiving widespread attention from society. As an essential position for mental health education, how to effectively utilize big data technology for students’ psychological nurturing and enhance the pertinence and effectiveness of mental health education has become an important issue in education. In this article, by creating a four-level psychological parenting model built on a positive psychology perspective, we study extensive data mining, deep learning and other methods to analyze students’ social media usage data and identify students’ mental health status. For the 225 students in this article, the anxiety and depression self-assessment scale scores decreased significantly (P<0.05) after the psychological parenting education intervention, indicating that big data technology enhances the effect of mental health education in colleges and universities. Association rule mining revealed a distinct connection between psychological problems, such as learning pressure and maladaptation, and students’ social media behavior patterns. The application of big data technology in this study helps colleges and universities identify and intervene in students’ mental health problems more accurately, promoting comprehensive development.
Research on the Path of Integrating Traditional Culture into Music Teaching in Colleges and Universities in the Era of Artificial Intelligence
Authors: Li Xin 
Published: April 2024

Abstract
As artificial intelligence (AI) matures, it presents challenges and opportunities for music education in higher education. This study investigates how AI can facilitate integrating and transmitting China’s rich traditional culture within modern music pedagogy. Utilizing literature review, surveys, and experimental teaching, our research demonstrates that AI enhances teaching efficiency and significantly boosts students’ engagement with traditional culture—evidenced by a 30% increase in their understanding and appreciation of traditional music compared to conventional methods. AI technology emerges as a powerful tool for preserving and digitalizing traditional music, thus sparking students’ interest and fostering a deeper connection with their cultural heritage. This paper suggests that AI can significantly improve music teaching quality and efficiency, offering a novel approach to artistic education and heritage preservation.
Dilemma and Path of Innovative Practice of Civic Party Building Curriculum in Colleges and Universities in the Era of “Internet Plus” Combined with Logit Modeling
Authors: Liu Junjian , Zhang Jun 
Published: April 2024

Abstract
In the “Internet+” era, Civic Party building programs at universities face the challenge of modernizing their pedagogical approach to meet students’ evolving needs. This paper examines the hurdles in updating course content and diversifying teaching methods, utilizing the Logit model to determine factors crucial to innovating these courses. Analysis reveals that political cognition, emotion, and participation play pivotal roles in the success of course innovation, especially political cognition with a coefficient of 0.784. Recommendations for course improvement include adopting internet technologies, updating content, varying teaching techniques, and enhancing student involvement. Ultimately, the study advocates for a strategic overhaul of Civic Party building courses to fulfill their educational goals.
Research on the Evaluation of Art Teaching Based on Optimized Neural Network of Bottle Sea Sheath Algorithm
Authors: Liu Xuedan , Li Na , Peng Jingfu 
Published: April 2024

Abstract
Today, the evolution of art teaching evaluation systems faces significant challenges and opportunities, particularly with the limitations of subjective judgments in traditional methods impacting the accuracy of teaching quality assessments. This study leverages advancements in artificial intelligence, specifically neural networks enhanced by the Taruhai Sheath algorithm, to propose a novel and more objective approach for evaluating art teaching. Our optimized neural network model demonstrates a remarkable 92% accuracy in art teaching evaluations, outperforming traditional methods by 15%. Furthermore, our analysis reveals that the quality of practical teaching is a critical factor in boosting students’ artistic creativity. By applying the Taruhai Sheath algorithm for neural network optimization, this research offers a groundbreaking method to elevate the objectivity and rigor of art education evaluations, promising a significant leap forward in art teaching quality.
Financial Asset Allocation and Portfolio Optimization Strategies Based on Stochastic Optimization Methods
Authors: Yang Tao 
Published: April 2024

Abstract
Financial market uncertainty necessitates sophisticated asset allocation and portfolio optimization strategies for enhanced returns and risk mitigation. This study explores stochastic optimization methods to overcome the limitations of traditional models in complex market environments. By employing expected utility theory and the Lagrange multiplier method, we develop an optimization model that facilitates fair equity pricing and enables dynamic allocation of financial assets, outperforming traditional approaches in simulated markets. Specifically, our method yields mean allocations for state-owned and non-state-owned enterprises at 17.4112 and 16.9297, respectively, demonstrating its superiority. Analysis of bank asset portfolios reveals significant improvements in asset structure and yields, underscoring the effectiveness of stochastic optimization in financial asset management, offering substantial benefits in efficiency and investment returns.