bluuebunny/crossref_metadata_2025 huggingface dataset:
Incretin-Based Weight Loss Pharmacotherapy: Can Resistance Exercise Optimize Changes in Body Composition?
Authors: Locatelli João Carlos , Costa Juliene Gonçalves , Haynes Andrew , Naylor Louise H. , Fegan P. Gerry , Yeap Bu B. , Green Daniel J.
Published: April 2024

This narrative review highlights the degree to which new antiobesity medications based on gut-derived nutrient-stimulated hormones (incretins) cause loss of lean mass, and the importance of resistance exercise to preserve muscle. Glucagon-like peptide 1 receptor agonists (GLP-1RA) induce substantial weight loss in randomized trials, effects that may be enhanced in combination with glucose-dependent insulinotropic polypeptide (GIP) receptor agonists. Liraglutide and semaglutide (GLP-1RA), tirzepatide (GLP-1 and GIP receptor dual agonist), and retatrutide (GLP-1, GIP, and glucagon receptor triple agonist) are peptides with incretin agonist activity that induce ∼15–24% weight loss in adults with overweight and obesity, alongside beneficial impacts on blood pressure, cholesterol, blood glucose, and insulin. However, these agents also cause rapid and significant loss of lean mass (∼10% or ∼6 kg), comparable to a decade or more of aging. Maintaining muscle mass and function as humans age is crucial to avoiding sarcopenia and frailty, which are strongly linked to morbidity and mortality. Studies indicate that supervised resistance exercise training interventions with a duration >10 weeks can elicit large increases in lean mass (∼3 kg) and strength (∼25%) in men and women. After a low-calorie diet, combining aerobic exercise with liraglutide improved weight loss maintenance compared with either alone. Retaining lean mass during incretin therapy could blunt body weight (and fat) regain on cessation of weight loss pharmacotherapy. We propose that tailored resistance exercise training be recommended as an adjunct to incretin therapy to optimize changes in body composition by preserving lean mass while achieving fat loss.
Analisis Preferensi Konsumen dalam Memilih Produk Hortikultura Menggunakan Metode Algoritma C45 dan Naive Bayes
Authors: Ridho Said
Published: April 2024

Saat ini, pertanian dan hortikultura memainkan peran yang sangat penting dalam perekonomian banyak negara, termasuk Indonesia. Dalam hortikultura, tomat dan cabai merupakan dua jenis tanaman sayur yang sering dibudidayakan. Kedua bahan makanan ini memegang peran penting dalam makanan tradisional Indonesia. Ketersediaan tomat dan cabai rawit yang melimpah memberikan lebih banyak pilihan bagi konsumen sesuai dengan kebutuhan mereka. Namun, jika ketersediaan tomat dan cabai terbatas, konsumen akan memiliki pilihan yang terbatas dalam perilaku konsumennya. Penelitian ini bertujuan menganalisis preferensi konsumen terhadap kombinasi atribut tomat dan cabai rawit di Pasar Keramat Baamang Sampit. Dalam analisis preferensi konsumen ini, digunakan dua metode algoritma yaitu C4.5 dan Naive Bayes dengan menggunakan software RapidMiner untuk melakukan klasifikasi. Hasil analisis menggunakan algoritma C4.5 untuk memilih tomat menunjukkan tingkat akurasi antara 60,00% hingga 63,06%, sedangkan algoritma Naive Bayes memiliki akurasi antara 45,00% hingga 49,05%. Dengan demikian, dapat disimpulkan bahwa dalam hal ini, algoritma C4.5 memiliki tingkat akurasi yang lebih tinggi. Namun, analisis preferensi konsumen untuk memilih cabai rawit menunjukkan bahwa algoritma C4.5 memiliki tingkat akurasi sekitar 89,00% hingga 89,05%, sementara algoritma Naive Bayes memiliki tingkat akurasi sekitar 89,05% hingga 90,00%. Oleh karena itu, algoritma Naive Bayes menunjukkan tingkat akurasi yang lebih tinggi.
Analisis Jaringan 4G untuk Aplikasi Smart Ambulance
Authors: Jayati Ari Endang , Harmini  , Budiningrum Diah Setyati
Published: April 2024

Intelligent Transport System (ITS) is an intelligent traffic system used for traffic management and engineering. The ITS network uses inter-vehicle telecommunication technology. The city of Semarang is one of the cities that will establish ITS technology. One area that applies technology is health. The telecommunications system applied in the health sector today allows medical staff to treat patients in ambulances before arriving at the hospital. This communication system needs to be supported by a good cellular network so that data can be sent to the server. This study aims to measure QOS (Quality of Service) including the characteristics of delay, packet loss, jitter, and throughput from various cellular providers. The results of data measurements using wireshark software with TIPHON standard guidelines obtained an average value for delay obtained by Provider A with the best result of 86.22 ms. The average value for packet loss obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider A with the best result of 0.474% d for the City Hall Bus Stop to the Penggaron Terminal obtained by Provider B with the best result of 0.574%. The average value of jitter obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider B with the best result of 0.42 ms, for the City Hall Bus Stop to the Penggaron Terminal, Provider A was obtained with the best result of 0.57 ms. The average throughput value obtained from the measurement results for Penggaron Terminal to City Hall Bus Stop was obtained by Provider C with the best result of 6631 Kbps, for City Hall Bus Stop to Penggaron Terminal obtained by Provider B with the best result of 6339.5 Kbps. Based on this, knowing the value of delay, packet loss, jitter and throughput is expected to be a consideration for the development of a smart ambulance system in the city of Semarang. There are two measurement points, namely the Penggaron Terminal to the City Hall Bus Stop and the City Hall Bus Stop to the Penggaron Terminal using the Semarang BRT.
Monitoring dan Pengatur Suhu Otomatis Pada Lumbung Padi Berbasis IoT
Authors: Rozie Fachrul Rozie
Published: April 2024

Lumbung padi merupakan tempat penyimpan padi sebagai teknik ketahanan pangan masyarakat di desa. Suhu pada tempat penyimpanan padi akan sangat panas pada saat matahari terik dan akan cukup dingin pada saat musim penghujan. Penyimpanan padi tersebut mempunyai beberapa masalah yaitu ketika curah hujan meninggi menyebabkan suhu dilumbung menurun sehingga padi lembab dan mengakibatkan padi rusak menghitam terkena jamur dan juga ketika suhu matahari terlalu panas menyebabkan padi terlalu panas dan mudah hancur. Sensor yang dapat mendeteksi suhu dan kelembaban adalah sensor DHT22 dan bisa dikombinasikan dengan mikrokontroller  nodeMCU untuk menerapkan teknologi Internet of Thing didalamnya dipadukan dengan lampu pijar yang dapat menaikan suhu dan kipas untuk menurunkan suhu. Tujuan dari penelitian ini adalah untuk memonitoring suhu dan kelembaban pada lumbung padi dari jarak jauh serta mengendalikan suhu yang ada didalamnya agar tetap ideal. Dari penelitian yang dilakukan Nilai suhu rata-rata pada lumbung berkisar 29-37°C pada sebelum menggunakan alat yang dibuat dan untuk kelembaban pada lumbung padi berkisar 55-76%. Nilai set poin suhu yaitu 40°C.
Effect of Parameter Modification on Torque Ripple in Interior Permanent Magnet Synchronous Motor
Authors: Farha Auditya , Ferdyanto  , Achmad Zuchriadi  , Muhamad Alif Razi
Published: April 2024

Electric motors are electromechanical machines commonly used in industry and automotive products. Interior Permanent Magnet Synchronous Motor (IPMSM) is a synchronous electric motor with a permanent magnet that is often used on electric vehicle engines. However, one of the characteristics of an electric motor with permanent magnet is the existence of torque ripples. The presence of torque ripples in electric motors can cause vibrations in the machine which affect efficiency, structural durability and operating speed. Therefore, it is necessary to make improvements or changes to the motor design to overcome this problem. This study was conducted to analyze the effect of a modification of the electric motor parameter on the torque value in the existing design. Modifications are applied to the existing design with an initial condition of torque ripples of 42.58%. This study modified the air gap, magnet angles, magnet thickness, and opening slot width. Modifications are carried out through a finite element method-based software simulation with the Taguchi method to reduce experimental configurations of design factors from 625 to 25 experiments. Through this research on parameter modification, the results of the analysis show that each modification of the design factor has an influence on the value of torque ripples. From the process of Analysis of Mean (ANOM), it can be seen that wider air gap and slot opening, the smaller torque ripples. Then, the greater magnet rib and thicker magnet cause the greater torque ripples.
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.
El ajedrez geopolítico de América Latina en el nuevo orden multipolar
Authors:   Profesora permanente laboral, Universidad de Valencia (España), Barragán Mélany , Sribman Mittelman Ariel ,   Investigador, Grupo de Estudios de Asia y América Latina (GESAAL), Instituto de Estudios de América Latina y el Caribe, Universidad de Buenos Aires (UBA)
Published: April 2024

Este artículo analiza el papel de América Latina en un sistema internacional en transición, con altos niveles de incertidumbre y transformaciones que implican una reconfiguración geoeconómica y geopolítica a nivel global. En este contexto, el trabajo examina los nuevos y viejos desafíos a los que la región debe hacer frente, poniendo el foco en el impacto de los cambios globales en las dinámicas regionales y la capacidad de los diferentes países para dar respuesta tanto a la coyuntura internacional como a viejos y nuevos problemas internos. Asimismo, y a modo de artículo introductorio de este monográfico titulado «Geopolítica desde América Latina: cambio de ciclo y multipolaridad», se identifican los ejes que articulan dichos cambios, su impacto, los principales actores implicados y los procesos en curso.
Del consenso a la complejidad: relaciones interamericanas diversas y en transición
Authors:   Profesor asociado, Escuela de Gobierno Alberto Lleras Camargo, Universidad de los Andes (Colombia), Bitar Sebastián , Long Tom ,   Profesor de Relaciones Internacionales, University of Warwick (Reino Unido).
Published: April 2024

Desde la crisis financiera de 2008, la política internacional ha sido alterada por una mayor difusión de recursos económicos y capacidades militares. Ello, para muchos, constituye una transformación radical que puede ser el fin de la hegemonía estadounidense en América Latina. Este artículo examina esta proposición comenzando por revisar el concepto de hegemonía que, en lugar de verse como una característica estructural, aquí se conceptualiza como una «hegemonía matizada», es decir, una red de relaciones asimétricas y jerárquicas. En este sentido, se observa no una sola transformación, sino la emergencia de un contexto hemisférico que vendría definido por la «asimetría diferenciada». Estados Unidos aún ocupa una posición central, pero su política exterior está marcada por la fragmentación subregional y la externalización de prioridades electorales. Al respecto, se exploran sus efectos a nivel subregional con un enfoque en las oportunidades y los desafíos que ello supone para los países latinoamericanos.
Análisis y relación entre la composición corporal y variables de rendimiento en jugadoras de fútbol sala
Authors: López Mariscal Samuel , Reina Gómez Álvaro , Ortega Becerra Manuel , Suárez Arrones Luís
Published: April 2024

El fútbol sala es un deporte en crecimiento en los últimos años. Es un deporte colectivo intermitente de alta intensidad jugado en un espacio reducido con altas demandas físicas. El objetivo principal de este estudio fue analizar la composición corporal, el rendimiento en acciones de alta intensidad como el salto vertical, el sprint, el cambio de dirección, la estimación de potencia y el rendimiento intermitente en jugadoras de fútbol sala de 1ª división y relacionarlas entre sí. En este estudio participaron once jugadoras de fútbol sala de primera división española (N=11; 21,55 ± 4,32 años; 162,09 ± 5,56 cm; 60,17 ± 8,05 kg). Los resultados revelan que la composición corporal y el rendimiento de las jugadoras varía en función de su posición específica. Además, la composición corporal está relacionada con el sprint y la estimación de potencia. El CMJ se relaciona con el sprint en 10 y 20 metros y con el cambio de dirección, así como la VIFT se relaciona con el sprint en 20 metros.
A Study on Improving Tactical Strategies in Sports Games Using Optimization Algorithms
Authors: Xiao Yakang
Published: April 2024

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Innovative Application of Digital Audiovisual Language Based on Natural Language Processing Technology in Graffiti Art Education
Authors: Yang Xiaolan
Published: April 2024

In this paper, natural language processing technology is used to establish coupled Hidden Markov Models for speech formalization and features of the two channels. The adaptive weight selection method is used to recognize speech under different signal-to-noise ratios in the recognition stage. The audio-visual recognition system is applied to actual graffiti art education research, and the feasibility and development direction of audio-visual graffiti in the classroom are studied through questionnaires. It can be found that most teachers and students believe that the concept of audiovisual graffiti painting is helpful or more helpful in the cultivation and development of students’ subconscious mind. Most teachers and students believed audiovisual doodling was beneficial or advantageous for developing students’ subconscious. In the acceptance test and learning effect analysis of the audiovisual graffiti course, the mean values of the four dimensions of perceived usefulness (41.87), perceived ease of use (18.11), willingness to use (31.45), and perceived experience of the process (28.31) are basically around the maximum value, which indicates that the audiovisual technology is well accepted and that the mean value of the experimental group’s performance score (83.68) is significantly higher than that of the control group. Control group. The audiovisual course has been found to enable learners to concentrate on the learning situation faster and master the teaching essential contents more thoroughly.
Lexical Characterization of Ancient and Modern Chinese Combined with Semantic Association Network Modeling
Authors: Zhang Lixia
Published: April 2024

This paper explores the linguistic features of ancient and modern Chinese, focuses on capturing the knowledge of the relevant domains of ancient and contemporary Chinese using semantic models, and carries out the corresponding semantic resource extraction and semantic representation through the resource expression mechanism in the framework of the Semantic Association Network Model (SANM), and then analyzes the historical evolution of the lexicalization patterns of the Chinese language and the linguistic features of the Chinese language in different periods. In the historical evolution of Chinese lexicalization patterns, it is found that the distribution of lexicalization patterns of ancient Chinese table-cutting action lexical items is [action+object] > [action+method] > [action result] > [action+tool], and the distribution of lexicalization patterns of modern Chinese table-cutting action lexical items is [action+method] > [action+tool] > [action+result] > [action+object]. The linguistic features of different periods show that all networks have small-world properties and scale-free properties, and the statistical parameters of the networks in each period are relatively close to each other, with the modern network having the most significant C (8.59) and the smallest L (0.92) and γ (0.40). Ancient and modern Chinese words and phrases with relational word collocations in the interval [0,23] have the most extensive and densest distribution of relational words. These data present the similarities and differences between linguistic features between ancient and modern Chinese, which is significant for studying ancient and modern Chinese.
An Intelligent Model of Mixed Teaching of Business English in Colleges and Universities in the Information Age
Authors: Zuo Xiaojuan , Chen Zeyu
Published: April 2024

With the rise of foreign trade, various industries have increased the demand for foreign language talents, and colleges and universities have paid more attention to the intelligent teaching of business English. Based on this, this paper proposes a dynamic time regularization recognition algorithm (DTW). It improves the recognition efficiency of the algorithm, preprocesses the speech signal, and selects the feature parameter MFCC. The improved DTW algorithm is used to construct an intelligent mode of mixed teaching of business English in colleges and universities, and the learning effect of the students majoring in business English is calculated and evaluated. The research object of this paper is students majoring in business English at college C and is conducted by conducting experimental research on their oral learning, article writing ability, and learning attitude. From the results of oral teaching, the coefficient of variation P-values before and after the application of each group are 0.000, 0.002 and 0.000 respectively, which are all less than 0.05, indicating that students with different English fundamentals have a more significant effect of improving their performance after applying the mixed teaching mode. Before the application, the average score for article writing increased from 59.67 to 64.50. In terms of affective attitude and value, the learning interest risen from 2.75 to 2.97, with the most significant improvement Thus, it can be seen that the intelligent hybrid teaching mode constructed in this paper can effectively improve the teaching effect of business English in colleges and universities.
Research on IoT Edge Data Mining Techniques Based on Cloud Computing Models
Authors: Kang Yang , Li Qiang , Liu Yuanyong
Published: April 2024

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.
Research on the Path of Teaching Reform of Colleges and Universities Courses in the Age of Artificial Intelligence
Authors: Wang Zhuyu
Published: April 2024

This paper proposes a simulation of teaching behavior based on metacellular automata in response to the problems in the teaching reform of college courses on the digital platform. Using the rule grid and binary method, the tuple units in the structure of tuple automata are described and represented, followed by the standard combination of many tuples to form a tuple space. Considering that the definition of neighbors is relatively complex, von Neumann-type, Moore-type, and extended Moore-type represent the neighbors of a two-dimensional tuple automaton, and rules for the evolution of tuple automata are also formulated. Led by the constructivist teaching model, the contextual teaching method is divided into five links, on which the contextual teaching model based on metacellular automata is constructed. Simulation and Analysis are used to analyze the teaching reform of college courses in the era of artificial intelligence, for example. The results show that when the simulation condition is the initial satisfaction rate of the system ξ = 0.425, and the teaching strategy n=2, the teaching quality improves with the increase of the response probability, which is in line with the law of teaching, i.e.The teacher should motivate the enthusiasm of learning, stimulate the interest of learning, and entirely play the role of students in teaching. This study aims to promote the reform of college course teaching in the era of artificial intelligence, which will contribute to the improvement of college teaching quality.
Research on Automatic Annotation Method of Korean Language under Data Driving and Fusion
Authors: Xiang Tianyu , Gao Li , Liu Wenming
Published: April 2024

In the quest to streamline Korean text and speech annotation, this research introduces innovative automatic annotation methods that promise to revolutionize efficiency and technical prowess in constructing Korean annotation datasets. By leveraging the sophisticated Seq2Seq architecture with BERT and bidirectional GRU models, we significantly enhance the model’s ability to grasp contextual nuances, ensuring precise text annotations. The speech annotation frontier benefits from a novel amalgamation of the Hidden Markov Model’s forced alignment and semi-supervised learning, perfected with Seneff auditory features for meticulous phonological consonant boundary detection. Empirical validation across diverse datasets showcases our methodology’s superiority, achieving a remarkable 96.01% accuracy in text annotation and setting a new benchmark for phonological boundary detection at a 14.5ms minimum distance threshold. Our approach outperforms traditional algorithms, marking a pivotal step forward in Korean automatic annotation.
Exploration of the Integration of Traditional Music Cultural Elements in Music Informatization Teaching
Authors: Feng Qiyue
Published: April 2024

With the development of information technology, music teaching methods are getting more affluent and more prosperous. This paper proposes a model for emotion classification and assessment that integrates traditional music culture elements with information technology in music teaching. The research first combines TextCNN and BiLSTM algorithms to establish the emotion classification model of conventional music. Then it combines PYIN and DTW algorithms to establish the evaluation model of traditional music, which completes the auxiliary efficacy of music informationized teaching. In the emotion classification test of the model, the classification accuracy and F1 value of emotions of different music samples are 82.98% and 75.22%, respectively. The model’s recognition accuracy of the four voices is 86.76%, and the overall effective scoring percentage is 81.98% under different playing abnormalities. This study has had an impact on traditional music evaluation. The model in this paper can be used to classify and evaluate emotions in conventional music, providing more intelligent and high-quality technical services for integrating traditional music into music teaching.