bluuebunny/crossref_metadata_2025 huggingface dataset:
Spill-free Robotic Arm Based on Pendulum Model
Authors: Wang Jessica , Ren Laura Yajing , Zhao Joy Jiayi , Lian Tim Juncheng
Published: January 2025

This paper investigates the application of a pendulum model in designing and simulating a 5-DOF robotic system that transports an open-top container filled with liquid free of any spill. In this work, the robot consists of a 3-DOF robotic arm for positioning and a 2-DOF end-effector for adjusting the containers orientation to prevent spillage. The system is implemented using both Matlab Simulink and Simscape environment. In this hybrid setup, Simscape handles the robots kinematic modeling, while Simulink is used for controlling the end-effectors orientation with a PID controller. Under the pendulum model, the gripper, the open-top container and the liquid move as one mass along the pendulum trajectory, so that no liquid is spilled out during the movement. Results indicate trajectory tracking accuracy. This approach avoids the need for complicated computational fluid dynamics model and underscores the advantages of autonomous mobile robots in achieving reliable performance in spill-free liquid handling, a task difficult to achieve for human beings.
Experimental Research on Recidivism Prediction
Authors: Zhou Junhong , Li Xingchi , Ma Xinjie , Ye Yiru , Zhou Zihan
Published: January 2025

The regression model and random forest model have been used to research recidivism prediction. The purpose of the research is firstly, to determine the crucial factors influencing recidivism rates among gender, ethnicity, prior crime record, misdemeanor status, and age. The second purpose is to discuss potential bias among the raw data. The third one is determining the pros and cons of machine decisions. With the work of data processing, analysis, and modeling, we had come to our result that factor terms that have a significant impact on the recidivism rate include: gender, priors, priors interact with age range, gender interact with age range. Through a variety of modeling methods, we established logistic regression models and random forest models to predict the crime rate through variables such as gender, age, and so on. Both models show decent accuracy, and two different models can be adapted to different situations. Some limitations have been admitted after comparison to other papers, such as the usage of only static variables, and at the same time, potential future improvements regarding the work are proposed in accordance with the limitations found.
Mobile Robot's Path Planning Algorithm Combines RRT* and CBS
Authors: He Xingjin , Guo Yanting
Published: January 2025

The A* algorithm is widely used in robot navigation because it ensures finding the shortest path and speeds up the search through heuristic functions. However, the A* algorithm needs to build multi-node paths in advance, which requires a lot of memory and time when facing large maps. In addition, it may only be able to find local optimal solutions, and thus has some limitations when applied to warehouse robot navigation. This paper explores the feasibility and effectiveness of using RRT* algorithm instead of A * algorithm in the field of warehouse robot navigation and tries to combine RRT* algorithm with CBS algorithm to avoid collision between robots. And the combination of RRT* algorithm and CBS algorithm is simulated in simulated obstacles based on MATLAB and compared with the existing A * algorithm and CBS algorithm and concluded that the combination of RRT* algorithm and CBS algorithm improves the efficiency of the path planning of the mobile robot to a certain extent. The algorithm can be used in multi-obstacle warehouse robots and hospital transportation.
Health Insurance Premium Prediction Based on Multiple Machine Learning Algorithms
Authors: Gao Davidson
Published: January 2025

The purpose of this paper is to predict the health insurance premium through a variety of machine learning algorithms, and compare and analyze the prediction effect of different algorithms. An open source dataset was selected for the study, and the experiments involved three machine learning models: linear regression, decision trees, and random forests. By testing these models, we obtain their performance in health insurance premium forecasts. The results show that the prediction performance of the random forest regression model is better than other models, and its score reaches 0.8564, which is the best algorithm among the three models. Second, the linear regression model has a score of 0.7584, and although its performance is not as good as that of random forest, it still shows some predictive power. Finally, the prediction effect of decision tree model is relatively poor, and the score is only 0.7097. To sum up, the experiments in this paper prove that the random forest model is undoubtedly the best choice in the prediction of health insurance premiums, which not only has good prediction accuracy, but also shows strong data processing ability. In the context of the growing importance of health insurance premium collection and analysis, the use of advanced machine learning algorithms such as Random Forest for forecasting will, to some extent, help insurance companies better price and assess risk. Therefore, it can be concluded that random forest regression model has the best performance for health insurance premium prediction and is an effective tool to achieve accurate prediction.
Exploring Integrated Models in Social Networks: Implications for Information Propagation and Misinformation Management
Authors: Wang Haochen
Published: January 2025

Disinformation has become a major challenge in the digital age, with significant consequences for public opinion, social cohesion, and the democratic process. This paper aims to explore the mechanisms of misinformation propagation within social networks, focusing on the role of key influencers, platform accountability, and policy interventions. By examining previous literature, this research seeks to identify strategies that influence the spread of misinformation. The research examines theoretical models such as the Attraction-Introduction Model to understand the dynamics of misinformation spread and the influence of key individuals in either amplifying or suppressing its dissemination. The study highlights that individuals with high network centrality, such as influencers, play a pivotal role in spreading and containing false information. Social media platforms are found to bear significant responsibility for managing information flow, primarily through algorithmic design and content moderation. Policy interventions, including regulation and public education, are necessary, but their impact is limited without international cooperation and platform transparency. The study requires a comprehensive approach to counter misinformation. Social media platforms must strengthen their accountability, algorithms must be reconfigured to prioritize accuracy over engagement, and governments should implement legislative and educational strategies. Collaboration between platforms, policymakers, and the public is critical for creating more resilient and credible social networks.
Enhancing the Security of Transmission Control Protocol (TCP): Challenges and Solutions for Modern Network Threats
Authors: Chen Hongyi
Published: January 2025

Transmission Control Protocol (TCP), the backbone of internet communication, ensures reliable, connection-oriented data transmission. Despite its widespread use in areas such as email, web browsing, and file transfer, TCP faces significant security vulnerabilities stemming from its design era, which prioritized functionality over security. Common threats include TCP sequence number prediction, session hijacking, SYN flood attacks, and TCP Reset attacks. Existing mitigation strategies, such as TCP-AO, SSL/TLS encryption, and network-based security measures like IDS/IPS, have reduced risks but face challenges like performance overhead and compatibility issues. This study reviews the root causes of TCP vulnerabilities, evaluates existing solutions, and highlights gaps in addressing threats within modern network architectures. While current measures are effective to an extent, future research must explore advanced technologies such as quantum cryptography, blockchain-based authentication, and AI-driven anomaly detection to enhance TCP security and adaptability. This work underscores the urgent need for interdisciplinary collaboration and innovation to secure TCP in evolving digital ecosystems.
Current Analysis and Future Perspectives of a Smart Sensor Monitoring System for Multi-Scenario Recreation
Authors: Xie Zhuoteng
Published: January 2025

With the accelerated aging of the population and the improvement of health awareness, the recreation industry is experiencing a booming development. Smart sensors, as the core component of recreation monitoring systems, play a key role in multi-scenario applications. This paper reviews the current research status of a multi-scenario recreation smart sensor monitoring system and analyzes its application in different scenarios such as family, community, and hospital. Firstly, the technological progress of smart sensors in physiological parameter monitoring, behavior analysis, and environment sensing is described. Second, the overall architecture design and data fusion method of a multi-scenario recreation monitoring system are discussed. Secondly, the characteristics and application requirements of smart sensors in different scenarios are compared and analyzed. Finally, the current technical challenges, including data security and privacy, system reliability, and intelligence level, are pointed out, and the future development trend is also prospected. The study shows that the introduction of emerging technologies such as multimodal sensing fusion, edge computing, and artificial intelligence will further enhance the performance of the recreation monitoring system. In the future, the construction of an intelligent and personalized recreation monitoring system covering the whole life cycle is of great significance in promoting the high-quality development of the recreation industry.
Research on Stock Price Rise and Fall Prediction Based on Optimization Random Forest
Authors: Wang Jingqi
Published: January 2025

Random forest algorithm is an effective machine learning algorithm in stock return classification prediction, with high accuracy, but it has problems such as parameter optimization defects and difficulty in feature selection. To this end, based on the traditional random forest algorithm, a new algorithm is proposed by combining the feature selection particle swarm algorithm with the parameter grid search algorithm - the particle swarm parameter grid search random forest algorithm. Using particle swarm optimization algorithm for feature selection of input data, reducing the dimensionality of input data by removing redundant features, and introducing grid search algorithm to optimize some parameters of random forest, not only reduces the computational complexity of random forest algorithm, but also improves the classification and prediction accuracy of random forest. The experimental results were compared with the original random forest, decision tree, and support vector machine classification models, confirming that the parameter optimized random forest stock prediction model has higher accuracy and AUC values in model evaluation than other models.
Solving the TSP Problem Based on Improved Genetic Algorithm
Authors: Li Haowang
Published: January 2025

This paper proposes an improved genetic algorithm (GA) for solving the Traveling Salesman Problem (TSP), aiming to overcome the issue of the classic GA becoming trapped in local optima when solving large-scale TSP instances. The proposed algorithm enhances the population diversity and global search ability by introducing an adaptive mutation probability strategy and a dynamic tournament selection strategy. Specifically, the adaptive mutation probability strategy dynamically adjusts the mutation probability at different stages of the algorithm, allowing for more mutations when the algorithm converges to a local optimum, thus helping to escape local optima. The dynamic tournament selection strategy, based on tournament selection, adjusts the tournament size during the algorithm's execution, thereby dynamically modifying the selection pressure as the algorithm progresses. Experimental results show that the improved GA significantly outperforms the classic GA in terms of solution quality and stability when solving the TSP, effectively finding shorter paths with smaller standard deviations, and demonstrating higher stability.
Exploring the Commercial Value of China National Knowledge Infrastructure (CNKI) based on Data Analysis Technology in the Context of the Big Data Era
Authors: Li Zhengwei
Published: January 2025

In the current big data era, where information technology is advancing quickly, the Internet is becoming more and more popular, data analysis methods are becoming more sophisticated, and data value is being gradually mined in depth, this paper makes use of data analysis technology to thoroughly explore the economic and commercial value of CNKI. The primary research question is what CNKI's business strategy or operational procedures are used to make money and what kind of commercial value CNKI possesses. This article applies techniques such as linear regression analysis, data screening, significance testing, and data visualization in conjunction with R language and tools such as Jupyter Notebook to study the problem. Finally, it was concluded that CNKI has taken a leading position in the market through intermediary models, proportional fees based on the number of literature pages, and a closed-loop monopoly of knowledge data. CNKI has also gained a competitive advantage in the market through its extensive literature and user base, as well as strong search technology capabilities.
Enhancing the Performance and Applicability of AI Large Language Models: Strategies and Improvements
Authors: Shen Pengfei
Published: January 2025

This work examines the methods of optimizing Large Language Models, focusing on their applications in present-day implementations of Artificial Intelligence across sectors such as Natural Language Processing, customer support, and text creation. As a result, prior models have restrictions when it comes to processing the more complex linguistic features, proper usage of CPU and those models are not adhering the ethical practices. This research responds to these challenges by exploring advancements in model designomal structure and update methods, together with categorical responsibility frameworks. Measuring the efficiency of large language models and the potential for their optimization, the work also uses the analysis of user feedback. The research shows that small changes in model design and training strategies have high impact on model performance and interpretability in various language tasks. These findings help to advance the creation of improved and more reliable language models with optimised ethical frameworks and utility of resources. Areas for future work are presented, such as integration of more complex multi-modal data and decreasing the amount of bias in model outputs.
Application of Supervised Learning Algorithms in Data Prediction
Authors: Jiao Hanying
Published: January 2025

With the advancement of artificial intelligence, such as Chat-Gpt and some generators, the development of all of these devices is based on Machine learning. Machine learning is one of the most popular keywords in the 21st century. One important category in machine learning is supervised learning which is the topic of this paper. Data prediction and peoples decisions are dominant in life, relying on algorithms, a supervised learning tool. So, which algorithms should people use under supervised learning to create models and predict data? The following paper will demonstrate the details of supervised learning and building models by using different algorithms to evaluate the quality of algorithms. The findings indicate that each algorithm has distinct advantages and limitations depending on data characteristics and context.
A Comprehensive Review of Transformer and Diffusion Models in Game Design: Applications, Challenges, and Future Directions
Authors: Zhong Chenhui
Published: January 2025

With the rapid advancement of artificial intelligence (AI) technologies, Transformer and Diffusion models have emerged as powerful tools across various fields, including game design. These models offer new possibilities for generating high-quality content and enhancing user experiences through personalized narratives. The integration of AI into game development promises to revolutionize how games are designed and experienced, potentially addressing limitations in traditional methods and opening avenues for more dynamic and engaging gameplay. However, despite the great potential of these advanced models, their use in game design is still an area that has not been fully explored, with limited systematic understanding of their capabilities, challenges, and future directions.This Scoping Review aims to provide a comprehensive overview of the current landscape of Transformer and Diffusion models in game design. By mapping out existing research, identifying key concepts, and highlighting gaps in the literature, this review seeks to establish a foundation for further exploration. It will analyze a broad range of studies to understand the diverse applications of these models, assess the methodologies employed, and explore the emerging trends in AI-enhanced game development. Through this process, the review will contribute to the broader discourse on AI's role in creative industries and inform future research efforts.
Multimodal Data Preprocessing and Community Detection in Social Media and Network Analysis
Authors: Zhang Haonan
Published: January 2025

This study targets multimodal data preprocessing and community detection in social media analysis. The research problem is to extract valuable insights from multimodal data and decipher social network structures for efficient information spread. The research employs a literature study, case examples, and algorithmic analysis to investigate the preprocessing steps for text and images and the Louvain algorithm for community detection. Results show proper preprocessing boosts data fusion, while community detection reveals network patterns, aiding marketing and public opinion management. Despite challenges, these techniques enhance data utility through solutions like multi-resolution algorithms, shaping better social media services and a more stable network environment.
The Progress and Trend of Intelligent NPCs in Games
Authors: Du Haodong
Published: January 2025

With the rapid development of artificial intelligence, non-player characters (NPCs) in games have become increasingly intelligent. NPCs undoubtedly enhance players gaming experience by allowing for better adjustments to game difficulty and narrative development through the optimization of NPC behavior logic. However, research on intelligent NPCs in games remains underdeveloped, highlighting the need for more systematic studies. To address this issue, we review the literature on intelligent NPCs and summarize the progress and trends of intelligent NPCs in games. We compare classical game NPCs with intelligent NPCs and conclude that continued development is essential for advancing intelligent NPCs. We should refine the scoring standards for NPC behaviors based on the content of different games, encouraging AI to exhibit human-like behaviors and enhance authenticity. Moreover, we should create an ideal environment for AI training to make the process more efficient and the results more effective. Furthermore, we propose the goals and challenges in the current development of NPCs. Finally, we summarize the progress and trends of intelligent NPCs in games to provide insights for future researchers.
A Review of Physics-Informed Neural Networks
Authors: Li Zhenyu
Published: January 2025

This article presents Physics-Informed Neural Networks (PINNs), which integrate physical laws into neural network training to model complex systems governed by partial differential equations (PDEs). PINNs enhance data efficiency, allowing for accurate predictions with less training data, and have applications in fields such as biomedical engineering, geophysics, and material science. Despite their advantages, PINNs face challenges like learning high-frequency components and computational overhead. Proposed solutions include causality constraints and improved boundary condition handling. A numerical experiment demonstrates the effectiveness of PINNs in solving the one-dimensional heat conduction equation, showcasing enhanced model stability and accuracy. Overall, PINNs represent a significant advancement in merging machine learning with physics.
The Impact Study of Mobile Payment on Household Asset Allocation: Evidence from CHFS Data
Authors: Xu Yuneng
Published: January 2025

Low household risk market participation has been a problem for a long time in China, this study explores the issue of Chinese household asset allocation from the perspective of mobile payment. Based on China Household Finance Survey data in 2017, this research studies the impact of mobile payment on Chinese household stock market participation and risk participation by using Probit model. Tobit and OLS model are used to analyze the impact of mobile payment on Chinese household risky asset allocation and the types of risky asset Chinese families hold. To reduce endogeneity caused by omitted variables and measurement errors, this study uses Instrumental Variable to make the estimation. The empirical results indicate that mobile payment can promote household stock market and risk participation by improving social interaction, social trust and financial literacy. Area heterogeneity analysis shows that mobile payment has a greater impact on eastern, first, second and third tier, and urban areas. Additionally, heterogeneity of family characteristics shows that mobile payment is more effective for highly educated households. This study provides a new perspective to conduct research on investment problems in China, and has extremely strong practical significance.
Should people invest in Bitcoin?
Authors: Xu Sicheng
Published: January 2025

I analyzed the historical performance of Bitcoin using its daily price data from October 2017 to January 2023, focusing on its unique characteristics and risk-return profile. The analysis began with an in-depth examination of Bitcoin's metrics, emphasizing factors such as maximum drawdown (MaxDD), a key measure of risk, and volatility, which highlights the asset's price fluctuations. Next, I extended the analysis to include four additional assets: GLD (gold), SPY (SPDR S&P 500 ETF Trust), DXY (U.S. dollar index), and QQQ (Invesco QQQ Trust). I evaluated these assets using metrics such as average return, volatility, Sharpe ratio, and maximum drawdown, comparing them directly to Bitcoin. This comparison revealed the relative strengths and weaknesses of Bitcoin against traditional and alternative assets, highlighting its potential for high returns at the cost of elevated risk and drawdowns. Following this, I constructed a portfolio of all five assets, optimizing for the highest Sharpe ratio, which balances return and risk. The analysis delved into why this specific asset combination produced the most optimal Sharpe ratio, considering factors like asset correlations and individual contributions to the portfolio's risk-return tradeoff. To further understand Bitcoin's role, I compared the optimized portfolio with and without Bitcoin, evaluating differences in Sharpe ratio, return, and volatility. This analysis underscored the conditional value of including Bitcoin, as its contribution depends on an investors risk tolerance and market conditions. Ultimately, I concluded that Bitcoin can be a worthwhile investment, but only under specific conditions where its high risk is offset by its potential for outsized returns and diversification benefits.
Differentiated Instruction in a Public Junior High School: EFL Teachers’ Perception and Practices
Authors: Padauleng A. , Abduh Amirullah , Samtidar Samtidar , Muslim Abu , Mujizatullah Mujizatullah , Massoweang Abd Kadir , Ridwan M. , Darmawati Besse , Rosidi Achmad
Published: January 2025

This qualitative case study was conducted in one of public junior high school in South Sulawesi, Indonesia. The study was aimed to explore the English teachers’ practices in differentiated instruction (DI) and their perception toward the curriculum policy. The data were collected through open-ended questionnaire and interview. The participants were EFL teachers who have implemented DI. The data were analyzed using thematic analysis. The findings elucidate English teachers' implementation, revealing three overarching themes: planning, implementation, and evaluation. In the planning phase, identification, curriculum analysis, and preparation emerged as crucial aspects. The implementation stage highlighted variations in teaching materials, guidance provision, and variation in evaluation. Evaluation encompassed assessment processes, learning outcomes, and reflective practices. The second result showed the teachers’ misconception regarding the implementation of DI, namely, chaos class and concern about fairness. The teachers found the challenges in implementing DI, namely determine assessment, class management and need extra time to allocate the DI. However, the implementation of DI gave the advantages in learning English, such as students’ engagement, understanding in learning and building inclusive environment. For the better quality of DI, the teachers need several supports from the policy makers.
Educational Environment and the Quality of Foreign Language Teaching: Perspectives of Kazakhstani Urban and Rural EFL Teachers
Authors: Gerfanova Elmira , Ismagulova Ainagul , Rakisheva Gulmira , Sabitova Diana , Yessengaliyeva Anna
Published: January 2025

The disparity in the quality of foreign language education between urban and rural secondary schools in Kazakhstan has become increasingly prominent in recent years. Despite the national goal of achieving B2-level proficiency, students in rural areas often encounter obstacles that impede their language learning progress. These challenges stem from factors such as limited access to qualified teachers, inadequate resources, socioeconomic disadvantages, and geographic isolation. Although previous researchers have recognized these factors, there remains a gap in understanding the perspectives of English as a foreign language (EFL) teachers regarding the quality of language education. To fill this gap, this study’s authors aim to explore the perspectives of EFL teachers in both urban and rural schools. Employing a mixed-method research design, the researchers integrate quantitative and qualitative approaches through surveys and semi-structured interviews. They administer a quantitative survey online with 524 participants, comprising 313 urban teachers and 211 rural teachers. Additionally, they conduct 20 individual semi-structured interviews with EFL teachers from secondary schools in Kazakhstan. The authors investigate six key components of the language educational environment that influence EFL teaching quality: 1) linguistic (teachers’ language proficiency), 2) sociopsychological (teacher–student interaction), 3) methodical (teachers’ professional development), 4) information and communication (ICT knowledge), 5) intercultural (teachers’ intercultural competence), and 6) managerial (teachers’ involvement in school administration). By conducting qualitative analysis of interviews and quantitative analysis of survey data, the researchers elaborate on characteristics of these components of the educational environment that may underlie the observed disparities in language learning outcomes.
Translation as a Tool for Language Acquisition: Perspectives of Saudi English Majors
Authors: Alenazi Yasir
Published: January 2025

This study aims to explore the perceptions of English major students on translation, its use as a learning strategy, and the relationship between these perceptions and translation practices. A quantitative approach was employed, gathering data through two types of questionnaires. The results indicated that English majors at a public university in Saudi Arabia consider translation as a valuable tool for acquiring English language skills and understanding various linguistic aspects. These students utilized translation to enhance their language competence in reading, writing, listening, speaking, grammar, and lexis. Additionally, the study reveals a strong connection between students' beliefs of translation and their application of it as a strategy for learning English as a foreign language.
Mediation as Intercultural Communication: A Narrative Review
Authors: Xirofotou Evangelia
Published: January 2025

Written mediation is a critical skill that enables individuals from diverse languages and cultures to communicate with each other effectively. Developing written mediation skills is essential in language learning classrooms and is included as a component of the National Foreign Language Exam System (KPG) for advancing harmonious global societies. This narrative review explores written mediation as a form of intercultural communication in English language classroom contexts. The review provides insights into the nature and purpose of written mediation, the role of the mediator, and the competencies needed to fulfill the role with the objective of providing the information for language learning classrooms to develop and instruct written mediation skills effectively. This review goes beyond traditionally held views of mediators as interpreters primarily concerned with linguistic aspects and underscores the close relationship between and the need for a broader conceptualization of mediation that includes both language and culture. In particular, critical cultural awareness is emphasized as a primary driver of effective mediation, underscoring the importance of addressing cultural components within the English language teaching context. English language teachers have a vital role in promoting the development of learners' intercultural competence, ultimately ensuring they are competent mediators equipped to facilitate intercultural encounters.
Assessing Pedagogical Strategies Integrating ChatGPT in English Language Teaching: A Structural Equation Modelling-Based Study
Authors: Zakarneh Bilal Ibrahim , Aljabr Fahad , Al Said Nidal , Jlassi Mohamed
Published: January 2025

This research investigates the impact of AI-powered ChatGPT technology on current pedagogical strategies, focusing on student engagement, language proficiency, and perceived usability. Data was collected through structured surveys from 301 English language learners at Ajman University (49.9%) and University of Hail (50.1%) in the United Arab Emirates and Saudi Arabia respectively. Grounded in constructivist learning theory, the results reveal strong positive outcomes. The integration of ChatGPT into pedagogical strategies significantly enhances student engagement, improves language proficiency (β=0.698, p<0.000), and increases perceived usability  among English language learners in higher education institutions in the UAE and Saudi Arabia. Respondents reported that these strategies made lessons more engaging and interactive, improved their language skills, and provided a user-friendly learning interface. These findings underscore the potential of AI tools like ChatGPT to revolutionize language education by offering interactive, personalized, and effective learning experiences. Additionally, the study supports the principles of constructivist learning theory, highlighting students’ positive attitudes and perceptions towards the integration of ChatGPT-enhanced pedagogical strategies,. Practical recommendations and study limitations are also discussed.
Linguistics in Literary Discourse: Exploring Meaning, Construction and Alternatives Approaches
Authors: Matiyenga Tinashe C. , Ajani Oluwatoyin Ayodele
Published: January 2025

This paper examines literary discourse patterns using the Appraisal Framework (Martin & White, 2005) and the Extended Pragma-dialectic Theory of Argumentation (van Eemeren & Grootendorst, 1984, 1992, 2004). It aims to demonstrate how these approaches offer systematic and logical methods for studying literature. By focusing on the linguistic tools through which authors and texts express, negotiate, and promote particular viewpoints, the study shows how these frameworks help reveal the ways writers convince readers of the acceptability of arguments. The Appraisal Framework provides insight into how texts reflect inter-subjective and ideological positions, while the Extended Pragma-dialectic Theory highlights how propositions justify or refute claims and counterclaims within a text. Furthermore, the paper explores the value of linguistics in literary analysis by integrating Bakhtin’s concept of dialogism. This perspective supports the idea that texts are inherently dialogic, reflecting a constant interaction of voices and ideas. By combining linguistic and literary analysis, the study argues that the Appraisal Theory and Pragma-dialectic Theory provide useful tools for understanding how texts persuade and engage readers in complex argumentation. Ultimately, it validates the relevance of these linguistic theories within literary studies.
Manajemen Lingkungan Sekolah untuk Mewujudkan Proses Pembelajaran yang Kondusif
Authors: Ruwaidah Ai Ira Siti , Adriawan Aura Nazwa Ar’zahra , Melisa Dhea Citra , Fitriani Feni , Hasanah Syifa Atiatul , Prihantini Prihantini
Published: January 2025

A conducive school environment is an important factor in supporting the effectiveness of the learning process. This research is based on the need for a holistic approach in school environment management, especially considering the challenges of globalization and digitalization in the 21st century education era. This research aims to explore the management of the school environment that can create a comfortable, safe, and productive learning atmosphere. This research was conducted using a qualitative research method based on literature review, which includes literature analysis from various reliable sources. The main source of this research is scientific articles published on the Google Scholar database. Data analysis is carried out qualitatively with the stages of data reduction, data presentation, and conclusion drawing. The findings show that the management of the physical environment, such as cleanliness, availability of facilities, and school layout, contributes greatly to student comfort. In addition, social aspects such as interpersonal relationships between teachers, students, and parents, as well as psychological aspects in the form of a sense of security and emotional support, also greatly affect student motivation and achievement. Collaboration between teachers, principals, students, and parents is necessary to create an optimal environment. These results provide important insights for educators to design sustainable and innovative school management strategies
Pengaruh Strategi Active Learning Terhadap Minat Belajar Matematika Siswa Kelas V SDN Tayem 01 Karangpucung
Authors: Tyas Apriliana Aisha , Febriyani Shoimatun , Isnawati Febri
Published: January 2025

Learning strategies have a considerable role in teaching and learning activities. The role of teachers in the learning process is very important, it is the teacher who designs learning methods, models, and strategies to achieve the expected learning goals, especially to attract the students' own interest in learning. This study aims to determine the influence of active learning strategies on mathematics learning interest. The research method used is a quantitative research method with a type of pre-experimental research. This research was carried out with a one-group pretest posttest design research design. Pretest and posttest are carried out by collecting data using questionnaires. Questionnaires and observation sheets were used in data collection. This research was carried out by providing active learning strategies to grade V students of SDN Tayem 01 Karangpucung in mathematics. The data obtained were analyzed using the t-test. The results of the study with the title The influence of active learning strategies on the interest in learning mathematics of Class V students of SDN Tayem 01 Karangpucung had a significant effect on an increase when learning using the active learning strategy.
Peran Media Sosial Sebagai Alat Publikasi Efektif untuk Membangun Hubungan Sekolah dan Masyarakat di SDN 181 Sukamiskin
Authors: Ihsani Hanipa , Fitriyati Nisa , Fatihaturohmah Nurul , Ihsani Virna Noor Adlida , Prihantini Prihantini
Published: January 2025

This study discusses the role of social media as an effective publication tool in building relationships between schools and the community at SDN 181 Sukamiskin. In the increasingly fierce competition for education, schools are required not only to rely on the quality of education, but also to actively promote themselves. Social media, such as Instagram, Facebook, and other social media have proven to be effective in conveying information quickly and interactively to the wider community. This study uses a descriptive qualitative method, with data collected through observation of school social media activities and interviews. Data analysis is carried out qualitatively with the stages of data reduction, data presentation, and conclusion drawing. The results of the study show that the use of social media is able to increase community involvement and support the achievement of school programs. Social media also plays a role in maintaining a positive image of the school in the eyes of the public
Tantangan dan Solusi dalam Pengelolaan Pendidikan Modern
Authors: Shafira Diva , Aditya Ferdino Arief , Anggia Intan Ratu , Putri Nasya Nurlillah Kusuma , Hanif Salman Al , Prihantini Prihantini
Published: January 2025

The management of modern education faces various challenges that continue to develop, especially in the face of technological changes and global needs. The education process now requires a more targeted approach, where various factors, including the quality of teaching, accessibility of education, and adaptation of the curriculum to the needs of the times, are increasingly becoming the main issues. This study uses a qualitative method that aims to describe the conditions that are occurring, the relationship between phenomena, views, processes, results, or certain tendencies in a context. The data collection techniques used are observation, literature study, and documentation. Literature Studies are conducted as a basis to analyze various perspectives and problem solving in education management in modern times. The data produced was analyzed in a qualitative descriptive manner. The results obtained from this study are that a series of solutions are needed which include the construction of equitable educational facilities, the use of technology that can reach remote areas, the improvement of teacher competence and welfare, and curriculum reform that is more responsive to the needs of the times and the world of work to realize the management of modern education in Indonesia
Pengaruh Game Baamboozle Terhadap Minat Belajar Siswa pada Mata Pelajaran Geografi di SMA Negeri 8 Bengkulu Utara
Authors: Triwulandari Septi , Rahmi Lailatur
Published: January 2025

This study aims to determine the influence of the baamboozle game on geography subjects on students' learning interest at SMA Negeri 8 North Bengkulu. This research method uses quantitative research with an experimental type. The sample determination technique uses purposive sampling. The sample size was 68 respondents. Divided into experimental and control groups. Some of the data collection techniques used in this study are questionnaires, observations, and documentation. Data analysis techniques include normality tests, homogeneity, n-Gain, and hypothesis tests. The results of the analysis showed that there was an effect of the baambozle game on the learning interest of geography students of SMA Negeri 8 North Bengkulu in studying the subject. The learning interest of students in experimental classes that use baamboole games as a medium is more effective compared to the traditional classroom method (lecture). It shows the influence of the baamboozle game on students' learning interest in the experimental class. The independent sample t-test hypothesis test showed significant values of 0.004 < 0.050. It can be concluded that there is an influence of the baamboozle game on students' interest in learning geography subjects at SMA Negeri 8 North Bengkulu
Green Supply Chain Management and Environmental Performance in Small, Medium and Large-Sized Italian Manufacturing Companies
Authors: Mazraani Ghassan , Giovannetti Giorgia , Capone Pietro , Fava Salvatre
Published: January 2025

With the world moving towards global warming, a growing population, and the reduction of non-renewable resources, Green Supply Chain Management (GSCM) has recently received increased attention. While green practices are essential in developing a more sustainable performance, the effectiveness of those practices may vary from company to company. The purpose of this study is to showcase various green practices adopted and implemented by companies of different sizes to achieve varying levels of environmental performance. Moreover, to ensure higher sustainability, a company could adopt strategies with the adoption of green practices. The present study is quantitative in nature. The study uses a survey of 100 randomly selected small, medium, and large manufacturing Italian companies listed in the “Bureau van Dijik Electronic Publishing” database about their green practices and environmental performance. The results were analyzed using SPSS. It aimed to analyze whether any relationship existed between different green practices and their impact on environmental performance. This research contributes positively to the literature on the association between GSCM practices and environmental performance. It establishes that company size significantly affects the number and types of green practices implemented and how these can be effectively executed to improve environmental outcomes. The study’s findings tested the hypothesis that different-sized companies integrate different Environmental Management practices and GSCM practices. The study revealed that large companies are more likely to implement green practices than small ones. The findings provide practical insights to managers, particularly those in the manufacturing and logistics sectors, about the GSCM practices likely to yield the best performance benefits. The research offers a nuanced approach to implementing various environmental management practices and to adopting GSCM strategies for a sustainable environment.
MS-related fatigue: towards implementation of an evidence-based treatment
Authors:   , de Gier Marieke
Published: January 2025

Although fatigue in Multiple Sclerosis (MS) can be effectively treated with cognitive behavioural therapy (CBT) for fatigue, implementation of this treatment in clinical practice is hindered by limited therapist capacity. In addition, long term effectiveness of CBT is unclear, leaving room for improvement. This thesis focussed on testing the effectiveness of a blended treatment format of cognitive behavioural therapy for MS-related fatigue and improving the long-term outcome of this treatment. In addition, this thesis aimed to increase our understanding of factors related to fatigue and the treatment response to CBT, in order to seek opportunities to further improve treatment of severe fatigue in people living with MS. and other chronic conditions In the future.
A Survey-based Inquiry and Future Foresight of ‘Research’ in the Arab World
Authors: Al-Hawamdeh Dr. Mohammad Amin
Published: January 2025

The current state and future directions of scholarly research in the Arab world are examined in this study. A group of Arab individuals with PhDs and master’s and bachelor’s degrees made up the study’s sample. They were asked to answer twelve items in two primary categories—conditions and horizons—on an online questionnaire. A number of issues confronting Arab research endeavors were illustrated, most notably the difficulties necessitating collaboration among all relevant sectors, more funding, and public awareness for the advancement and prosperity of societies. In any case, scholarly research was identified as a priority that needs greater focus for further socioeconomic progress in the Arab world and, consequently, support worldwide growth. The study’s conclusions led to a number of recommendations and suggestions, including greater funding for research from the public and commercial sectors to provide any necessary financial and technological resources. In order to encourage employees at the relevant institutions to put out more effort in a sufficient and productive manner, it is necessary to improve the research environment(s) by providing suitable infrastructures (such as laboratories, facilities, equipment, and apparatuses). In the end, it became clear that all parties involved were eager to improve the current state of ‘research’ in the Arab world and imagine a better future that fosters creativity and scientific advancement in the area.
DEFISIT ANGGARAN DAN HUTANG PUBLIK: DAMPAK JANGKA PANJANG TERHADAP PERTUMBUHAN EKONOMI
Authors: Adila Nesria , Dwi Gebi Sintia , Ningsih Rahayu , Nuraiman Nuraiman , Wahyuni Sri , Wulandari Wiranda , Julian Marval
Published: January 2025

Defisit anggaran dan hutang publik salah satu fenomena yang sering ditemui dalam pengelolaan keuangan. Defisit anggaran dapat digunakan untuk membiayai pembangunan dan dapat merangsang pertumbuhan ekonomi dalam jangka pendek, dan dampak jangka panjangnya terhadap perekonomian dalam suatu negara sehingga dapat menimbulkan resiko yang sangat signifikan. Tujuan peneliti sendiri yaitu untuk mengetahui pengaruh dan dampak defisit anggaran dan hutang publik sebagai dampak jangka panjang terhadap pertumbuhan ekonomi. Metode yang peneliti gunakan adalah metode kualitatif dengan pendekatan melihat berbagai referensi dan literatur. Hasil dari referensi tersebut peneliti mendapatkan beberapa poin penting defisit anggaran dan hutang publik sebagai dampak jangka panjang terhadap pertumbuhan ekonomi. Hasil penelitian ini dapat dijadikan sebagai data awal untuk peneliti berikutnya dalam mengkaji defisit anggaran dan hutang publik dampak jangka panjang terhadap pertumbuhan ekonomi.
A One Stop Solution Focusing on Tourism
Authors: Journal IJSREM
Published: January 2025

The "A One Stop Solution focusing on Tourism" is a multi-purpose application that focuses on
simplifying the booking and management of hotels, events, and cabs. It is also a centralized system
for the users, hotel administrators, and event organizers. There is an admin module for
administrators to manage hotels, events, and cab services as well as all bookings. With it, the hotel
managers can input details about their properties, monitor bookings, and manage reservations.
Event managers can input event details, monitor bookings, and track attendees' registration. Cab
module allows a user to book, manage, and check his or her current booking and necessary update.
Users can log in and register themselves, allowing them to view all the available hotels, events, and
cabs and make easy planning and management of a trip. The platform provides an all-inclusive
solution by converging hotel, event, and cab booking features to enhance user experience and
operational efficiency. This system ensures an easy process for service providers as well as the users,
guaranteeing convenience and satisfaction. Because all services are centralized in one location, it
becomes an effective way of handling travel and event requirements. The project deals with
streamlining the management of these services and provides a user-friendly interface. In the final
analysis, One Stop Solution maximizes convenience, operational control, and user satisfaction.

Keywords – Web Application, Booking, Management, Hotels, Events, Cabs, Admin Module, User
Module, Hotel Managers, Event Organizers, Cab Services, Reservations, Attendee