bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Bioprinting Innovations: Pioneering Solutions for Cardiac Disease
Authors: Song Hui
Published: November 2024

Abstract. Cardiac disease persists as one of the leading causes of morbidity and mortality worldwide and therefore there is a critical need for the establishment of innovative treatment approaches. This review updates the innovations in 3D bioprinting technology and its applications in cardiac tissue engineering, a fast-developing field that aims to overcome the limitations of the current treatments of cardiac diseases. The 3D bioprinting technique enables the layer-by-layer construction of complex tissue structures emulating natural cardiac tissue in architecture and function using bio-inks composed of living cells, biocompatible materials, and growth factors. The paper reviews inkjet-based, extrusion, and laser-assisted bioprinting techniques, all having different advantages and challenges. It further identifies the need for proper biomaterials, namely natural and synthetic polymers, to facilitate cellular growth and differentiation, as well as the 4 main cell types utilized in the bioink. The 3D bioprinting applications will be reviewed in the fabrication of heart patches and prosthetic heart valves and their potential to improve cardiac repair and regeneration will be described. Although these developments are quite promising, challenges relating to scalability, cell viability, and regulatory considerations remain. The conclusion drawn is that further research in the refinement of bioprinting methodologies and integration with advanced technologies is required so that cardiac care can be revolutionized with personalized and effective therapeutic solutions.
Treatment and Prevention Strategies of Breast Cancer
Authors: Liu Yixuan
Published: November 2024

Abstract. With the increasing trend of late childbearing and low childbearing, the incidence of breast cancer is increasing rapidly worldwide. At present, breast cancer has become one of the more frequent malignant tumor diseases in women and has aroused widespread concern in society. The cause of the disease is not completely clear, with the deepening of research, the relevant detection methods are constantly updated, as far as possible early diagnosis. At present, the main methods of breast cancer prevention are lifestyle adjustment, including reducing alcohol intake, maintaining a healthy weight and chemical prevention, but the methods are limited, so the research on breast cancer prevention is particularly important. At the same time, medical workers should educate the majority of women about disease prevention, so that women can establish a correct concept of prevention and treatment, so as in order to lower the prevalence of breast cancer and improve the women health.
Study on the Effect of Cinnamon in the Treatment or Auxiliary Treatment of Type 2 Diabetes
Authors: Yang Taixiang
Published: November 2024

Type 2 Diabetes Mellitus(T2DM) is one of the fastest-growing global health emergencies of the 21st century. Currently, more than 500 million people worldwide are living with diabetes. According to the International Diabetes Federation, it is expected to affect 592 million people by 2035 and 784 million by 2045. Therefore, the purpose of this paper is to explore the feasibility of natural medicine in the treatment of type 2 diabetes. Cinnamon, as a natural product, has been used as a spice or traditional Chinese medicine for thousands of years. The present clinical trials on the treatment of type 2 diabetes with cinnamon as an adjuvant were analyzed. By comparing the data of Fasting blood glucose(FBL), Glycosylated Hemoglobin(HbA1c), Total cholesterol(TC) and Triglyceride(TG) in different clinical trials, it can be concluded that cinnamon can be used to help treat patients with type 2 diabetes.
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Published: November 2024

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The Correlation Between Supply Chain Management and Sustainable Development: A Case Study of Coffee from Baoshan, Yunnan
Authors: Zheng Jiaqi
Published: November 2024

This paper conducts an in-depth analysis of the coffee supply chain and its relationship with sustainable development in Yunnan Province, using Baoshan coffee as a case study. The research reveals that local coffee enterprises in Baoshan primarily focus on selling coffee raw materials, operating at the lower end of the value chain with relatively low added value. The main obstacles to their development are issues related to the organizational, technological, informational, and financial aspects of the supply chain, particularly concentrated in the critical upstream and midstream segmentsplanting and production. To address these challenges, the paper proposes an innovative development approach for the Baoshan coffee supply chain based on the integration of the "Four Chains." This approach explores sustainable development strategies for the Baoshan coffee supply chain from the perspectives of organizational, technological, informational, and financial chains, incorporating modern technologies such as the Internet, IoT, and big data, as well as concepts of supply chain integration and supply chain finance. The goal is to establish a robust coffee supply chain environment and achieve sustainable development for Baoshan coffee.
Advancing Image Animation: A Comparative Analysis of GAN-Based and Diffusion-Based Models
Authors: Sun Yikai
Published: November 2024

Abstract. This paper provides an in-depth analysis of the latest advancements in image animation, focusing on two prominent models: Motion Representations for Articulated Animation (MRAA) and MagicAnimate. MRAA revolutionizes Generative Adversarial Networks (GANs)-based animation by employing regional descriptors instead of traditional key points, significantly enhancing the accuracy of motion capture and segmentation for complex articulated movements. MagicAnimate, on the other hand, utilizes a diffusion-based framework with temporal attention mechanisms, ensuring high fidelity and temporal consistency across animated sequences. The paper discusses the methodologies, datasets, and preprocessing techniques used in these models, offering a thorough comparison of their performance metrics, on various benchmark datasets. Through this comparative analysis, the paper highlights the strengths and limitations of these cutting-edge technologies, emphasizing MRAAs superior handling of complex movements and background dynamics, and MagicAnimates excellence in identity preservation and temporal coherence. The study concludes by proposing future research directions, such as developing hybrid models that combine the advantages of GANs and diffusion techniques, to further enhance the realism, versatility, and control of image animation systems.
Applications of Artificial Intelligence on Autonomous Driving
Authors: Guan Chule
Published: November 2024

Abstract. Artificial intelligence (AI) technology has rapidly developed in recent years and has gradually permeated various aspects of everyday life. The integration of AI with driving technology has given rise to autonomous driving, a technology that is expected to profoundly impact human transportation, efficiency, and quality of life. This paper provides a detailed exploration of the specific applications of AI in the field of autonomous driving and its future development prospects, analyzing the advantages and challenges of these technologies. It also briefly introduces practical application scenarios such as autonomous taxis, intelligent traffic management systems, and long-distance freight transportation, discussing the potential impacts of these technologies on society and the environment. Finally, the paper looks ahead to the profound changes that may result from the combination of autonomous driving technology with other cutting-edge technologies, such as 5G and the Internet of Things (IoT), in shaping future transportation systems.
The Advantages and Challenges of the Future Development of Intelligent and Automatically Adjustable Prescription Glasses
Authors: Hao Yifei
Published: November 2024

Abstract. The latest intelligent and automatically adjustable prescription glasses are mainly a combination of smart glasses and AI algorithms - adding some basic information about the customer's eyes, such as left and right eye vision, eye curvature, eye wheelbase, etc., to the device bound to the smart glasses. AI will use this basic information to estimate the approximate monthly increase in prescription, and automatically change the thickness of the lens through a driver to achieve the function of automatically adjusting prescription for the customer. In the future - an era where the population of myopia is rapidly growing and the cost of corrective lenses becomes a burden for many people - smart adjustable degree glasses not only have cost-effectiveness and adaptability to the times, but can also monitor eye health and provide real-time feedback to users. This has the great potential to change the game rules for managing myopia progression, especially for young people at the highest risk. But to achieve this vision, the technological challenges are also extremely high - the current development status of machine learning, sensor technology, and miniaturization technology is not sufficient to support the development of intelligent adjustable degree glasses; In addition, significant technological improvements are needed for battery life, comfort, data accuracy, and the adaptability of AI models to different user needs.
Impact of SVM-based Poisoning on the Semantic Recognition of Sounds
Authors: Jiao Shuobo
Published: November 2024

Abstract. Machine learning is a technique that enables computers to learn from data and make predictions or decisions, data poisoning is the process of machine learning training where malicious samples are put in to make the model predictions or classifications less accurate. Data poisoning attacks help to reveal security vulnerabilities in AI systems. In this paper, we study Support Vector Machine (SVM) poisoning for sound recognition techniques, using AISHELL-3 dataset training data, from which we find the most vulnerable features for SVM poisoning. In the field of speech recognition, SVM can be applied to speech feature extraction, speech classification and speech synthesis to find the best hyperplane by finding the maximum margin optimization for effective classification and recognition of speech signals. Experiments have resulted in biased semantic recognition of sounds, output of incorrect speech, reduced accuracy of model classification and generation of incorrect decision boundaries. The role of this research paper is to investigate whether SVM poisoning affects the semantic recognition of sounds, and the result of the research is that it does cause semantic bias.
Application and Problems of AI in Game Development
Authors: Zhao Ziye
Published: November 2024

The application of artificial intelligence (AI) in game development has significantly transformed the dynamics, interactivity, and personalized experiences of games. This paper explores the key applications of AI in game development, including procedural content generation (PCG), non-player character (NPC) behavior and control, as well as automated game testing and quality assurance. PCG enhances replayability and user engagement by automatically generating game content, such as levels and maps, through algorithms. AI-driven NPC behavior makes game characters more adaptive, intelligent, and lifelike, thereby enhancing player immersion. Automated testing utilizes machine learning algorithms to improve testing efficiency and game quality. Despite the numerous advantages that AI brings, there are also challenges related to ethics, technology, creativity, and economics. This paper discusses the potential benefits and challenges of AI in game development and emphasizes the importance of responsible and sustainable application of AI technologies.
The Application of Artificial Intelligence in Enhancing the Sense of Reality in Games
Authors: Nie Mingze
Published: November 2024

With the rapid development in the field of computer science, game development has emerged as one of its significant branches. Artificial Intelligence (AI), as an emerging discipline, has become increasingly integrated with the field of game development, offering new possibilities for enhancing the sense of realism and player experience in games. This paper delves into the application of AI in enhancing the sense of reality in games. Firstly, the paper analyzes the importance of the sense of reality in games and explores how AI can enhance the player's immersive experience by simulating game character behaviors. Secondly, the paper discusses the application of AI in game physics and environmental simulation. Next, the paper explores the application of AI in enhancing game audio and visual effects. Additionally, AI's application in image generation and enhancement technologies has raised the quality of game graphics to new heights. Lastly, the paper discusses the application of AI in the field of VR and AR, and how it can enhance user perception by optimizing hardware performance and providing more realistic physical experiences. The paper examines the potential of AI in improving the emotional experience of users and provides an outlook on future trends in game development.
Gesture Based Control Technology for Drones: Challenges and Solutions
Authors: Wang Jiabao
Published: November 2024

Gesture-based control systems have become an innovative approach in human-computer interaction, enabling users to operate devices like UAVs using hand movements. These systems generally consist of three main components: sensors to capture movements, gesture recognition algorithms to interpret them, and control interfaces to execute actions. However, several challenges still exist that limit the performance and practicality of these systems. Key problems include accuracy in varying environmental conditions, latency issues caused by sensor overload, and user ergonomics, which can affect usability over time. For instance, sensors like optical cameras and IMUs may struggle in low-light or complex environments, leading to errors in gesture interpretation. Moreover, real-time processing is essential for seamless control, but the high computational demands of machine learning algorithms often lead to delays. This paper explores these challenges in detail and presents solutions, such as using multi-sensor setups to improve data accuracy, implementing adaptive algorithms that can learn and adjust to user behavior, and refining control interfaces with real-time feedback mechanisms to enhance the user experience. Addressing these issues will make gesture-based control systems more efficient, reliable, and user-friendly, enabling wider application across industries like virtual reality, robotics, and military operations.
Research on the Application of Machine Learning in the Field of Heart Disease
Authors: Ding Hongli , Li Weilin
Published: November 2024

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.
Overview of Visible Light Communication: Past, Present, and Future
Authors: Ge Xincheng
Published: November 2024

Visible light communication (VLC) is an efficient wireless communication with great development prospects, which is closely related to our daily lives especially for indoor applications. This paper first introduced the development process of VLC, and some significant milestones were emphasized. Then some key technologies, including OOK, VPPM, CSK, OFDM, PWM, were mentioned to provide readers with a deeper understanding of the working principle of VLC. In addition, the paper discussed some prospects of VLC, as well as its practical applications classified based on technical principles. Moreover, the author also pointed out the current difficulties and challenges, specifically mentioning the difficulties faced by OFDM technology in VLC system and its current development process. Some suggestions were proposed for future advancement. With continuous exploration and research, VLC will flourish in future wireless communications.
Mutual advancement: How integrated circuits interact with artificial intelligence
Authors: Wang Linye
Published: November 2024

Abstract. Integrated circuits and artificial intelligence complement each other. On one hand, the continuous optimization of electron and photon accelerators can reduce the energy consumption of artificial intelligence algorithms and adapt them to edge computing, thereby promoting the development of artificial intelligence. On the other hand, artificial intelligence not only assists in troubleshooting and optimizing of integrated circuits, but also automates the design of integrated circuits, making them more convenient and efficient. The interactive relationship between integrated circuits and artificial intelligence is presented through qualitative analysis and literature review in this paper. The mutual influence of these two technologies creates new breakthroughs and possibilities for the development of science and technology. However, while photonic artificial intelligence accelerators are suitable for high-speed neural networks, their current development is not yet mature due to the high cost, complex manufacturing process, and environmental disturbances. Additionally, artificial intelligence algorithms for integrated circuits design lack sufficient high-quality data for training due to the secrecy of integrated circuit parameters. Thus it requires a long time to train and perfect these algorithms.
Comparison Impacts of Iteration Quantities on Loss Function for Different Data Sets
Authors: Wu Di
Published: November 2024

Abstract. As machine learning is applied to more and more high-tech fields, optimizing tasks has become a key step in the pursuit of more efficient and accurate artificial intelligence. Thes study will present an overview for machine learning and address the implementation of distributed machine learning, including cleaning the collected datasets, selecting different models, and discussing the influence of loss function types (L-smooth and non-L-smooth) on error convergence under the premise of increasing the number of iterations. Experiments show that the two types of loss functions have opposite results. For the L-smooth loss function, larger iterations reduce the error convergence. Regarding to the non-L-Smooth loss function, larger iterations increase the error convergence. This research also adjusted other model parameters and analyzed them, which has more meaningful implications for the parameter selection of machine learning model training in the future, and one can optimize error convergence by changing the type of loss function, so that improve the efficiency of scientific research.
Electrocardiogram Diagnosis Based on Spiking Neural Networks
Authors: Yin Zhenyu
Published: November 2024

Abstract. Electrocardiogram (ECG) diagnosis is pivotal in clinical practice, providing an essential means for early heart disease detection. However, traditional automated diagnostic approaches often face challenges such as limited accuracy, noise sensitivity, and inadequate real-time performance. This paper proposes a novel solution utilizing Spiking Neural Networks (SNN) to address these challenges. This method combines Convolutional Neural Networks (CNN) with Leaky Integrate-and-Fire (LIF) spiking neurons, offering advantages like low energy consumption, high computational efficiency, and enhanced robustness, particularly in dealing with noise in ECG signals. The MIT-BIH Arrhythmia Dataset, a widely recognized resource for arrhythmia detection containing annotated ECG signals for various heart conditions, is utilized in this study. The results indicate that the proposed method achieves a 97.98% test accuracy, significantly surpassing traditional Long Short-Term Memory networks (LSTM)-based architectures. These findings underscore the effectiveness and potential of this approach in ECG signal analysis.
Movie Recommendation System Based on Collaborative Filtering Network
Authors: Tang Ke
Published: November 2024

Abstract. In the era of big data, personalized recommendation systems have become crucial for enhancing user experience and improving information retrieval efficiency, especially in the movie recommendation domain. This study proposes a method based on Collaborative Filtering Network (CFN), utilizing deep learning techniques to construct an efficient and accurate movie recommendation system. We evaluated the model's effectiveness using the MovieLens dataset. The experimental results demonstrate that the proposed CFN model performs well across multiple metrics, including Hit Ratio, providing new insights for research in the recommendation system field.
Monitoring the Mental State of the Elderly in the Community Based on Machine Learning
Authors: Hu Tongjia , Lee Chi-Han
Published: November 2024

The rapid aging of the population has brought increasing attention to the mental health of elderly individuals. This study focuses on the mental well-being of elderly residents in several communities in Beijing. Through comprehensive surveys conducted in key communities with significant elderly populations, this paper gathered data to assess their mental health status. Subsequently, this paper employed various machine learning models, including Random Forest, Support Vector Machine, and XGBoost, to classify and predict mental health outcomes. The findings of this paper indicate that the Random Forest model outperformed the other models in accurately identifying mental health issues. Key predictors included factors such as whether the individual had received psychological counseling, age, social support, physical health status, and frequency of social interactions. This study provides empirical evidence that can guide healthcare providers and policymakers in developing more effective mental health management strategies for the elderly.
Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
Authors: Gong Yuhao , Zhang Yuchen , Wang Fei , Lee Chihan
Published: November 2024

As global climate change intensifies, accurate weather forecasting has become increasingly important, affecting agriculture, energy management, environmental protection, and daily life. This study introduces a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict historical temperature data. CNNs are utilized for spatial feature extraction, while LSTMs handle temporal dependencies, resulting in significantly improved prediction accuracy and stability. By using Mean Absolute Error (MAE) as the loss function, the model demonstrates excellent performance in processing complex meteorological data, addressing challenges such as missing data and high-dimensionality. The results show a strong alignment between the prediction curve and test data, validating the model's potential in climate prediction. This study offers valuable insights for fields such as agriculture, energy management, and urban planning, and lays the groundwork for future applications in weather forecasting under the context of global climate change.
Leveraging EMG Signals for Precision Control
Authors: Li Chengze
Published: November 2024

This study explores the application of electromyography (EMG) signals for controlling mechanical systems using an Arduino Uno microcontroller, an Olimex EMG shield, sEMG electrodes, and a servo motor. EMG signals have been used for various applications such as prosthetics and assistive devices, proving to be a reliable source for control mechanisms. The experiment involves initial EMG testing, calculating RMS voltage, and integrating servo motor control. Results show that EMG signals can effectively control a servo motor, with improvements achieved through filtering, PID control, and multi-motor setups. Advanced techniques, such as Neural Networks, Reinforcement Learning, and Model Predictive Control, were also explored to enhance system performance. This research demonstrates the potential of EMG-based control systems in robotics and prosthetics, highlighting their future applications and innovations in adaptive technologies.
Applications of Ultrasonic Sensors: A Review
Authors: Wei Yutong
Published: November 2024

Ultrasonic sensors are devices that measure distance and detect objects without physical contact by emitting and receiving high-frequency sound waves. This study explores the technology and applications of ultrasonic sensors, with a focus on their use in the automotive and agricultural sectors. In automotive systems, ultrasonic sensors are widely used in parking assistance, autonomous driving, and blind spot monitoring to enhance safety and efficiency. In the domain of agriculture, ultrasonic sensors are integrated into precision agriculture techniques for the management of irrigation, the surveillance of crops, and the identification of obstacles. The research draws on existing literature and case studies from industrial and agricultural applications to analyze the effectiveness of ultrasonic sensors under various environmental conditions. The findings indicate that while ultrasonic sensors are cost-effective and highly reliable for short-range detection, they face limitations in long-distance detection and adverse weather conditions. With the advancement of artificial intelligence and sensor fusion technologies, the future of ultrasonic sensors looks promising, and their role in smart systems such as autonomous driving and intelligent agriculture is likely to expand significantly.
A Survey of Incentive Mechanisms for Federated Learning
Authors: Zhou Yuzhe
Published: November 2024

Federated learning is a cutting-edge distributed machine learning technique that lowers the possibility of data leaking from centralized uploads by enabling users to cooperatively train models. It not only protects client privacy but also better utilizes decentralized data resources, demonstrating high learning performance. To employ their models in the training process, however, a significant number of clients must be involved, which inexorably uses up the device's resourcescomputational power, communication bandwidth, and energy. Therefore, incentivizing more clients to participate in federated learning is crucial. Typical methods can provide numerical or model-based compensation to ensure they contribute their valuable model resources. This paper provides a brief introduction to federated learning and incentive mechanisms, and surveys related research on federated learning incentives. Specifically, this paper categorizes existing federated learning incentive mechanisms into four technical approaches: Shapley values, Stackelberg games, auctions, and contracts. Finally, the paper discusses some future directions for incentivizing clients in federated learning.
An Overview of Key Technologies for Wireless Charging Systems for Unmanned Aerial Vehicles
Authors: Yang Yirun
Published: November 2024

With the demand for an intelligent and modernized society, the application of UAVs has become increasingly widespread. However, their performance is limited by battery capacity constraints, making UAV wireless charging an effective solution for prolonging operational time. This thesis focuses on the research of shutdown technology in UAV wireless charging systems. Firstly, it introduces the current research status in this field. It then comprehensively covers the principles of wireless energy transmission, with a particular focus on magnetic coupling resonance. Next, it addresses key issues such as compensation topology mechanisms and coupling coil circuit analysis. Following that, a technical analysis is conducted based on hover charging and landing charging, respectively. Finally, the thesis summarizes the current progress of UAV wireless charging and provides an outlook on areas for improvement in related research.
Research on the Elderly Fitness Fall Risk Prediction Model Based on Decision Tree
Authors: Han Jinxiu , Huang Jiayue , Luo Yutong , Yang Ping
Published: November 2024

As the global aging process intensifies, the issue of falls has become a common health risk in the daily lives of the elderly, especially in physical fitness activities. This article establishes a fall risk prediction model using a decision tree algorithm based on fitness data of the elderly and analyzes the relationship between various health factors and fall risk. The study found that features such as heart rate variability, blood sugar levels, and exercise duration have significant impacts on fall prediction. Through the analysis of the ROC curve and PR curve of the model, the results show that the model performs well in the prediction accuracy and risk identification of fall events. The research in this article not only provides a predictive tool for the safety of the elderly in fitness but also provides a theoretical basis and practical guidance for preventing falls in the elderly and formulating personalized safe exercise plans.
Building capacity to lead: Development implementation and evaluation of a leadership and management education program for nurse leader-managers
Authors: Bookey-Bassett Sue , Thomson Heather , Chee Janet , Bajnok Irmajean , Miscio Victoria , Cook Kim , Leybourne Donna , Naik Shanoja
Published: November 2024

Background/Objective: Nurses in leadership and managerial roles require ongoing opportunities to develop their leadership knowledge and skills. Effective nursing leadership contributes to team functioning and healthy work environments, as well as improved outcomes for staff and patients. This paper reports on the development, implementation and evaluation of a Leadership and Management for Nurses Program (LMNP) in Ontario, Canada. The program was designed for novice nurse leader-managers to enhance knowledge and skills necessary to promote competence, confidence, and effectiveness in these roles.Methods: A program evaluation framework was developed to evaluate the LMNP including the degree to which the program supported participants in achieving their leadership goals and the impact of the program on participant leadership and confidence. Upon completion of the program all participants were invited to respond to an electronic evaluation survey exploring both program processes and outcomes. Descriptive statistics were used to analyze survey data.Results: Fifty-four participants responded to the survey. Results suggest that the program length and content were appropriate, contributing to the achievement of leadership learning goals and the development of leadership and managerial skills and knowledge, increased confidence and overall preparedness to lead.Conclusions: Novice nurse leader-managers require support to develop and maintain leadership and managerial competencies, underscoring the importance of providing leadership development opportunities through programs such as the LMNP. Healthcare organizations should consider how best to support ongoing nurse leader-manager development through leadership training and mentorship programs.
Dampak Media Sosia terhadap Kesehatan Mental Siswa Remaja
Authors: Jalal Novita Maulidya
Published: November 2024

The aim of this research is to determine the impact of social media on the mental health of adolescent students. The method in this research is library research which uses 9 scientific articles related to the impact of social media on the mental health of adolescent students. Scientific articles were obtained from national journal literature, nationally accredited journals and international journals. The analysis used was content analysis. The results of this research show that apart from having a positive impact on teenage students, social media also has a negative impact on the mental health of teenage students, including feelings of loneliness and emotional disturbances, stress, addiction and dependence on using social media, cyberbullying, reducing the quality of teenagers' critical thinking. , mood and anxiety disorders, lack of sleep, decreased work productivity, less time with other people, stressed mood, addiction and depression, addiction to pornographic media, and even depression. The conclusion of this research is that although social media can provide benefits in terms of social relationships, its negative impact on the mental health of adolescent students cannot be ignored. Collaborative efforts between parents, educators and teenagers themselves are needed to maximize the benefits of social media while minimizing the risks posed
Efektivitas Media Google Sites untuk Meningkatkan Pembelajaran IPAS Bagi Anak Tunarungu
Authors: Fepiana Indrika , Iswari Mega , Nurhastuti Nurhastuti , Zulmiyetri Zulmiyetri , Yuliana Syari
Published: November 2024

This research is motivated by the problem found in phase C, namely a deaf student who has difficulties in learning science and science in the material where living things live. This study aims to prove whether the use of google sites learning media is effective in improving the learning of deaf students in science and science subjects in the material where living things live in SLB Negeri 1 Pariaman. This study uses a type of experimental research in the form of Single Subjeck Research (SSR) with an A-B-A design. The subjects in this study are phase C deaf students at SLB Negeri 1 Pariaman. The data collection techniques in this study are observation and tests. The data was analyzed using qualitative and quantitative descriptive. The results of the study showed that in the baseline condition (A1) which was carried out three observations and obtained a stable percentage of 20%. In the intervention condition (B) which was carried out 5 times with a stable percentage of 100%. In baseline conditions (A2) which were carried out 3 times, a stable percentage of 100% was obtained. Based on the results of the study, it can be concluded that google sites media is effective in improving students' ability to understand science and science learning on the material where living things live for deaf students
Hubungan Persepsi Terhadap Harapan Orang Tua dengan Stres Akademik pada Siswa Kelas Unggul Tahfidz di MTsN X Bukitinggi
Authors: Yulianti Fitri , Yusra Zulmi
Published: November 2024

This study aims to investigate the correlation between perception of parental expectations and academic stress in tahfidz class students in MTsN 1 Bukittinggi city. This study uses a correlational quantitative method. Data collection was carried out offline by distributing questionnaires. The technique in taking samples was carried out in total sampling, involving 95 students from the superior class of tahfidz grades 7, 8, and 9 as participants. The data produced was analyzed using a product moment correlation test. The results of this study showed a significant positive correlation between students' perception of parents' expectations and the level of academic stress among students of the superior class of tahfidz in MTsN 1 Bukittinggi City, with a correlation coefficient value of 0.705. This shows that the higher the perception related to parental expectations, the higher the academic stress of tahfidz class students in MTsN 1 Bukittinggi City.
Pemanfaatan Kebun Sayuran Sekolah sebagai Media Pembelajaran dalam Meningkatkan Produktivitas Peserta Didik di SDN Bulusidokare Sidoarjo
Authors: Wati Evi Indar , El-Yunusi Muhammad Yusron Maulana
Published: November 2024

Vegetable garden does not only serve as a source of food but also as an educational tool that supports experiential learning. This research was conducted at Bulusidokare Sidoarjo Public Elementary School. Through gardening activities, students can develop practical skills, an understanding of the plant life cycle, and environmental values. This research aims to explore the utilization of vegetable gardens as an innovative learning medium to enhance student productivity. This research method uses a qualitative approach by conducting interviews with teachers and students, field observations, and collecting supporting documentation. The author describes the findings as data analysis. The research results show that student involvement in gardening activities can enhance motivation, teamwork skills, and a sense of disciplined responsibility. In addition, this experience strengthens the understanding of scientific and environmental concepts, as well as enhances the love for nature. Thus, vegetable gardens can be used as an effective learning medium to enhance the productivity and quality of students' education.
Penerapan Model Problem Based Learning (PBL) dalam Membentuk Kemampuan Berpikir Kritis Siswa SD Talenta Al-Fatih Saimbang Sidoarjo
Authors: Umala Risa , El-Yunusi Muhammad Yusron Maulana
Published: November 2024

The purpose of this study is to identify and analyze students' critical reasoning skills in the Natural Sciences lesson of Talenta Al-Fatih Sidoarjo elementary school. This research focuses on the use of problem-based learning models to improve students' critical thinking skills in the study of Natural Sciences. This study uses a qualitative method through a naturalistic approach. Data collection uses observation, interviews, and documentation. Data analysis techniques use reduction, presentation, and conclusion drawing techniques. The results of the analysis found that the critical reasoning skills of SD Talenta Al-Fatih Sidoarjo students in science lessons can be known from the indicators of being able to formulate the main problem in a material, being able to answer questions in the main problem, being able to solve problems, and being able to make conclusions. The Problem-Based Learning Model (PBL) to improve students' critical reasoning skills at SD Talenta Al-Fatih Sidoarjo in Natural Sciences lessons is carried out with its activity program Supporting factors and hindering the application of the problem-based learning model to improve students' critical reasoning skills in learning Natural knowledge is a supporting factor including students, principals, and teachers and an ineffective learning process and limited time is an obstacle
Pengaruh Penggunaan Teknologi Informasi terhadap Peningkatan Kinerja Guru
Authors: Sopiyah Maria , Niswah Choirun , Ibrahim Ibrahim , Febriyanti Febriyanti , Zulkipli Zulkipli , Fatmawaty Fatmawaty
Published: November 2024

This study aims to analyze the impact of information technology usage on improving teacher performance at SMP Negeri 26 Palembang. The use of information technology is expected to enhance the efficiency and effectiveness of the teaching process, particularly in planning, implementation, evaluation, and improvement of educational programs. The research method employed is a quantitative approach, with data collection techniques including observations, questionnaires, and documentation. The subjects of this research are teachers at SMP Negeri 26 Palembang. Data analysis was conducted using simple linear regression tests and prerequisite tests to ensure the validity and reliability of the collected data. The research findings indicate that the use of information technology has a positive effect on enhancing teacher performance at SMP Negeri 26 Palembang. The regression coefficient of 0.606 suggests that every increase in the use of information technology can improve teacher performance. The t-test results show a value of t_calculated = 7.386 and t_table = 1.676 with a significance level of 0.000, indicating a significant effect. The determination test shows that information technology accounts for 52.2% of the improvement in teacher performance, while the remaining percentage is influenced by other factors. In conclusion, optimizing the use of information technology has a positive impact on teacher performance, and it is recommended that schools continue to support training and development of technology skills for teachers in the future
Insilico Insights into Resveratrol as a Potential Inhibitor of Mycobacterium Tuberculosis Enoyl-ACP Reductase (InhA) Protein
Authors: Ullah Obaid , Hanif Nimra , Ayesha . , Mufti Abdul Qayyoum , Amjad Fizza , Manzoor Maleeha , Jameel Esha , Fatima Sana
Published: November 2024

Mycobacterium tuberculosis, the causative agent of tuberculosis, is a global cause of death. Thus, the development of innovative treatment strategies is required. Objective: To develop in-silico drugs by phytochemicals to inhibit the Enoyl-ACP reductase (InhA) protein, which is essential for synthesizing mycobacterial cell walls. Methods: The 3D structure of InhA was taken from the Protein Data Bank. The Ramachandran plot validated the model with a score of 98.7% from the favoured Ramachandran plot. Computed Atlas of Surface Topography of Proteins was used to detect the active sites for ligand interaction. Resveratrol were selected based on existing studies and further listed for drug-likeness. Absorption, Distribution, Metabolism, Excretion, and Toxicity analysis showed the possibility of resveratrol as a drug candidate, with no violation of Lipinski rules and excellent absorption in the Gastrointestinal Tract. Results: The boiled egg model confirmed the ability of ligands to go through the blood-brain barrier. Toxicity predictions of resveratrol indicated low risks with several other systems of organs. Molecular docking with CB-Dock2 showed the strong binding of Resveratrol to InhA, with a Vina score equal to -8.8 kcal/mol. Further exploration of the docking complex by molecular docking simulation using the Integrated Management of the Public Distribution System was carried out, and the trajectory confirmed stable interaction and protein flexibility. Conclusions: It was concluded that resveratrol acts as a potent, non-toxic candidate for tuberculosis treatment and highlights its inhibition capacity of InhA. Results need future vitro and in vivo validation to develop this highly reliable therapeutic alternative for combating tuberculosis.
Կոմիտասի հարմոնիայի ձայնակարգային (մոդալ) բնութագիրը
Authors: Նավոյան Մհեր
Published: November 2024

Կոմիտասի հարմոնիայի հիմնական առանձնահատկությունների բացահայտման ճանապարհին անհրաժեշտ է գոնե հիմնական հատկանիշներով նկարագրել այդ հարմոնիան՝ որպես բազմաձայնության կազմակերպման ուղղահայաց սկզբունքի ամբողջական համակարգ ։
While seeking to uncover the main features of Komitas’s harmony, it is necessary to first describe his harmony at least by its general characteristics as a holistic system of organizing vertical polyphony.
Adimen artifizialaren erabilera EAEko helduen euskalduntzean
Authors: Txintxurreta Martin
Published: November 2024

Adimen artifiziala (AA) gero eta hedatuagoa dago gizarteko esparru guztietan, eta euskararen irakaskuntza ez da salbuespena. EAEko helduen euskalduntzean ari diren irakasleei AAri buruz bidalitako galdetegi baten emaitzak aztertuko dira lan honetan. Emaitza nagusiek erakusten dute AAren erabilera ez dagoela oso hedatua irakasleen eguneroko lanean, nahiz eta datozen urteetan euskararen irakaskuntzan eragina izango duenik ez den zalantzan jartzen. Testuinguru horretan, AAren hedapena kezka sortzen duen gaia da irakasleen artean.
AN INVESTIGATION OF CHRISTIAN MISSION APPROACHES IN THE CONTEXT OF RELIGIOUS PLURALISM IN INDIA IN THE 20THCENTURY
Authors: Kunjumon Satheesh K.P.
Published: November 2024

This paper investigates the historical development of Christian mission approaches in response to religious pluralism in India during the 20th century. It highlights the theological challenges posed by the coexistence of diverse religious beliefs and practices, where no single religion is viewed as having a monopoly on absolute truth. The analysis traces the failure of major missionary engagements—Syrian Orthodoxy in the 3rd century, Roman Catholicism in the 17th century, and Protestant missions in the 19th century—to engage India’s religious pluralism effectively. Through an exploration of the limitations of traditional Christian missions and an evaluation of ancient Hindu texts with their unhistorical mythical figures, this paper proposes a new mission strategy: the Pneumato-Logos-Christo-centric approach. This approach emphasizes a fresh theological framework for Christian missions to proclaim Christ within the pluralistic context, navigating the tension between respecting religious plurality and upholding the uniqueness of the Christian faith.
GENESIS 1 & ANCIENT NEAR EASTERN COSMOLOGIES
Authors: Cusack Paul T E
Published: November 2024

AncientNear Eastern peoples knew far more about cosmology than previously thought and they recorded it in theirarchitectural wonders, theiretymology, and their sacred works. In Geensi they recorded earth, airfare and water, all whichcan be analyzed using the latest physicswhich I called Astrotheology.There are some calculations here which only require high school mathematicsand basic calculus to understand. The ancientpeople knew this mathematics. Of course, the questionthat comes to mind is how did people as far back as the Minonians (4000 BCE) know this mathematics and corresponding cosmology?Were they visited by orbs? After readingthis paper, you'll know that ancientNear Eastern people's cosmology deserves respect.It is arrogant of modern man to think we knew more and discardbookssuch as Genesis that has a lot to teach us.
DAVID, INCULPABLE OF URIAH’S BLOOD: RE-READING 2 SAMUEL 11:8–13
Authors: C. Uroko Favour
Published: November 2024

The article argues that Uriah died due to Yahweh’s hatred for the Hittites. The Deuteronomist was very interested in telling us how David was very promiscuous and ungodly. However, when an exegesis was done on the study text, it was discovered that Yahweh, as well as Bathsheba, should be held responsible for the death of Uriah. Furthermore, it was proven that David did all he could to make Uriah escape being killed, but Providence being orchestrated by Yahweh made it clear that Uriah was not ready to give heed to the pleadings of David. Moreover, Yahweh hated the Hittites, the tribe of Uriah. Yahweh warned the Israelites right from the beginning that they should annihilate the Hittites from the land. Thus, David was a tool used to achieve this aim. This research piece adopted the narrative analysis of historical-critical methodology in arriving at these findings.
JESUS’ SACRIFICE (HEBREWS 9:15-28) AND THE HUMAN SACRIFICE IN YORUBA TRADITIONAL RELIGION
Authors: Okunoye Job Oluremi , Abodunrin Michael Olusegun
Published: November 2024

The sacrifice of Jesus as exemplified in the epistle to Hebrews 9:15-28 is studied in this paper in the context of human sacrifice in Yoruba Traditional Religion (YTR). The paper argues that sacrifice is at the centre in relation to salvation of mankind in both Hebrews and Yoruba traditions, though there is a great variety in the ways in which a sacrifice can be offered, as well as in the functions that it is expected to perform. Contextualizing the text under study enhances clarity in the understanding of the significance of the sacrifice of Jesus. Using historical-critical method and qualitative approach, the paper examines the sacrifice of Jesus as presented in Hebrews 9:15-28.Finding reveals that understanding the human sacrifice in YTR enhances contextualizing the sacrifice of Jesus among the Yoruba Christians. In conclusion, the sacrifice of Jesus as exemplified in Hebrews 9:15-28 is superior to the human sacrifice in Yoruba Traditional Religion.