bluuebunny/crossref_metadata_2025 huggingface dataset:
Transformation From Schottky to Ohmic Contact at High Temperature in Diamond Schottky Barrier Diode
Authors: Cui Shuning
Published: January 2025

Relations between Schottky contact and ohmic contact in diamond Schottky diode is attentively studied. Whether the metal-semiconductor contact forms metal carbide at high temperature on the interface of p-diamond is showed to determine the character of the contact. Formation of carbide and/or intermixing of metal and p-diamond at elevated temperature are believed to lower the Schottky barrier height and make Schottky contact “ohimicize” in a certain extent. This transformation can be advantageous for fabricating ideal ohmic contact, especially when boosted by annealing, but it can also be detrimental to maintaining high-quality Schottky contact at higher temperature. This suggests general criteria for identifying potential candidates for the two types of contact. Oxide and/or surface intermixture formed by metal and Oxygen-Terminals (OTs) on p-diamond, however, can resist the transformation trend at high temperature, thereby leading to better thermal stability. Nevertheless, the desorption of OTs at certain temperature may lead to re-reaction between metal and carbon, thus lowering the barrier again. Luckily, this temperature limit for certain metal is usually higher than the operating high-enough temperature and has less to do with the thermal stability and effectiveness of the functioning diode. Finally, careful selection of metal materials combined with adjustment of Schottky barrier height through high temperature can help fabricate desirable, robust and more economic diamond Schottky diode working at required higher temperature. Similar phenomenon may be observed for non-diamond Schottky diode as well and further researches can be conducted.
Analysis of the Basic Concepts and Applications of Machine Learning in the Medical Field
Authors: Wang Wenxing
Published: January 2025

The application of machine learning in cancer prediction has important academic value and practical significance, and can make important contributions to improving the accuracy and efficiency of early cancer detection. The article explores the key concepts of contemporary machine learning, the processes and steps involved in data processing, and its application in cancer prediction. It particularly focuses on the accuracy of different machine learning models in predicting specific cancers and their characteristics in medical applications. Due to its ability to handle large-scale and complex data, machine learning has become a crucial tool for improving the accuracy of early cancer detection. The article provides a detailed introduction to supervised learning models (such as SVM, decision trees, logistic regression) and unsupervised learning models (such as clustering analysis, PCA), discussing their concepts and conditions of application. The article also describes the performance of these models in breast cancer classification tasks, finding that SVM performed the best in terms of accuracy. Despite the significant potential of machine learning in medical prediction, challenges such as noise and missing values in clinical data, model interpretability, and personalized prediction remain. Future research should focus on improving data preprocessing methods, enhancing model interpretability, and promoting its application in clinical practice.
Sentiment Analysis of Long-Term Care Insurance in China Using NLP
Authors: Li Yixuan
Published: January 2025

Long-term care insurance (LTCI) is important for addressing the needs of aging populations and increasing long-term care demand. China initiated its LTCI pilot in 2016 and expanded it in 2019. This study employs Natural Language Processing (NLP) to analyze the sentiment impact of policyholders on LTCI decisions. By classifying sentiments in comments from various Chinese provinces, this study trained a Long Short-Term Memory (LSTM)-based deep learning model using annotated data and validated its accuracy with a pre-extracted test dataset. The application of the trained model to LTCI comments revealed sentiment distributions across different provinces, highlighting the developmental progress of LTCI since its pilot implementation. This research not only demonstrates the effective use of NLP in understanding sentiment trends but also underscores the significance of transitioning from qualitative to quantitative analysis. The study proposes integrating NLP with behavioral finance and insurance disciplines to enhance the utility and value of insurance products. This approach can guide product development, refine target marketing strategies, and inform policy decisions, contributing to the future expansion and success of LTCI in China.
Advancements in Understanding and Mitigating Near-Earth Asteroid Threats: Technology, Classification, and Global Cooperation
Authors: Li Jiayang
Published: January 2025

The study of near-Earth asteroids (NEAs) is vital for understanding the solar system and enhancing planetary defense. By researching these asteroids, scientists gain valuable insights into planetary evolution and impact history, aiding in the prediction and prevention of potential future threats. This article introduces how advancements in technology have enabled more precise classification and analysis of NEAs, such as categorizing them into groups like Amor, Apollo, and Aten. It points out that accurate orbital analysis enhances the ability to assess collision probabilities and develop preventive measures against catastrophic impacts. Additionally, the article examines global efforts in addressing NEA threats, which involve collaboration among international organizations, government agencies, and private enterprises. Organizations like the United Nations play crucial roles in coordinating observation missions and sharing information to improve awareness and response to asteroid risks. Tools like the Torino and Palermo Scales help translate complex data into information that policymakers and the public can understand. The article also highlights those future challenges, such as the potential impact of an asteroid like Apophis, underscore the necessity for continuous observation and technological development, including methods to alter an asteroid's path. Furthermore, the study emphasizes the importance of strengthening international cooperation through sample return missions and advanced monitoring networks to enhance global defense capabilities. In conclusion, the article underscores the critical role of global collaboration and scientific innovation in addressing NEA challenges, ensuring the long-term safety and prosperity of the planet and civilization.
Research for the Working Theorem and Application of Electrocardiogram
Authors: Takada Mai
Published: January 2025

Electrocardiograms (ECGs) are essential for diagnosing cardiac diseases, and recording the heart's electrical activity across cycles. Despite their critical role, there remains a gap in understanding the detailed principles and historical development of ECGs. This article introduces the historical development, basic principles, and application of ECG in the diagnosis of heart diseases. The working principle of ECG is studied, including the depolarization and repolarization process of myocardial cells, and how these physiological activities are manifested in ECG waveforms. The research involves analyzing the process of depolarization and repolarization in cardiomyocytes, illustrating how these principles manifest in ECG waveforms. The findings show that ECGs provide valuable insights into cardiac function, enabling the detection of arrhythmias and other conditions. This study concludes that ECGs are indispensable for cardiac diagnostics and highlights their continued significance in modern medicine. This study provides an in-depth exploration of ECG technology and its application in the diagnosis of heart disease, laying an important foundation for future research and practice.
Research and Development of Artificial Intelligence in Electronic Games
Authors: Yu Baihan
Published: January 2025

This study examines the multiple applications of Artificial Intelligence (AI) technologies in game design and development and their implications. First, this paper outlines the main types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, and introduces deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Subsequently, the specific applications of AI in game design are analyzed in detail in the article, encompassing procedural content generation, game balancing, behavioral control of non-player characters (NPCs) and social AI implementation. In game testing and quality assurance, AI technology significantly improves game development efficiency and user experience through automated error detection and user feedback analysis. In addition, AI also shows great potential in adaptive difficulty adjustment and personalized content recommendation, further enhancing players' game experience. In particular, this article also discusses the application of AlphaGo, DeepMind's StarCraft II AI, and OpenAI Five in Dota 2, demonstrating the superior performance of AI in complex gaming environments. Finally, the article discusses the future direction and challenges of AI in gaming, emphasizing the importance of technical security, data privacy, and ethical issues. Overall, AI technology shows great potential in the gaming field, which not only improves the intelligence level of games, but also brings new opportunities and challenges for game development and industry development.
Advances in Text-to-Image Generation: Integrating Transformer Models and Self-Attention Mechanisms
Authors: Yuan Chang
Published: January 2025

This study provides a comprehensive overview of advancements in Text-to-Image (TTI) generation through the application of Transformer models and Self-Attention Mechanisms. It begins with a review of the evolution of Generative Adversarial Networks (GANs) and highlights the benefits introduced by Self-Attention, such as improved contextual understanding and clearer image generation. The paper explores the theoretical foundations of Transformers and GANs, detailing how their integration can enhance TTI tasks. It also examines several leading models that employ these methodologies and presents quantitative performance evaluations comparing these models with other commonly used approaches. The findings indicate that Transformer-based modifications significantly improve TTI performance. The study concludes by assessing the current state of Self-Attention techniques and identifying potential research directions, such as exploring multi-head, hard, and soft attention mechanisms. These future research efforts are expected to further refine TTI capabilities and address existing challenges, providing deeper insights and more robust solutions for generating diverse and high-quality images from textual descriptions.
C-Simultaneous Localization and Mapping (C-SLAM) for Underwater Robotics Using Sonar Data
Authors: Gu Zhaowen
Published: January 2025

Since the current methods for precise underwater navigation are either insufficient in terms of accuracy or environmentally unfriendly, C-SLAM aims to address the critical issue of underwater localization for Uncrewed Underwater Vehicles (UUVs) undertaking seabed surveys. To improve the accuracy of navigation and overall operational efficiency, we’ll use Simultaneous Localization and Mapping (SLAM) techniques, which can achieve precise localization by looking at matching landmarks and Inertial Navigation System (INS) measurements from sonar data. While processing these sonar data, C-SLAM will adjust position estimation errors through loop corrections and pose-graph optimization methods. As a result, it can generate a detailed 2D/3D visual representation of the vehicle’s trajectory in the seafloor environment. Considering the importance of exploring the seafloor for scientific research, resource identification, and environmental monitoring, it is essential to track the accurate trajectory of UUVs, and our final deliverable, C-SLAM, is dedicated to tackle this issue.
Performance Modification and Application Of Poly (Lactic Acid)-Based Biodegradable Biomaterials
Authors: Zhang Xincheng
Published: January 2025

As new type of biodegradable material, polylactic acid (PLA) is synthesized by plant resources. This material features high mechanical properties, biocompatibility and biodegradability. It is mainly used in agriculture, medicine, biology, environmental protection, engineering and other fields. With the progress of science and technology, new requirements and applications have been put forward for the properties of PLA material, thus it must be modified to improve its processing and application properties. Only higher performance polylactic acid based materials can meet the growing needs of production and life. This review summarizes the research progress of PLA performance improvement from both physical modification and chemical modification, involving blending modification, plasticizing and toughening modification, composite modification as well as copolymerization modification, cross-linking modification and other modification methods, and introduces the research progress in the fields of agriculture, food, engineering. Furthermore, it is aimed at providing certain references for the broadening of PLA application market and discusses the future development trends of PLA.
A Study on the Prospects of Regional Artificial Intelligence Development Based on Carbon Emission and Development Indicators
Authors: Li Qiaochu , Zhuang Xinyu
Published: January 2025

Artificial Intelligence today demonstrates an astonishing productivity, and its development has become one of the directions for many countries. However, while developing, it is also necessary to consider the adverse impact of AI development on carbon emissions and to seek environments and regions suitable for AI development. To address this issue, this paper innovatively proposes the concept of "fertility" to describe the AI development potential of a region, and fully considers the adverse impact of AI development on carbon emissions. Based on the carbon emission and development data of various provinces in China in recent years, the "fertility" is modeled through PCA (Principal Component Analysis) and GB-DT model, and combined with LSTM for predicting the future AI development potential, thus deriving the future AI development potential of various provinces in China.
Different Light Sources and Physical Quantities to Be Monitored in Gas Sensing with Lasers
Authors: Cao Qiuyu
Published: January 2025

This article first presents some background and facts about the development of the field of gas sensing with lasers. Subsequently, the relevant content about using lasers for gas sensing was introduced. The introduction starts with the technical system and laser light sources. Laser light sources include several important and widely used lasers. The research directions and content suitable were identified for different lasers based on the existing research progress of different light sources. Starting from the Beer-Lambert law, the derivation process of gas concentration has been restored. Physical quantities detected in application have been classified and introduced, including absorption intensity, absorption line shape, Raman scattering intensity, photoacoustic signal and transmittance, and several physical quantities which are with high relevance to the previous text were derived. At the end of the article, the author summarized the article and provided an outlook on future development of gas sensing with lasers.
Exoplanet Detection of the Kepler Space Telescope through the Transit Method
Authors: Yang Helin
Published: January 2025

With the exploitation of resources and environmental pollution, the Earth has suffered countless damages. As a result, there are more and more environmental problems such as the greenhouse effect and desertification. People only have one home, one Earth, so if the Earth becomes extinct, people will not exist anymore. Therefore, people need to repair and protect the planet. Humans have tried many ways to protect the Earth, but some damage is irreversible. Thus, to find another home and save mankind long after, scientists began to detect some potentially habitable exoplanets. In 2009, the National Aeronautics and Space Administration (NASA) launched the Kepler Space Telescope (KST) to detect exoplanets through the transit method. Surprisingly, it did find several exoplanets that are similar to Earth in some aspects. Unfortunately, with current technology, people can’t get there. This paper is going to introduce the KST, the transit method, and several potentially habitable exoplanets.
Whispering-Gallery Mode Lasers: A New Frontier in Micro resonators
Authors: Xu Guowen
Published: January 2025

Whispering Gallery mode (WGM) optical microcavities represent a significant focus in current laser research. Initially, this paper elucidates the concept of WGM and its acoustic significance, before delving into its development in the optical field and its crucial role in optical devices. Subsequently, the author explores the theoretical foundations of WGM, elucidating the photon confinement effect due to total internal reflection. These optical microcavities are noted for their high symmetry and surface smoothness, which confer an exceptionally high Q-factor. The enhanced light-matter interactions in these microcavities lead to substantial improvements in nonlinear optical effects, laser gain, and photonic crystal effects within the amplified optical fields. The author discusses micro resonators, the fabrication methods of microcavities, and the unique nonlinear optical effects arising from high Q-factors and small mode volumes, including Stimulated Brillouin Scattering, Stimulated Raman Scattering, the Kerr effect, and four-wave mixing. These effects hold broad application prospects in fields such as precision spectroscopy, optical communication, and frequency comb generation. The advantages of WGM optical microcavities in laser fabrication and the study of nonlinear optical effects are summarized, with an outlook on their potential applications in future optical fields, such as high-sensitivity sensors and low-threshold lasers.
Prediction of Electric Load Neural Network Prediction Model for Big Data
Authors: Li Jialin , Li Binbin , Luo Zheng , Chen Weikang
Published: January 2025

Rock bursts are one of the most common and hazardous disasters encountered in coal mining operations, particularly in China. The occurrence of rock bursts poses significant risks to the safety of miners and the structural integrity of mining operations. Consequently, coal mines are required to conduct extensive and frequent rock burst risk assessments, which contribute to the increasing workload and operational pressures on mine personnel. Addressing this challenge is critical for enhancing the safety and efficiency of mining activities. In response to this issue, this paper proposes a novel approach to predicting rock bursts by preprocessing electromagnetic radiation (EMR) and acoustic emission (AE) signals to remove noise and interfering signals. The preprocessing step is crucial as it helps in isolating the relevant features that are indicative of potential rock bursts. By extracting both the interference signal features and the signal precursor features, a comprehensive training set for the predictive model is established. To further enhance the accuracy of predictions, the study employs advanced machine learning techniques, specifically random forests and decision trees, to train and analyze the signal features. These models are chosen for their robustness and ability to handle complex data patterns, making them well-suited for the prediction task. The experimental results are promising, demonstrating that the model achieves a high prediction accuracy of 99.39%, with an Area Under the Curve (AUC) value of 0.99, indicating excellent performance. This high level of accuracy suggests that the model is highly effective in predicting rock bursts, thereby offering a valuable tool for improving mine safety and reducing the workload associated with manual risk assessments.
Prediction of the Next Solar Cycle and Solar Maximum Based on Machine Learning
Authors: Xiang Xuanyi , Zhang Xinyue , Huang Qingyi , Liu Jingyi
Published: January 2025

The intensity of solar activity varies with the solar cycle of about 11 years, reaching a peak during the solar maximum. Solar activity can affect space weather in ionospheric states and conditions related to shortwave radio propagation or satellite communications, which brings challenges to space exploration, communications, and weather forecasting. Therefore, accurate prediction of the start time and duration of the solar maximum becomes pivotal. In order to solve this problem, this research first applies the K Nearest Neighbors classification algorithm to predict that the beginning of the next solar cycle will be around June 2030 until about July 2040. Then, by establishing the Extreme Gradient Boosting model, this study forecasts that the solar maximum in the next solar cycle will start in August 2034 and last for 8 months. With this accurate prediction of the next solar maximum, aerospace, communications, meteorology, and other industries can reduce the interference of solar activities.
Research on VR Applications Based on Computer Technology
Authors: Lin Zizhi
Published: January 2025

With the continuous progress of the era of science and technology and the increasing abundance of technology products, the influence of VR technology has attracted increasing attention, and people are constantly exploring the uses and possibilities of VR. The evolution history of virtual reality technology can be divided into four stages, and the simulation of sound shape dynamics is the first stage containing the idea of virtual reality. Virtual reality germination is the second stage. The emergence of virtual reality concept and theory is the third stage. The further improvement and application of virtual reality theory is the fourth stage. This paper uses the role of computer technology in VR, and discusses the application of computer in VR and the work that VR can be realized or carried out, such as VR games or use Virtual reality in medical health,even in comprehensive application in urban governance.As well as the current development problems and the prospects for the future.Like Technical restrictions, high hardware costs and Virtual reality content is relatively less represented in various fields.
All-Polymer Solar Cells Based on Wide-Band Gap Conjugated Polymers
Authors: Meng Jiaoyang
Published: January 2025

Recently, there has been an increased utilization of wide bandgap polymer electron donors in non-fullerene organic polymer solar cells. Consequently, scientists and engineers worldwide have shown substantial interest and research focus on all-polymer solar cells utilizing wide bandgap conjugated polymers. These advanced materials provide distinct benefits in terms of adjustable electronic properties, superior effectiveness, and possibility for cost-effective manufacturing, making them a central focus for the advancement of future solar energy technologies. This article will begin by giving a detailed summary of the progression of all-polymer solar cell development in recent decades. We will track the major achievements and advancements that have influenced the field of organic photovoltaics, from its earliest discovery to the most recent progress in polymer chemistry and device engineering. This historical viewpoint will focus on the continuous initiatives and advancements that have propelled the enhancements in performance and expanded the range of applications for polymer solar cells. Afterwards, the article will outline the fundamental layout and functioning principles of polymer solar cells. We will investigate the basic elements such as the active layer, charge transport layers, and electrodes, and demonstrate how these components work together to convert sunlight into electrical energy. In conclusion, the paper will explore the studies on donor materials of conjugated polymers with wide bandgaps. These materials are now seen as crucial elements needed to improve power conversion efficiencies and enhance stability in polymer solar cells.
Leveraging Big Data Technologies for Smart Grid Development: Frameworks and Implementation
Authors: Xue Qichen
Published: January 2025

As the urgency to ensure energy sustainability and environmental preservation grows in response to climate change and escalating energy demands, transitioning from conventional grid systems to technologically advanced systems become imperative. The proliferation and complexity of data generated during energy production, conversion, measurement, and calculation pose significant challenges to traditional power system management methods. Consequently, the development of a new advanced grid system, which is named smart grid, is proposed. This system incorporates big data technologies to effectively manage the vast amounts of data that were previously cumbersome and difficult to handle. This study introduces the background and fundamental concepts of smart grids and big data, providing a comprehensive overview of mainstream data processing frameworks such as Apache Spark, Apache Storm, and Hadoop. The characteristics and capabilities of these frameworks are demonstrated and compared. The paper summarizes the contributions of these big data technologies to the development of smart grids, identifies the difficulties encountered during implementation, and proposes Apache Flink as a solution to meet the evolving requirements.
Research Status of Machine Learning in Stroke Treatment
Authors: Chen Jiafeng
Published: January 2025

Stroke, characterized by high morbidity, mortality and disability, seriously jeopardizes human health. Timely and accurate prediction and diagnosis of the disease is of great significance. Machine learning can efficiently analyze large-scale datasets and shows great potential for application at various stages in the treatment of stroke disease. In this paper, we start from the common data processing, feature selection, model training and evaluation of the conventional application process of machine learning. This paper analyzes the application of Random Forest, Support Vector Machine, and Deep Learning, which are commonly used machine learning models, in the diagnosis, treatment, and prognosis of stroke. This paper summarizes their advantages over traditional statistical methods and the current shortcomings and points out the appropriate methods for improvement. In addition, this paper also looks forward to the future application of machine learning in the field of stroke prediction. It explores the possible path of machine learning technology in the future development of the fight against stroke. This study aims to promote the progress of machine learning in the field of stroke research by integrating and comparing related research approaches, and so on.
THE IMPORTANCE OF PRAYER TO SAVE THE MILLENNIUM GENERATION FROM PSYCHOLOGICAL DISORDERS
Authors: Mustafa Zahri  , Retno Prihadi  , Lutfiyani  , Amul Husni Fadlan
Published: January 2025

This essay highlights the importance of prayer in protecting young people from mental disorders. This paper argues that through prayer, one can complain, pray, and confide in a substance that is believed to The All-Hearing and All-Helping. Therefore, this can help someone feel listened and cared for while also making them calmer and think more clearly, with the ultimate goal of achieving peace. Hence, by knowing the importance of prayer, we may protect the younger generations or the future leaders of the Indonesian nation from harm and devastation, transforming them into noble, peaceful, happy, and enthusiastic individuals.
The Challenges Inherent in Poetic Translation: Lexical, Semiotic, Syntactic, and Prosodic Challenges
Authors: Balla Dr. Bonaventure
Published: January 2025

The word “poetry” stems from the Greek verb “ποιέω” “poiein” which means “to create”, not in the sense of “making ex nihilo”, but by using the powerful and priceless resources of one’s imagination. This cursory etymological background undergirds the outstanding ability for the poet and poetry to conceive a unique art made up of special techniques, stylistic devices, and coin special words and constructions to express the ineffable by thinking outside the box. It follows that poetry can be conceived as a special genre that utilizes an eminently creative language to express a particular vision of the world to sublimate nature. That is precisely why scholars consider it a metalanguage, that is, a language formalized from and beyond the ordinary language (“meta” means “beyond”, “above”, “on the other side”) used as a heuristic prop/stepping-stone for such formalization. Then, linguists subsequently formalize it as well. Therefore, poetry as a meta-language, is designed in such a way that it factually superimposes two types of language: The common language and, at a higher level, a formal language. All these considerations attest to the fact that poetry is such a highly sophisticated art that it becomes very challenging to translate. As a matter of fact, one can translate a prose text from one language to another. However, it can be very difficult to do likewise with a poetic text. Therefore, the goal of this paper is to reflect on the appropriate strategy or approach that can be utilized to translate poetry.
Exact Solutions in Birefringent Fibers for Stochastic N-LSE with Kerr-Law Nonlinearity and Multiplicative White Noise Effects
Authors:   Department of Mathematics, Faculty of Science, Elmergib University, Khoms, Libya, Alurrfi Khaled. A. , Almasroub Ragab M. ,   Department of Mathematics, Faculty of Science, Azzyatuna University, Tarhuna, Libya., Aleyan Omar A. ,   Department of Mathematics, Faculty of Science, Alasmarya Islamic University, Zliten, Libya, Saeid Marwa A. ,   Department of Mathematics, Faculty of Science, Alasmarya Islamic University, Zliten, Libya
Published: January 2025

This article explores the exact wave solutions of the coupled one-dimensional stochastic non-linear Schrödinger equation with Kerr-law nonlinearity, incorporating multiplicative white noise in the Itô interpretation. The generalized projective Riccati equations (GPREs) scheme is employed to obtain the obtained solutions. The study presents a range of soliton solutions, including dark, bright, singular solitons, in addition to solutions for rational and periodic functions. Finally, the MATLAB software was used to explain the numerical simulation of some soliton solutions.
Keywords: Generalized projective Riccati equations scheme; Non-linear stochastic Schrödinger equation; Exact solutions; Soliton solutions; White noise.
A Critical Study on the Effects of RERA Regulations on India’s Real Estate Industry
Authors: Raju Sabar  , Snigdhadip Ghosh
Published: January 2025

The Real Estate (Regulation and Development) Act (RERA), introduced in 2016, represents a pivotal regulatory framework aimed at transforming the Indian real estate sector. Its objectives include fostering transparency, curbing malpractices, and protecting consumer interests, thereby addressing the systemic inefficiencies that previously characterized the industry. This research paper provides a comprehensive analysis of the impact of RERA on various stakeholders, including developers, homebuyers, and financial institutions. By examining real-world examples and leveraging both qualitative and quantitative research methods, this study evaluates the extent to which RERA has fulfilled its goals. Special emphasis is placed on the challenges of its implementation, state-wise variations in compliance, and its broader implications for economic growth and urban planning. The paper also highlights the relevance of RERA in integrating technology-driven project management tools and aligning real estate practices with global standards of accountability. In conclusion, this study identifies actionable recommendations to enhance the efficacy of RERA and ensure its sustainable contribution to the Indian real estate sector.
Cybersecurity Awareness for Student SMKN 1 Denpasar
Authors: Hanriyawan Adnan Mooduto , Firosha Ardian , Yullherniwati  , Rasyidah  , Afyenni Rita , Satria Deni
Published: January 2025

The digital era has changed the way we live and work. The use of the internet and digital devices is increasingly widespread, including among school-age children. However, along with easy access, various cyber threats have also emerged such as hacking, online fraud, and the spread of negative content. It is from this background that the Community Service for the Implementation of Independent Community Science and Technology (PIMM) International was carried out between Indonesian universities, namely the Padang State Polytechnic (PNP) and the Bali State Polytechnic (PNB) with a Malaysian university, namely the National Defense University of Malaysia (UPNM). This activity was carried out on the island of Bali, precisely at SMKN1 Denpasar. SMKN 1 Denpasar, as a vocational high school, has students who will enter an increasingly digital workforce. Cybersecurity skills are very important to protect personal data, company assets, and prevent cyber attacks. Therefore, it is important to equip the younger generation with cybersecurity knowledge and skills from an early age. This community service program can make a significant contribution to increasing the awareness and cybersecurity skills of SMKN 1 Denpasar students. Thus, students can be better prepared to face challenges in the digital era and protect themselves from cyber threats
Dokumen Manajemen Sistem (DMS) dalam Penatausahaan Administrasi Nagari di Kenagarian Lubuk Malako Kabupaten Solok Selatan
Authors: Ilhami Arsyah Ulya , Yupita Lasmi , Febriani Widya , Hasanah Miftahul , Arizona Febriani Tika
Published: January 2025

Nagari Lubuk Malako Kabupaten Solok Selatan menghadapi tantangan dalam penataan administrasi nagari agar lebih efisien, terorganisir, dan responsif terhadap kebutuhan masyarakat. Salah satu tantangan utama adalah kebutuhan akan penyimpanan data, pengelolaan dokumen administrasi dan ilmu pengetahuan tentang teknologi informasi. Perkembangan teknologi informasi menawarkan solusi yang sangat potensial dalam penggunaan Dokumen Manajemen Sistem (DMS) di tingkat nagari masih terbatas. Mayoritas perangkat nagari masih mengandalkan cara tradisional dalam pengelolaan dokumen administrasi, dan infrastruktur teknologi informasi yang terbatas juga menjadi permasalahan khusus. Jurnal ini menyajikan penggunaan Google Workspace, implementasi DMS dengan Google Workspace, manajemen jadwal dan komunikasi dengan Google Calendar dan Google Chat, pengumpulan data dan umpan balik menggunakan Google Forms, serta manajemen email dengan Gmail. Dengan demikian, jurnal ini  memberikan  wawasan  tentang  pemanfaatan fitur-fitur yang ada dalam Google untuk membantu dalam penataan administrasi nagari.
Cloud-based adolescent physical health research incorporating cell molecular biomechanics insights into predictive modeling and biometric algorithms
Authors: Li Huanpin
Published: January 2025

The physical health of adolescents is critical to their future development and quality of life. However, there is still much room for improvement in monitoring and intervention of adolescent physical health in colleges and universities. Based on the National Standard for Student Physical Fitness and Health, this study proposes a physical health management framework for adolescents that combines a cloud platform and real-time data analysis. Through an innovative health monitoring and early warning system and feedback mechanism, the effectiveness of health intervention was significantly improved. Body mass index is related to the balance between energy intake and expenditure at the cellular level. Excessive calorie intake can lead to an increase in adipose cells, which secrete molecules that can affect overall metabolism and biomechanical stress on tissues. Lung capacity is linked to the elasticity and strength of the alveolar cells and the connective tissue in the lungs. he proper functioning of these cells, regulated by intracellular signaling pathways and molecular interactions, determines the efficiency of gas exchange. The 50-meter run, seated forward bending, standing long jump, pull-ups/sit-ups, and 800-meter/1000-meter run all involve muscle contractions. Muscle cells contain actin and myosin filaments, and the sliding of these filaments, regulated by calcium ions and other molecular signals, generates the force required for movement. The graded early warning parameters established by the system can thus be seen as indicators of potential disruptions in these cell molecular biomechanical processes. The innovative health monitoring and early warning system and feedback mechanism not only help students recognize the weaknesses in their physical functions from a macroscopic level but also potentially identify areas where cell molecular biomechanical imbalances may exist. This enables physical education teachers to formulate personalized training plans that can target and correct these imbalances, as experimentally validated by the system's ability to accurately identify health risks and enhance the overall health of students.
Humanized physical education teaching plan design: Utilizing biosensors to evaluate students’ movement status
Authors: Li Tao , Chen Wei
Published: January 2025

Biosensors allow the monitoring of student movement in real-time and enhance the effectiveness of personal workouts through data analysis to enhance performance. Even though there is significant potential, biosensor precision, concern about data privacy, cost, and the need for expert knowledge limit the implementation of such technologies in physical therapy. This research aims to analyze educational systems that make use of biosensors to monitor the movement of students and customize their course of ideas. In addition, it also provides more efficient tools for exercise interventions based on factual information. A Resilient Sailfish Algorithm-tuned Enriched Long Short-Term Memory (RSA-ELSTM) method is proposed to increase prediction accuracy, address data challenges, and improve motion analysis beyond current limitations. Datasets used include motion capture and sensor readings, which capture different student movement patterns. Preprocessing involves image resizing, and normalization, while VGG16-based feature extraction is used to improve model performance and accuracy. The RSA-ELSTM approach uses biosensor data and deep learning (DL) to optimize motion analysis, increasing accuracy, flexibility, and real-time analysis. The RSA-ELSTM the model obtained a 99.1% F1-score, 99.3% accuracy, 98.7% recall, and 99.2% precision. Results revealed improved accuracy in motion prediction and real-time analysis, improving personalized workouts. In conclusion, the RSA-ELSTM approach significantly enhances biosensor-based exercises, provides accurate student movement analysis, and improves individual performance management, thus making educational outcomes good.
HELMET AND NUMBER PLATE DETECTION USING YOLOV5 AND YOLOV8
Authors: Gorak Tanusha , Kumar Reddy Dr.C.Kishor
Published: January 2025

The purpose of this project is to create a reliable and effective system for detecting vehicle number plates using two well-known versions of the object detection algorithm YOLO (You Only Look Once), specifically YOLOv5 andYOLOv8. This system aims to automatically identify and extract number plates from images of vehicles, which is essential for applications such as automated tolling, traffic monitoring, and law enforcement. The project involves training both YOLOv5 and YOLOv8 models on a specially created dataset of labelled vehicle images, and then evaluating and comparing the models based on detection accuracy, inference speed, and their robustness in various environmental scenarios. Additionally, the system's performance is improved by incorporating Optical Character Recognition (OCR) to retrieve text from the identified number plates. The results of this comparison indicate that YOLOv8, thanks to its enhanced architecture, offers greater accuracy and faster inference rates compared to YOLOv5, making it a better choice for real-time applications. This work emphasizes the efficacy of deep learning methods in addressing number plate recognition challenges, providing a scalable and practical solution for intelligent transportation systems.
Advancing Patient Care through the Role of Medical Professionals in Optimizing Clinical Environments: A Systematic Review
Authors: Alshihri Mohammad Hadi dafer , Alenizi Amira Hayaf Suleima , Alanazi Khulud Olaywi Basher , Al Juaid Hatem Awadh Awaif , Abuhaimed Sara Nasser Abdullatif , Alrashidi May Awad Saeed , Alshahrani Seham sanad Shaya , Aldarami Alhanouf Ali , Samman Amani Salman Abbass , Alyami Mohammed Saleh Mohammed
Published: January 2025

The clinical environment plays a critical role in determining the quality of healthcare delivery and patient outcomes. Medical professionals, as front-line contributors, significantly influence the optimization of these environments through their expertise, interventions, and collaboration. This systematic review examines the role of medical professionals in enhancing clinical environments to improve patient care. Following PRISMA guidelines, studies from 2016 onward were analyzed to explore their contributions to workflow efficiency, resource management, and patient safety. Findings reveal that medical professionals are pivotal in driving improvements, leading to enhanced patient satisfaction, reduced medical errors, and streamlined clinical processes. The review underscores the need for targeted strategies and policy enhancements to support their role in achieving optimal clinical environments.
The Role of Healthcare Professionals in Enhancing Clinical Environments for Improved Patient Outcomes: A Critical Analysis
Authors: ALmaslum Ameer Mohammad Said , Al Yami Salim Saleh Hasan , Alhmmami Abdllah salem abdllah , Aqeel Reem Dassan Alanzi , Alyami Hadi Masoud , Al Zulaiq Hussein Mohammed Hussein , Al Hadaisan Maihat Hamad , Al Alhareth Ali Saleh Ali , Alhamami Musaad Hassan Nasser , Alhamami Hammam Hassan Musaad
Published: January 2025

Healthcare professionals play a pivotal role in shaping clinical environments that significantly impact patient outcomes. This critical analysis explores how healthcare workers, including doctors, nurses, and support staff, contribute to optimizing clinical settings through effective communication, teamwork, and environment management. The study examines challenges such as workplace stress, resource constraints, and training deficiencies while highlighting innovations and best practices like the integration of technology and Lean methodologies. By linking the contributions of healthcare professionals to improved patient outcomes, this article underscores the need for continued investment in workforce development and systemic enhancements. Recommendations for policy changes and further research directions are provided to support sustainable improvements in clinical environments.
Analysis About Inflation Persistence in West Sumatra Province: Pre-Post the COVID-19 Pandemic
Authors: Safela Remon Astra , Satrianto Alpon
Published: January 2025

This research is based on significant inflation fluctuations due to global economic disruptions, changes in consumption patterns, and the impact of a pandemic that triggers shock inflation away from its average value. The purpose of the study is to analyze between inflation persistence before and after the COVID-19 pandemic in West Sumatra Province. The variables used in this study include general inflation and commodity group inflation. This study uses a univariate autoregressive (AR) approach with monthly inflation time series data for 2017-2019 (pre-pandemic) and 2021-2023 (post-pandemic). Data obtained from the Central Bureau of Statistics of West Sumatra.   The results showed that the persistence rate of general inflation before and after the pandemic was in a controlled category with an average length of time back to its natural level of less than one month. All commodity group inflation variables such as processed food, housing, health, and education are in the low inflation persistence category with the length of time to return to its natural level of less than 3 months both before and after the pandemic. The results are different for the inflation variables of the transportation, communication, and financial services commodity groups which have a low inflation persistence rate before the pandemic, but high after the COVID-19 pandemic with a length of time to return to its natural level of 8.09 months. This research is expected to help policy makers in formulating strategic steps to maintain inflation and regional economic stability.
Public State Services for Merchants in the Islamic East in the Book Masalik al-Ibsar fi Mamlik al-Amsar by Ibn Fadl Allah al-Amri, who Died 749 AH / 1349 AD
Authors: Salman Amer Adnan , Mohammed Saddam Jassim
Published: January 2025

Commercial activity in the countries of the Islamic East required care for merchants and trade caravans and hospitality through the efforts made by the Arab Islamic state and the rulers of the provinces in the East to eliminate dangers, achieve political and administrative stability, establish security, conclude agreements with countries and kingdoms neighboring the East, and establish guard posts on the roads. In addition to that, there was the nature of the people of the East who were accustomed to welcoming and caring for guests and trade caravans passing through the cities and regions of the Islamic East. Many ribats and inns emerged in the cities, and these ribats became rest stops for merchants and travelers to stock up on what they needed for themselves and their animals for long journeys, in addition to what they found of security, reassurance, and peace during the buying, selling, and banking operations.
The Development of Piano Teaching for Piano Pedagogy Course
Authors: Zhang Xiaolin , Daoruang Krisada
Published: January 2025

As an ancient and beautiful form of music, piano education plays an important role in college music education. Piano teaching is an important part of college music education and an essential direction of music education research. However, there are still many problems in piano teaching. This study takes piano teaching methods and music education major college students as research objects in this context. This research aims to study and analyze 3 piano pedagogy methods, develop a piano teaching model manual for a piano pedagogy course, and use and evaluate my teaching model manual for a piano pedagogy course. Multiple research tools were used in this study, such as the literature research method, questionnaire, and experimental study. The three piano teaching methods are studied through the collection of literature and data. Meanwhile, the study explored and innovated piano teaching. A questionnaire survey was conducted among 80 sophomore students. These research methods aim to evaluate the impact of innovative pedagogy on improving piano-related quality for students majoring in higher music education. The findings reveal that each of the three teaching methods has its advantages and disadvantages; a suitable piano teaching method should have both the cultivation of skills and the skillful application of the piano, and the new piano teaching method has an excellent application effect in college music education, which can help students improve their piano playing skills and musical expression ability.