bluuebunny/crossref_metadata_2025 huggingface dataset:
Data-driven School Management: An Analysis of Pathways to Enhancing Student Academic Performance and Mental Health
Authors: Pan Jiachen
Published: January 2025

The rapid growth of data analytics in recent years has opened up new opportunities for organizations of all kinds to make the most of large amounts of data to drive decision making and optimize operations. This paper focuses on the application of data analysis in improving student management in educational institutions, and analyzes a variety of lifestyle and behavioral variables such as students' study time, extracurricular activities, sleep duration, and social behavior. By analyzing the impact of these factors on academic performance and stress levels, this study highlights the importance of data-driven approaches in shaping education policy. The study uses statistical tools and regression analysis to identify key patterns and correlations and provide actionable recommendations for schools to enhance student academic performance and effectively manage mental health. This study aims to show how data analytics can be applied in education to help schools create more personalized, efficient and supportive learning environments. The findings highlight the importance of balancing academic demands with student well-being and suggest interventions that schools can take to optimize academic outcomes and overall student health.
The Impact of Social Media on Corporate Social Responsibility: Motivations, Practices, and Outcomes
Authors: Yan Keyu
Published: January 2025

In the information age, the rapid development of social media has influenced the public's views and cognition, and has directly and indirectly increased the expectations for companies to fulfill their social responsibilities. Based on a review of previous research literature, this paper argues that the impact of social media on corporate social responsibility (CSR) primarily includes motivational, operational, and effect-based impacts. The motivational impact of social media on CSR can be attributed to four factors: meeting consumer expectations and shaping brand image, reducing the pressure from social media public opinion, gaining interactive incentives from social media, and providing a transparent communication platform to capture stakeholders' attention. Social media also affects the specific practices of companies in implementing CSR activities. It offers a more effective platform for disseminating CSR information, helps establish appropriate communication methods, and enhances CSR image management on social media. Fulfilling CSR through social media can yield positive outcomes, shape corporate reputation and brand image, actively influence consumer attitudes and behaviors, and encourage companies to fulfill their social responsibilities in alignment with publicized information. Future research could focus on the differing impacts of social media on companies of various types and sizes, providing more detailed guidance and recommendations for companies.
Hardware Implementation of Convolutional Neural Networks
Authors: Guo Yulin
Published: January 2025

Convolutional Neural Networks (CNNs) have broad application prospects in computer vision, image processing, and other fields. However, their characteristics, such as high computational speed and large data volume, pose significant challenges for hardware implementation. This project aims to improve computational efficiency and reduce energy consumption by summarizing hardware-based convolutional neural network algorithms to meet real-time requirements. Additionally, it will summarize research methods for accelerating convolutional computation, pooling, and fully connected layers using different hardware platforms such as ASIC, FPGA, and GPU. This paper focuses on optimizing data flow, parallel processing, and memory architecture to reduce computation latency and energy consumption. To ensure network accuracy, methods such as quantization and pruning are employed to reduce model size and computational complexity. Literature indicates that custom hardware designs for convolutional neural networks significantly enhance performance and energy efficiency compared to traditional software implementations. This review aims to provide an efficient hardware acceleration method for practical deep learning algorithms and promote development in fields such as smart terminals and edge computing.
An Adaptive Event-Triggered Predictive Control Method
Authors: Hu Jiawei
Published: January 2025

This paper studies an adaptive event-triggered (AET) control method for sampled-data systems to enhance communication efficiency and conserve resources. Initially, a dynamic event-triggering mechanism is introduced to minimize redundant data transmissions while maintaining system performance. Then, a state feedback control law is designed by optimizing the event-triggered threshold condition, with the system's stability verified using a Lyapunov function. The gain parameters are computed through a cone complementarity linearization algorithm, ensuring computational efficiency and robustness. Additionally, the proposed approach addresses the trade-off between communication resource utilization and control performance by dynamically adjusting the triggering conditions based on system states. This ensures a reduction in redundant transmissions without compromising system reliability. Finally, this method provides a systematic and effective framework for improving communication efficiency, with potential applications in industrial automation and resource-constrained networked control systems.
Live Music Industry and Common Music Trend Prediction in Machine Learning Algorithms
Authors: He Jiaqi
Published: January 2025

As live music became popular, many music platforms emerged, and the music industry experienced a dramatic development. To further develop the music industry, music trend prediction is necessary for music companies and composers. The paper aims to provide a detailed description of the current situation in the music industry and showcase the procedures and some common methods used by other researchers to predict music trends. Those methods are all based on training models, including big data algorithms making mathematical models, feature extraction from the songs, utilizing Self-Embedding Attention Layer (SEAL) framework and Graph Neural Networks (GNNs), and using the influence data set and Integrative Collective Music (ICM). These methods could predict the music trends on certain platforms or data sets. Meanwhile, they also have some drawbacks, such as a lack of data access, especially data with high quality, and the static characteristic since the trend of pop music always changes. Concerning the models created by those authors, some possible future perspectives, like establishing a public data set and further support for the music industry, are proposed, attempting to summarize the field of music trend prediction in current society.
A Comparative Analysis of YOLOv5nu, YOLOv8n, and YOLOv11n Models for Blood Cell Detection and Classification
Authors: Li Tingting
Published: January 2025

Object detection, which detects and classifies objects, is widely used in different fields. One such field is in the medical field, and more specifically it can be used to detect molecules in microscopy images to improve blood test efficiency. Nowadays, plentiful hospital labs are still manually counting blood cells which is time-consuming, and it is likely to make manual errors. A solution to this problem is to enable accurate object detection specifically designed for identifying molecules. To achieve this goal, the comparison analysis of different YOLO models, one of the most popular models in object detection, on a blood cell dataset is valuable as it evaluates the models accuracy, which provides insight into their strengths and weaknesses. The paper considers three types of YOLO models, YOLOv5nu, YOLOv8n, and YOLO11n models, on detecting and classifying red blood cells (RBC), white blood cells (WBCs), and Platelets. The experiment results show that all three models have high precision and recall rates, which means that they can identify most of the molecules accurately. This indicates a promising future of integrating object detection on blood cell count to speed up medical analysis.
Applications and Future Developments of Microelectronics and Integrated Circuit Technologies in Smart Home Systems
Authors: Hao Yingjie
Published: January 2025

With the rapid development of smart home systems, microelectronics and integrated circuit (IC) technologies have not only driven the miniaturization and multifunctionality of home appliances but also significantly enhanced their intelligence, enabling them to better meet personalized user needs and improve overall quality of life. This paper explores in depth the application of these technologies in improving smart home systems, especially how to enhance user experience and address challenges in smart home development, such as energy efficiency, comfort, security, privacy protection, and system stability. By reviewing the current status and future trends of the smart home market, it analyzes the role of these technologies in real-time data analysis, rapid response, and advanced function implementation, combined with the evolution of microelectronics and IC technologies from single sensors to integrated sensing systems, from basic controllers to complex control systems. Moreover, this paper discusses the technical challenges and innovations faced in power management, security and privacy, and device interoperability. The results indicate that, notwithstanding certain technical challenges, microelectronics and IC technologies are crucial for enhancing the automation, convenience, and security of smart home systems, providing a robust foundation for the future advancement of smart homes.
Application of Optical Sensing Technology in Oximeter
Authors: Ma Jiuyue
Published: January 2025

Optical sensing technology now plays an important role in the field of health monitoring. With the improvement of the level of science and technology and the rapid development of the medical service industry, people pay more and more attention to health, resulting in the various functions of the oximeter are becoming more and more perfect, and the oximeter has been more popular with people since then. This paper mainly studies the application of optical sensing technology in oximeters, especially the importance of health monitoring. The purpose of this article is to review the historical development of optical sensors, discuss the technological innovation of oximeters, compare different technical performance indicators, and some practical application scenarios of oximeters. This paper will conduct research and analysis through a review method, and find that optical sensing technology has significantly improved the accuracy of oximeter measurement, and optical sensing technology will continue to develop and benefit mankind in the future.
Harnessing More Power of Wind with a Novel Wind Turbine Design
Authors: Tang Edward
Published: January 2025

Renewable energy sources, especially wind energy, offer one of the most viable and adaptable solutions. In our study, we have enhanced the conventional wind turbine design to extend the power generation phase, thereby improving the turbine's overall efficiency. Through experimental comparisons between our innovative design and the traditional one, we demonstrate that our modifications allow for overall higher power output under identical wind conditions, which suggests future directions in wind power generation equipment
Research on the Effect of Topographic Factors on Extreme Rainfall in South China
Authors: Shu Xiaohan
Published: January 2025

The extreme rainstorms, along with the flood disasters, have been plaguing south China every summer. The reason for this phenomenon is not only because of its sub-topical monsoon climate, but the specific terrain character in south China also plays a role in the extreme weather. This essay delves into the extreme rainfall events in South China, highlighting the significant role of topographic factors in influencing precipitation patterns. The focus is on understanding how geographical characteristics impact rainfall intensity and subsequently lead to various natural hazards. The analysis employs a combination of historical precipitation data and topographic assessments to explore the forming mechanisms of extreme rainfall, specifically frontal rainstorms and warm-sector torrential rains (WSTRs). The roles are divided into the direct effects and the indirect effects, with the former taking the lead. The direct effects include the uplift of the moist air and the fraction of the ground contributing to the addition of turbulence. As for the indirect roles, key research questions emphasize the influence of low-level jets effects. The data of the precipitation figures comes from ERA5 reanalysis data of ECMWF, and the topography data is excerpted from GEBCO Gridded Bathymetry Data Download. In conclusion, extreme rainfall events are significantly intensified by South China's rugged topography, leading to increased risks of landslides and flooding. It emphasizes the need for improved disaster management strategies and long-term data collection to better understand and mitigate these impacts.
Research on Kinematic Modeling Methods for Six-Axis Robotic Arms
Authors: Li Jiayi , Li Yuezhou , Su Ruoque , Zheng Yuxuan
Published: January 2025

With the rapid advancement of technology, 6-DOF robotic arms, capable of simulating complex human arm movements and achieving precise positioning and manipulation in 3D space, have become crucial in modern industrial automation. This paper aims to validate the design and implementation of a six-degree-of-freedom (6-DOF) robotic arm controlled by a Raspberry Pi 4B. The robotic arm, composed of six servo systems, is capable of object grasping and manipulation in three-dimensional space. This paper presents forward and inverse kinematic models for the robotic arm's motion control and discusses their application in object grasping tasks. With technological advancements, robotic arms have become increasingly important in various fields such as manufacturing, medical, and construction. This study delves into the forward and inverse kinematics of the 6-DOF robotic arm through the development of new formulas, solutions, and control programs, aiming to achieve precise control over the arm's movements during object manipulation. By creating and testing custom formulas, solutions, and programs, this research seeks to implement and validate the theoretical aspects of the robotic arm's design and operation.
Object Detection based on HSV in OpenCV
Authors: Song Puchuan
Published: January 2025

In the realm of computer vision and automation, the ability to identify and track objects in real time is critical for various applications, including robotics, surveillance, and machine control. This paper explores the process of controlling a Raspberry Pi computer equipped with a camera to detect and locate circular objects with a green color. By leveraging the power of open-source libraries and image processing techniques, the system performs real-time analysis of camera input to identify target objects based on color and shape. The research employs a literature review methodology, synthesizing information from relevant studies on computer vision, object recognition, and color filtering techniques. The paper focuses on the use of algorithms for color segmentation, contour detection, and shape recognition to achieve accurate object identification. Through this method, it examines the effectiveness and limitations of using a Raspberry Pi for object detection tasks in constrained environments. The findings indicate that Raspberry Pi, when coupled with optimized algorithms, provides a cost-effective solution for basic object detection tasks, though its performance is limited by processing power compared to more robust systems. This review emphasizes the significance of affordable solutions for educational and small-scale automation purposes, highlighting their potential impact on future applications in low-cost robotics and IoT devices.
Application potential of ZnO nanoparticles in orthopedic
Authors: Su Xinmeng
Published: January 2025

Orthopedic implants are susceptible to bacterial infections such as S. aureus and E. coli, which increase the risk of postoperative infection. Surface modification can effectively address this issue by imparting new properties to the implants without affecting their mechanical strength and Young's modulus. Zinc oxide nanoparticles have been widely studied due to their excellent bactericidal properties, osteogenic characteristics, and biological functions. The article aims to offer an extensive analysis of its bactericidal, cytocompatibility, and osteogenic advantages as well as potential limitations, and explore the feasibility of their application in orthopedic implants. It aims to provide new ideas and methods for solving implant-related infections and promoting bone repair.
Additives in Interfacial Engineering for Performance Improvement of Perovskite Solar Cells
Authors: Lu Yongchen
Published: January 2025

Perovskite solar cells (PSCs) have gained significant attention in recent years due to their high efficiency and low cost. However, challenges remain in addressing non-radiative recombination at interfaces and improving stability, largely due to interfacial defects, energy level mismatches, and environmental factors. Interfacial engineering, as an effective approach, can significantly enhance the efficiency and stability of perovskite solar cells by optimizing energy level alignment, improving charge transport, and reducing defect density. This paper reviews the application of interfacial engineering in PSCs, focusing on common interfacial modifiers, such as organic small molecules, polymers, and two-dimensional materials, and systematically review their mechanisms in optimizing charge transport and passivating interfacial defects. Summarizing recent research progress in interfacial engineering, this study highlights future directions towards multifunctionality, commercialization, and environmental sustainability.
A Robot Autonomous Navigation System Based on Deep Reinforcement Learning
Authors: Li Xusheng
Published: January 2025

This paper provides an in-depth review of advancements in robotic motion control systems through the application of Deep Reinforcement Learning (DRL). The study highlights the growing complexity of robotic tasks. It emphasizes the need for adaptive control strategies in dynamic, uncertain environments where traditional methods fall short. By categorizing DRL algorithms into value-based approaches, such as Deep Q-Networks (DQN), and policy gradient methods, like Proximal Policy Optimization (PPO), the paper offers a comparative analysis of their applications in robotic systems. Key areas explored include autonomous navigation, object manipulation, and human-robot interaction. "The authors present specific simulation results, including performance metrics and comparative data, to demonstrate DRL's superior capabilities in real-time robotic control." Challenges in transferring DRL from simulation to real-world applications, such as sensor noise and computational constraints, are also discussed. The paper concludes by suggesting future research directions, particularly the integration of DRL with other machine learning techniques to enhance robot capabilities in multi-agent systems.
Research on the Application of Social Media Data Mining Based on Sentiment Analysis
Authors: Hu Yanning
Published: January 2025

As social software becomes increasingly prevalent, social media platforms have evolved into a major avenue for individuals to express their emotions. On these platforms, users convey their emotions through text, images, and videos. Their expressions not only reveal their personal emotional state but also correlate with subsequent behavioral patterns. As an invaluable tool for understanding human psychology, it is able to identify emotional tendencies and make correlation predictions by analyzing vast quantities of user data on social media. Recently, sentiment analysis methods and applications have evolved significantly, spanning fields like marketing, public relations, and social research. However, it continues to encounter significant challenges in complex contexts and cross-cultural communication. To gain a deeper understanding of the applications of sentiment analysis in social media, the paper surveys the principal methods and domains of sentiment analysis in recent years by literature investigation, with particular attention to multidisciplinary case studies. The findings indicate that sentiment analysis techniques are effective in some areas like real-time monitoring and trend prediction, which are vital for business decision-making and planning. Meanwhile, sentiment analysis is increasingly being applied to areas such as mental health monitoring as well as social opinion analysis. Nevertheless, current methods still struggle with complex contexts and cross-cultural sentiment expressions, necessitating further research and improvement.
Wearable Sensors for Smart Electronics
Authors: Xia Yaoqi
Published: January 2025

Wearable sensors now play an important role in our daily life. With the advances of technologies, it has become a mature solution for continuous detection. These advanced systems leverage innovations in material science, microfabrication, and integration with the Internet of Things (IoT) to deliver continuous, real-time data on a variety of physiological parameters. Also, the development of material science allows us to produce biocompatible and flexible materials which enables the sensors to be easily attached to our skin. Meanwhile, they provide accurate and reliable measurements of vital signs such as glucose levels, lactate, pH, and more. Recent advancements include the use of microfabricated chips for sorting particles, graphene-based chemical sensors, and biofuel-powered soft electronic skins. These technologies are not only able to evaluate our body status noninvasively but also ensure its biocompatibility. The integration of near-field communication (NFC) technology further simplifies the electronics, making these systems more user-friendly and accessible. This abstract explores the cutting-edge developments in wearable sensor technologies and their potential to transform personalized health management and improve overall quality of life.
An review of the production of polyolefins
Authors: Gu Xinzhe
Published: January 2025

Polyolefins, including polyethylene (PE) and polypropylene (PP), are important high molecular weight plastics derived from olefins. They are used in various industrial and everyday uses due to their sustainability, low density and resistance to degradation. This paper reviews the development and current situation of polyolefin production methods emphasising the progress of the catalyst and polymerization process. The key process of synthesis of olefin, Fischer-Tropsch synthesis, is summarized, and its variation, low temperature method (LTFT) and high temperature method (HTFT), as well as the role of catalyst accelerator in improving the reaction efficiency and selectivity are discussed. Various polymerization techniques, including high pressure, low pressure and gas phase polymerization, have also been studied, each of which has unique advantages for the molecular weight and crystallinity of polyolefin. Notable advances include the development of Ziegler-Natta and supported metallocene catalysts, which have significantly improved polymer quality and production efficiency. Recent work has focused on improving sustainability through recycling, renewable raw materials, and creating biodegradable and functionalized polyolefins. Future directions emphasize the need for more efficient catalysts, reduced carbon emissions, and optimized processes to improve environmental and economic benefits. This comprehensive review highlights the progress and challenges facing the polyolefin production industry, highlighting its importance and the potential for future innovation.
The use of Carbon Nanotubes in Enhancing Bone Implants
Authors: Li Yanzhe
Published: January 2025

Nowadays, traditional implants (for instance, Ti-based alloy, 316L stainless steel and Mg alloy) still suffer from defects such as poor wear resistance and may degrade and release toxic ions. These properties may inhibit cell viability, causing a stress shielding effect, and requiring multiple operations to make the implants function properly (but too many surgeries will put a burden on the patient's body). The superior mechanical properties, biocompatibility, and potential to enhance implant function have made carbon nanotubes (CNTs) a significant interest in bone implants. This study investigates the integration of CNTs into various alloys, focusing on their effects on mechanical strength, wear resistance, and biocompatibility. CNT-reinforced alloy composites exhibit enhanced tensile and compressive strengths and increased hardness, making them suitable for orthopaedic applications. In addition, CNTs improve cell adhesion and show antimicrobial properties, making them promising for reducing the infection risk of biomedical implants. CNT-doped alloys offer a lot of potential to enhance bone implant function overall, but more study is required to maximize its application in clinical situations.
Artificial Intelligence-Driven Ultra-Short-Term Photovoltaic Power Forecasting: A Comprehensive Review
Authors: Sun Wenbo
Published: January 2025

Photovoltaic (PV) power prediction is an important tool for optimizing PV power generation efficiency and grid operation. PV power is affected by weather conditions, time series, and other complex factors, which requires accurate data processing and feature extraction. This paper firstly introduces data processing methods in PV power prediction, such as missing value processing, outlier detection and correction, and data normalisation, to ensure data integrity and improve model training efficiency. Secondly, the applications of several machine learning methods are discussed, including linear regression (LR) , support vector machines, and random forests for handling nonlinear relationships and assessing feature importance. Finally, the advantages of deep learning methods in PV power prediction are analyzed. Convolutional neural networks are used for extracting spatial features. Long short-term memory networks(LSTM)and Bi-Directional LSTM Networks effectively handle time-series data and their global dependencies. This significantly improves prediction accuracy and stability. Finally, it concludes and looks at future research trends in Ultra-Short-Term Forecasting of Photovoltaic Power.
Bifacial solar panel with mirror condenser system
Authors: Chen Weijie
Published: January 2025

This paper introduces an innovative method to enhance the efficiency of bifacial solar panels by 140% relative to standard configurations through the deployment of a reflective mirror array. As a pillar of clean energy, solar technology has made strides in photovoltaic (PV) and concentrating solar power (CSP) systems. Bifacial solar panels, designed to capture sunlight from both surfaces, have emerged as a significant innovation, albeit with a higher production cost. Our proposed system synergizes the advantages of PV and CSP by integrating a bifacial solar panel with a mirror array, complemented by a sun tracking system, precision motor units, and a smart control computer. This engineered solution is optimized to enhance sunlight irradiance on the panel surfaces, thereby maximizing efficiency. The simulation environment for this system is set in Tucson, Arizona, a region known for its high solar insulation. We conduct a detailed simulation of a day's energy production and, using historical weather data, estimate the annual energy output for a single system. The paper delineates the system's design, operational mechanics, and the theoretical framework that underlies its enhanced performance, providing a comprehensive projection of its year-round energy generating potential
Research on the Design of Low-Power SAR ADC for Wearable Health Monitoring Devices
Authors: Hu Zhixian
Published: January 2025

In the current era of rapid technological development, the proliferation of wearable devices and Internet of Things technology is significantly elevating the demand for low-power, high-performance analog-to-digital converters (ADCs). Especially in health monitoring, these devices must boost battery life and efficiency without sacrificing performance. Consequently, the research and development of low-power successive approximation register (SAR) ADCs is essential for advancing these technologies. This paper explores the design of low-power SAR (Successive Approximation Register) ADCs. Due to their digital architecture and low power consumption, SAR ADCs are particularly well-suited for these applications. Moreover, it outlines the working principle, performance indicators, and low-power design strategies of SAR ADCs, while also discussing their applications in health monitoring, and addressing the challenges and future development prospects. While high accuracy and resolution are essential, this research primarily aims to reduce power consumption via innovative design. The results emphasize the potential of SAR ADCs in enhancing health monitoring devices, and continuous research aims to achieve higher accuracy and lower power consumption
Review of 3D Printed Concrete: Mix Design
Authors: Chen Jiakun
Published: January 2025

3D printing technology has gained attention for its potential to revolutionize concrete construction by offering improved automation, reduced costs, and enhanced environmental benefits. This paper provides an overview of current research on 3D-printed concrete (3DPC), with a focus on mix design and the fresh and hardened performance of 3DPC. The review examines the differences between 3DPC and traditional concrete, highlighting how factors such as mix composition, binder selection, and aggregate types affect the material's flowability, buildability, and mechanical properties. Notable advancements in optimizing 3DPC mix designs are discussed, including the use of fine aggregates and fibers to improve performance. The paper also covers methods for testing the early performance of 3DPC. Moreover, the review identifies ongoing challenges such as anisotropy and interlayer bond strength, which impact the mechanical properties of 3DPC. The paper aims to provide insights into the current state of 3DPC research and its practical implications for construction.
Analyze the Influence of Positive Feedback to Declarative Memory Strength
Authors: Wang Zeyu
Published: January 2025

Extensive research in neuroscience and psychology has focused on memory consolidation driven by positive feedback. This paper elucidates methods to enhance memory retention. Evidence indicates that dopamine plays a crucial role in the formation of declarative memories associated with rewarding stimuli and may affect the generalization of reward value. An event-related fMRI study investigated the anticipatory mechanisms involved in reward-based memory formation during a monetary incentive encoding task, where participants were presented with cues signaling high or low-value rewards for memorizing forthcoming scenes. Following a 24-hour evaluation, participants exhibited significantly improved recall of images associated with high-value cues compared to those linked to low-value cues. Additionally, a separate monetary incentive delay task pinpointed specific brain regions involved in reward anticipation. In the encoding phase, high-reward cues that preceded recalled events activated the ventral tegmental area, nucleus accumbens, and hippocampus. Enhanced activation in these areas was predictive of stronger memory retention. The relationship between the hippocampus and ventral tegmental area was correlated with improved long-term memory retrieval. These results imply that neural activity prior to stimulus encoding can serve as a predictor for declarative memory formation, reinforcing the notion that positive feedback bolsters memory by facilitating dopamine release in the hippocampus before the learning process.
Climate Change and the Poor: Reasons, Impacts, Inequities, and Sustainable Solutions
Authors: Gao Xiaoquan
Published: January 2025

This paper thoroughly analyzes the complex and challenging impacts of climate change, driven by human activities like as greenhouse gas emissions, deforestation, and scenery- use changes. It examines the consequences, especially sea level rise, extreme weather events, and ecosystem disruption. The research demonstrates how these issues, especially those that affect the poor, exacerbating their vulnerability through increased natural disasters, leading to loss of essential resources and widespread displacement. The problems become more complicated as a result of deteriorating water, food security, and severe health threats. Such conditions undermine impoverished communities' ability to sustain their livelihoods and resilience. Additionally, the study demonstrates how women and marginalized groups face more serious effects, with gender-specific vulnerabilities worsening their social and economic hardships. Scattered barriers to resources and systemic inequalities increase risks for these populations, limiting adaptive capacity and deepening socio-economic disparities. The most impoverished are fewer likely to receive resources and assistance programs, which worsens unequal opportunities. In response, the paper proposed a multi-layered method that combines adaptation and mitigation. For adaptation efforts, they include enhancing agricultural resilience, improving health systems, strengthening disaster response. Sustainable forest management, green energy promotion, and emissions reduction are among the mitigation approaches that are most important. Moreoverm, the paper emphasizes the value of public participation, policy support, and international cooperation in order to effectively implement these strategies for a more profound and sustainable future
A Review of Wildfire Effects on Forests Soil Properties in China
Authors: Liu Guanhong , Liu Xiaodong , Li Bingyi
Published: January 2025

The increasing frequency and intensity of wildfires pose significant threats to forest ecosystems, particularly in Chinas diverse climatic regions. This review focuses on the impacts of wildfires on forest soil properties in China, highlighting changes in soil physiochemical properties, nutrient cycles, enzyme activities, and microbial communities. Wildfires often lead to the loss of soil organic matter, disruptions in nutrient availability, and alterations in microbial dynamics, which are essential for nutrient cycling and ecosystem recovery. The severity of these impacts is closely linked to fire intensity, soil depth, and post-fire management practices. While short-term recovery of soil properties is possible, long-term effects on soil health, especially on microbial diversity and nitrogen cycling can persist for years. This review underscores the importance of region-specific research and long-term monitoring to better understand the complex interactions between soil and vegetation post-fire. Future research should focus on the influence of climate change on wildfire patterns, the role of innovative restoration techniques, and the development of comprehensive management strategies tailored to Chinas unique forest ecosystems
Research on the Light Absorption Properties of Nanoparticles in the Cosmetics Industry
Authors: Luo Minjun
Published: January 2025

Cosmetic materials can directly affect the performance, safety and user experience of products. High quality materials can ensure that cosmetics can effectively improve skin conditions, maintain makeup for a long time, reduce the risk of allergy, and improve overall beauty and comfort. With the progress of science and technology, the R&D and application of cosmetic materials are increasingly rich, and strive to meet the needs of different skin types and styles. However, the light absorption performance of traditional materials is poor, which is not enough to achieve the desired cosmetic effect. In contrast, nanomaterials have unique structural characteristics, such as high surface area volume ratio and enhanced optical properties, which can significantly improve the absorption and utilization of light. The paper studies the exploration and application of nano materials in cosmetics, and their potential to change the industry. In addition, the potential disadvantages and hazards of these nanomaterials are also discussed. The significance of this work is to summarize the current situation of nanomaterials in the cosmetics industry and new possibilities for more effective and innovative cosmetics products
Comparison and Selection of Filters for Target Tracking
Authors: Liu Wenjie
Published: January 2025

In today's fast-paced fields like robotics, computer vision, and autonomous systems, researchers often face the challenge of selecting the most effective filters for accurate and reliable target tracking. Understanding the principles, usage, and appropriate scenarios of different filtering techniques is crucial for making informed decisions. This paper explores how to choose the right filter by focusing on four key types: Kalman filters, particle filters, adaptive correlation filters, and Joint Probabilistic Data Association (JPDA) filters. Each filter offers unique advantages and caters to specific conditions and requirements. Kalman filters are optimal for linear systems with Gaussian noise, while particle filters are well-suited for nonlinear and non-Gaussian systems. Adaptive correlation filters excel at tracking objects whose appearance changes over time, and JPDA filters are effective in multi-target tracking scenarios. By discussing the principles, strengths, limitations, and applicable scopes of these filters, this paper aims to assist researchers in selecting the most suitable filtering method for their specific target tracking needs.
Multisim-Based Digital Comparator Design and Performance Optimization
Authors: Lu Xiaohui
Published: January 2025

Nowadays, as many electronic products are moving towards increased portability and high reliability, higher demands are being placed on the various performance parameters of digital integrated circuits. Currently, digital integrated circuits are developing in the direction of low power consumption. Digital comparators are widely used in many areas of digital integrated circuits. Power consumption has an impact on many performance parameters of digital integrated circuits, so it is important to optimize power consumption during the design of digital integrated circuits. Therefore, a digital comparator consisting of CMOS logic gate circuits is designed and further, the comparator energy consumption is optimized and functional simulation is carried out using Multisim software in this paper. This paper describes the principles of the logic gates that make up the designed comparator, the design of the circuit according to the function of the comparator, and the calculation and optimization of the energy consumption of the digital comparator. Through analysis and calculation, the comparator is able to realize the numerical comparison function, and different numerical comparison results correspond to different output signals. After optimization, the comparator is able to achieve a reduction in power consumption.
РОЛЬ ГРОМАДСЬКИХ ОРГАНІЗАЦІЙ У ФОРМУВАННІ НАЦІОНАЛЬНОЇ ІНФОРМАЦІЙНОЇ БЕЗПЕКИ
Authors:   "Науково-дослідний центр «Інститут соціально-правових та політичних досліджень імені Олександра Яременка» Українського державного університету імені Михайла Драгоманова, ЗАХАРЕНКО КОСТЯНТИН ,   м. Київ, Україна,   e-mail: kzakharenko@ukr.net,   ORCID: 0000-0003-0980-7313"
Published: January 2025

"Громадські організації та ініціативи відіграють ключову роль у формуванні національної
інформаційної безпеки, сприяючи розвитку інформаційного суспільства та зміцненню
громадянського суспільства. Ці інституції виникають через посилення взаємодій
в інформаційному середовищі, зміни у способах життя та цінностях, а також утвердження
ролі громадян у політичних процесах. Громадські організації допомагають знижувати ризики,
пов’язані з інформаційними атаками, і забезпечують консолідацію зусиль для вирішення
соціально-політичних проблем. Вони активно співпрацюють з державними інституціями
для підвищення ефективності управлінських рішень і сприяють демократизації політичного
життя."
Contribuição ao estudo do crime de concorrência desleal
Authors: Gonçalves Aline Martins , Junior Walter Godoy dos Santos
Published: January 2025

O artigo analisa a evolução da concorrência desde a Idade Média, quando surgiram as corporações de ofício que monopolizavam a produção e a comercialização nos burgos. Com o declínio do feudalismo e a ascensão do mercantilismo, o Estado começou a regular o mercado com políticas protecionistas e concessões de monopólios por “carta patente”. A Revolução Industrial enfraqueceu as corporações e fortaleceu o liberalismo, promovendo a livre concorrência. Contudo, a falta de regulamentação específica levou ao surgimento de práticas comerciais parasitárias, necessitando a criação de leis de propriedade intelectual. A legislação evoluiu para proteger a lealdade no mercado e os direitos dos consumidores, como evidenciado pela Lei de Propriedade Industrial no Brasil e tratados internacionais como o Acordo TRIPS. Assim, a repressão à concorrência desleal evoluiu para proteger não apenas direitos individuais, mas também supraindividuais, equilibrando liberdade e lealdade na concorrência e assegurando a proteção do mercado e do consumidor.
Implementasi Pembelajaran Berbasis Alam sebagai Sarana Pengembangan Karakter Sosial Peserta Didik di Sanggar Anak Alam
Authors: Ayuni Febiana Atika , Mujib Muhammad Yazid Fatkhul , Ahriza Davina Putri Cony , Hidayatusholiha Alvi , Aprilia Popy , Muhtarom Taufik
Published: January 2025

This study examines the implementation of Nature-Based Learning (NBL) at Sanggar Anak Alam (SALAM) as a means of developing students' social character. A descriptive qualitative method was used, with data collection techniques including observation, interviews, and documentation. The subjects of the study were 15 students selected purposively. The results showed that NBL was implemented through project-based activities, inclusive interactions, and regular reflections, effectively shaping students' responsibility, empathy, and environmental awareness. Despite challenges such as limited facilities and a lack of teaching method variation, curriculum innovation and community involvement helped address these issues. The findings emphasize that NBL not only enhances academic skills but also social character, making it a relevant and applicable holistic education model
Dampak Kebiasaan Memaki dan Menghina di Sekolah SDN 03 Buntulia Beserta Pencegahannya
Authors: Adam Muh. Wahyuddin S. , Madu Ainun , Suleman Manda , Bangga Nurfadhila
Published: January 2025

The habit of swearing and insulting can have a negative impact on students' social and emotional development, thus affecting the learning atmosphere at school. This study aims to find out the habit of swearing and insulting in the SDN 3 Buntulia environment and make efforts to prevent it. This study uses a quantitative method by distributing questionnaires to students to collect data to analyze the frequency of these habits. The results of this study show that although most students know the meaning of swear words and many of them have heard their classmates, only a small percentage admit to using abusive words. This shows that awareness of the use of vulgar language is still low and the need to increase prevention in schools to limit such behavior. For further research, it is better to use a mixed method by adding interviews or group discussions to obtain results. Gain a deeper understanding of the reasoning behind abusive and offensive behavior in students.
Pengembangan Media Pembelajaran Booklet untuk Meningkatkan Keterampilan Membaca Pemahaman Siswa Kelas IV di SDN 7 Sungai Raya
Authors: Ningsih Ayu , Afryaningsih Yunika , Listiarini Yuni
Published: January 2025

This research and development aims to produce booklet learning media that is feasible, practical and effective. The problem in the learning to read process is the lack of reading comprehension skills in class IV due to the lack of learning media during the learning to read process, making students tend to get bored with learning and not interested in participating in learning to read. So many students can read but don't understand what they read. This is what encourages researchers to try to develop booklet learning media. The type of research used in this research is the Research and Development (R&D) method or research and development developed using the ADDIE model which consists of five stages, namely, Analysis, Design, Development, Implementation, and Evaluation. The data analysis used is quantitative and qualitative analysis. The results of this research and development are based on research results from experts, the results obtained from media experts were 95%, material experts were 100%, and language experts were 93.75% to obtain feasibility. Practicality results were obtained from student response questionnaires and effectiveness results were obtained from observational assessments of reading comprehension skills
Analisis Metode Team Games Tournament Terhadap Kemampuan Menulis Naskah Berita pada Pembelajaran Bahasa Indonesia Kelas Xi Mastahfizil Qur’an
Authors: Siregar Rodia Tammardia , Gajah Ewin Sanjaya , Panjaitan Devi Hertina , Azzahra Annisa , Maulani Syafina , Marpaung Ayu Anggreini
Published: January 2025

The Team Games Tournament (TGT) learning model is applied in Indonesian language learning to improve the news text writing skills of grade XI students at MASTAHFIZIL QUR’AN. The purpose of this study was to determine and describe the effectiveness of the TGT model application in improving news text writing skills. The data collection techniques used were observation, interviews, and written tests which were then analyzed descriptively. Based on the research conducted, it was found that the TGT application was able to improve students’ abilities in understanding the structure of news texts, and organizing information systematically. This model also creates an interactive learning atmosphere through competition and group cooperation. In addition, TGT has a positive impact on students’ critical thinking skills, collaboration, communication, and creativity. Researchers also found several obstacles, such as the difficulty of students adapting to the group-based learning system. These obstacles can be overcome through intensive guidance from teachers. Researchers concluded that the TGT model not only improves students’ writing skills, but also strengthens social interaction and a sense of responsibility in group-based learning