Science and Technology of Engineering, Chemistry and Environmental Protection
ISSN 2960-1339(Online) Editor: Cosimo Arnesano (University of Southern California), Alexei (Columbia University), Aytek Erdil (University of Cambridge), Cosimo Arnesano (University of Southern California), Freya Baetens (Cambridge University), Osman (Carnegie Mellon University) Science and Technology of Engineering, Chemistry, and Environmental Protection provides a platform for researchers, scholars, and professionals to share their latest findings and insights in engineering, chemistry, and environmental protection. With a strong multidisciplinary focus, the journal covers a wide range of topics, including materials science, environmental engineering, chemical synthesis, renewable energy, and pollution control, ensuring that readers stay informed about the most relevant and cutting-edge research. Committed to bridging theory with practice, the journal emphasizes research with real-world applications that can inform industry advancements and policy-making. Additionally, the involvement of a chief editor from IEEE enhances the journal’s influence by introducing the most authoritative review standards, ensuring the highest level of academic rigor and credibility.
Fundamentals, Challenges, and Improved Algorithms of PID Control for Quadrotor UAVs
The quadrotor unmanned aerial vehicles (UAVs) are widely employed in power line inspections, agricultural monitoring, cargo delivery, and surveying operations because of their straightforward design and remarkable agility. However, traditional proportional-integralderivative (PID) controllers perform poorly under wind, load changes, and sensor noise, which limits the UAV’s precision and flight reliability. Thus, this paper reviews the current development of PID control for quadcopter UAVs, examines the limitations of traditional PID controllers, and compares the performance and applications of various improved algorithms, offering guidance for controller selection and parameter tuning. By analyzing relevant literature on improvement strategies, such as fuzzy adaptive PID, evolutionary learning-optimized PID, data-driven adaptive PID, deep reinforcement learning PID (DRL-PID), and hybrid model PID, and integrating existing simulation and experimental data, key performance indicators such as stability, speed, disturbance rejection, and computational load are summarized. The results show that data-driven PID like DRL-PID achieves the highest accuracy, while fuzzy adaptive PID remains widely used in engineering for its low computational load and real-time performance. Each algorithm has its strengths in different applications: fuzzy adaptive PID is suitable for real-time tasks, RL-GA and hybrid models are suitable for high-precision mapping, and WRLS adaptive PID is suitable for logistics with frequently changing loads. Meanwhile, lightweight DRLPID and multi-UAV cooperative control are promising future directions.
Copyright (c) 2026 Science and Technology of Engineering, Chemistry and Environmental Protection
Optimization Design of Three-Layer Glass Thickness Based on the Ant Colony Algorithm
In this paper, the three-layer glass system is taken as the research object, and its optical transmission performance is optimized to enhance the energy-saving effect. Based on the theory of thin-film interference, an optical transmission model that accurately describes the propagation of light in multi-layer media is established to evaluate the spectral transmittance under different thickness combinations. To solve the problem efficiently, an improved ant colony algorithm is introduced. This algorithm simulates the optimization behavior of ants foraging and is characterized by strong global search capability and good robustness, making it suitable for such multi-dimensional, nonlinear combinatorial optimization problems. The optimization objective is to maximize the total solar radiation transmission energy of the glass system within the main solar radiation band of 300- 2000 nm, which covers the visible and near-infrared energy crucial for building lighting and passive heat gain. Through iterative optimization of the algorithm, an optimal set of glass thickness configurations is finally obtained: 4.2 mm, 5.7 mm, and 3.9 mm. This solution can make the most effective use of incident solar radiation while ensuring structural safety and process feasibility. The optimization process converges around the 60th generation, demonstrating the algorithm’s good efficiency and stability. By integrating optical models and intelligent algorithms, this study provides a scientific and efficient methodology for the design of photothermal performance and energysaving potential of building envelopes.
Pathways for Enterprise Informatization Reengineering in the AI Era: Empowering Intelligent Systems and Driving Sustainable Growth
The rapid development of artificial intelligence (AI) has opened new pathways for enterprise informatization. As data evolves from mere information to knowledge and eventually wisdom, its role has transformed from a simple resource to a strategic asset with substantial economic value, driven by the growing utility and market value of data. In the context of AI, enterprise informatization calls for the construction of a “Growing Basement”, a platform that enhances knowledge and data management, integrates legacy systems, and intelligently addresses realworld digitalization challenges within enterprises. By enabling systems to think and self-optimize, enterprises can achieve true digital intelligence”, convert it into tangible productivity, enhance competitiveness, and foster sustainable growth.
Copyright (c) 2025 Science and Technology of Engineering, Chemistry and Environmental Protection
Beyond Sequestration: Evaluating Mangrove Restoration through a Net Global Warming Potential Framework
As a critical component of blue carbon ecosystems, mangroves have attracted increasing attention under the global climate change agenda. Their unique carbon storage mechanisms encompass both above-ground biomass and substantial soil organic carbon pools. However, most existing studies and policy assessments tend to overlook the emission dynamics of non-CO₂ greenhouse gases such as methane (CH₄) and nitrous oxide (N₂O), which may significantly distort evaluations of their net climate benefits. This study systematically reviews the mechanisms of carbon sequestration and non-CO₂ fluxes in mangrove ecosystems, compares climate and ecological co-benefits under different restoration strategies across regions, and examines the compatibility of current carbon market methodologies and policy instruments. Findings suggest the urgent need for an integrated assessment framework centered on Net Global Warming Potential (Net GWP), which incorporates ecological integrity, biodiversity recovery, and social co-benefits. Such a transition from carbon-centric to climate–ecology-oriented mangrove restoration is essential for achieving genuine mitigation outcomes and long-term ecosystem sustainability.
Fundamentals of AI and Deep Reinforcement Learning
The sphere of Artificial Intelligence (AI) develops quickly, and Deep Reinforcement Learning (DRL) is one of the innovative methods to create adaptive intelligent systems. DRL is a combination of reinforcement learning and deep neural networks, allowing agents to learn the best strategies by interacting with their environments and achieve better performance as time goes on. The following paper will provide an overview of the concept of DRL and its usage and limitations, especially in three areas where it has been used most: computer vision, natural language processing (NLP), and robotics. A comparative analysis of the literature was conducted. The review grouped research in the three domains and compared DRL algorithms according to the performance measures of accuracy, success rates, and sample efficiency. It is found that with DRL, significant progress has been made on vision-related tasks, such as 88% accuracy in medical imaging and 85% in object detection. Dialogue and conversational systems based on DRL models show 75-82% success rates in NLP. Robotics DRL allows significant amounts of manipulation and locomotion control, and has been adequate in the range of 72 to 78 percent, even though safety and efficiency issues have still been encountered. The study details that DRL is a valuable AI technology that needs specific improvements to achieve data effectiveness, readability, and risk-free implementation. Overcoming these challenges will enable wider acceptance in high-stakes industries and increase DRL’s potential to solve even more complex problems and issues in society and technology.
Research on the Asymptotic Convergence of Bayesian Regression and Least Squares Regression under the Condition of Large Samples
This paper compares the large-sample asymptotic convergence of Bayesian linear regression and ordinary least squares regression. It analyzes their fundamental differences from three aspects: theoretical foundations, convergence paths, and asymptotic performance, and points out the advantages of Bayesian methods in scenarios with moderate sample sizes and reliable prior information. Furthermore, a unified framework is constructed to reveal the relative efficiency of both methods under different model settings through mathematical derivation and simulation studies. Based on the Bernstein-von Mises theorem and asymptotic statistical theory, it is demonstrated that the two methods are asymptotically equivalent under regularity conditions. This provides a theoretical basis for method selection in practical applications.
Safety Interaction Mechanisms and Robust Control Methods for Human-Robot Collaborative Systems in Dynamic Environments
Traditional human-robot collaboration methods primarily rely on rule-based control and physical human-robot interaction, suitable for industrial scenarios with clear rules and relative stability. However, with the advancement of robotics technology, complex environments challenge the rapid adaptability of traditional approaches, limiting system adaptability. We propose a safety interaction framework based on multimodal perception, adaptive safety constraint generation and dynamic adjustment, and collaborative decision-making strategies to address risks arising from dynamic uncertainty. At the robust control level, we integrate adaptive control with intelligent optimization strategies to handle unpredictable human behavior and dynamic environmental changes. This research achieves dynamic obstacle avoidance and conflict resolution through adaptive safety constraints, significantly enhancing the safe operation of human-robot collaborative systems across diverse scenarios.
The Function and Utility of Visual Methods in Calculus learning
Visualization plays a crucial role in understanding and teaching calculus, bridging the gap between abstract mathematical concepts and intuitive comprehension. This essay explores the significance of visualization in calculus, examining how graphical representations and geometric interpretations enhance the learning of fundamental topics such as derivatives and vectors or matrix. By analyzing diverse strategies and technological tools—such as graphing calculators and computer algebra systems—this paper demonstrates how visualization aids in conceptual clarity, problem-solving, and engagement. Additionally, the essay discusses challenges and limitations, including potential over-reliance on visual aids and the need for balanced analytical reasoning. Ultimately, the study argues that effective integration of visualization techniques can deepen mathematical understanding and foster a more accessible and engaging calculus education.
Technological Evolution in Additive Manufacturing: From Three-Dimensional to Multidimensional Printing Systems
Though conventional 3-Dimensional (3D) printing or additive manufacturing (AM) has emerged as a disruptive technology, the printed components are functionally limited due to their static nature. Fortunately, the expansion of this technology in multiple dimensions will result in dynamic functional structures. 4-Dimentional (4D) printing adds a temporal dimension to 3D printed components, using technologies such as shape-memory polymers (SMPs) to enable the components to deform over time. 5-Dimentional (5D) printing is similar to 3D printing, but the build plate tilts on two additional axes that can angle the print in such a way that support structures are not needed. 6-Dimentional (6D) printing, in a similar way to 4D printing relative to 3D printing, enables 5D printed components to change over time. 7-Dimentional (7D) printing can produce complex, fine, and intricate products that traditional manufacturing methods cannot achieve. These technologies can be used in medical applications such as organ replacement and bone grafting, as well as in the food industry to achieve ideal colours, flavours, and nutritional components.
Bayesian Hierarchical Modeling for Stock Price Forecasting: Evidence from Apple Inc. (AAPL)
Stock price forecasting has long been a significant research topic in finance, and the stock market has grown increasingly popular in recent years. As a popular saying goes, “even the aunts in the vegetable market are speculating on stocks.” In a rapidly changing market environment, investors need more reliable forecasting tools to assist in decision-making. Traditional prediction methods often struggle to cope with the complexity and uncertainty of the market, whereas Bayesian statistical methods offer new possibilities in this field due to their unique probabilistic framework. This study takes Apple (AAPL) as an example to explore the application of Bayesian methods in stock price prediction. We have collected the company’s historical transaction data from 2015 to 2023, including key indicators such as daily opening price, closing price and trading volume. By establishing a Bayesian hierarchical model, we try to capture the inherent laws of stock price fluctuations. The study found that the Bayesian method can better deal with uncertainty in the stock market.
Prioritizing Recall: Recall‑First Machine Learning for Traffic Accident Severity Detection under Class Imbalance
Traffic safety agencies seek predictive models that identify high-risk crashes before traffic accidents occur, but realworld crash data are highly imbalanced, rendering the overall accuracy of machine learning models a misleading metric for predictive performance. This study investigates a recall-first approach to crash-risk classification in Virginia, arguing that missing a dangerous event carries a much higher cost than issuing an extra alert based on the data provided by Virginia Department of Transportation (VDOT). The modeling framed high-risk identification as a binary classification task and used standard tools, Logistic Regression and Random Forest, augmented with imbalance-aware training. Evaluation emphasized recall and precision, utilizing precision–recall curves and confusion matrices. Across experiments, recall-oriented training and thresholding consistently improved detection of high-risk cases relative to accuracy-optimized baselines, with expected trade-offs resulting in lower precision and overall accuracy. This paper further translates operational priorities into simple threshold policies, showing how agencies can tune models based on recall to align with resource constraints while minimizing costly misses. In conclusion, for safety-critical applications, recall should be the primary metric of success; furthermore, straightforward imbalance treatments—without complex model additions—can realign model performance with public-safety goals.
A Systematic Review and Quantitative Meta-Analysis of Multi-Modal SLAM Methods
Simultaneous Localization and Mapping (SLAM) technology holds utmost significance in robotics, autonomous cars, and augmented reality. This paper first addresses the limitations of single-modal SLAM under dynamic environments (e.g., light changes, lack of texture, sensor degradation). The current paper aims to critically review and quantitatively evaluate multi-modal SLAM methods via a systematic review coupled with quantitative meta-analysis. This study details a comprehensive literature search strategy adopted herein, i.e., the databases queried (IEEE Xplore, ACM Digital Library, Scopus), keywords, and time frame. It further extracts the most critical information derived from the literature, such as sensor configurations, fusion architectures, and performance measures including absolute trajectory error (ATE), relative pose error (RPE), and real-time performance (FPS) on public datasets (KITTI, EuRoC). Finally, it outlines the key conclusions of the meta-analysis. For example, quantitative results illustrate that tightly coupled visual-inertial (V-I) methods exhibit high accuracy in high-speed motion scenes, while visual-LiDAR fusion systems demonstrate greater robustness for large-scale mapping tasks. The conclusion emphasizes that multi-modal fusion is an inexorable trend towards achieving high robustness and high accuracy for SLAM.
Hydrodynamic Behavior of Blood Components in Elastic Vessels
The circulatory system’s intricate network of elastic vessels is governed by complex hydrodynamic phenomena that are crucial for tissue perfusion and physiological homeostasis. This review synthesizes findings from high-impact clinical observations and advanced computational modeling, focusing on the multi-physics approach of Fluid-Structure Interaction (FSI) to understand cardiovascular health and disease. The article discusses the non-Newtonian rheology of blood, pulsatile flow dynamics, and the impact of arterial stiffness on pulse wave velocity. It also explores the hydrodynamic behaviors of red blood cells, white blood cells, platelets, and plasma proteins, and how computational modeling, particularly Computational Fluid Dynamics (CFD) and FSI, provides insights into clinical applications. The review reveals that altered hemodynamics are fundamental drivers of cardiovascular disease and that computational models, especially FSI, are indispensable for accurate simulation and clinical application, highlighting the need for further research into mechanobiology and personalized medicine.
AI-based IoT Intrusion Detection Using Machine Learning
The development of smart home technology offers an unprecedented level of convenience, fundamentally transforming traditional home environments, but it also introduces the system to various cybersecurity risks such as unauthorized access and data leakage. With the expansion of the application scope of the Internet of Things network and the increase in the number of device deployments, traditional static defense methods have proven inadequate in the timely identification of complex intrusion behaviors and protecting network security. In response to this situation, this study proposes an intrusion detection system based on artificial intelligence, which can autonomously identify abnormal network traffic and isolate infected devices. The study used the CIC-IoT2023 and IoT-23 datasets to train and evaluate three machine learning models: logistic regression, support vector machine (RBF kernel), and random forest. The research results indicate that all three models exhibit high performance in terms of accuracy, robustness, and response speed, verifying the feasibility of applying artificial intelligence and machine learning to the security monitoring system of smart home systems. This study proposes a novel approach for constructing intelligent Internet of Things systems with self-defense capabilities.
Analysis of the Application and Prospects of Artificial Intelligence in Various Frontend Frameworks
In recent years, the rapid development of artificial intelligence has influenced software engineering thoroughly. Especially in front-end development, the combination of artificial intelligence with front-end frameworks such as Vue, React and Angular is becoming a significant factor in advancing productivity and developing efficiency. This paper mainly investigates the application mechanism of AI over different front-end frameworks, and also researches the prospects of future development. This study aims to analyze the different aspects of AI integration over several frameworks, comparing their characteristics’ influence on their performance in generating code and converting the layout design into code. Research adopts literature analysis and comparative study, combining existing AI development tools and academic materials. The accuracy, capacity and adaptiveness of AI within the front-end frameworks are discussed in this paper. The findings indicate that AI has established a significant efficiency advantage in front-end development by generating components automatically, optimizing the logic between code, and solving debug errors. However, due to differences in syntax features and data complexity, AI’s effectiveness varies from different front-end frameworks.
Research on the Current Situation, Challenges and Development Trends in the Field of Electric Vehicle Charging
This paper explores the challenges in the charging industry, particularly in the context of electric vehicles (EVs). It examines the current status of DC, AC, and wireless charging, revealing that DC fast-charging has a power range of 50-180kW, while ultra-fast charging exceeds 250-350kW. AC charging piles are crucial for daily energy replenishment, while wireless charging achieves efficiency close to wired charging but faces promotion obstacles. The study also highlights the diversified operation modes of charging facilities. Challenges include power grid impact, unbalanced layout, insufficient power access, and low return on investment. The paper proposes solutions and predicts development trends, predicting a shift towards high-power ultra-fast charging and intelligent Vehicle-to-Grid (V2G), upgrading infrastructure towards balance and intelligence, and shifting the market focus to “operationcentric” with capital concentration and diversified business models. This research aids in planning, policy-making, and enterprise operations in the charging field, contributing to EV promotion and carbon neutrality goals.
Robomaster Robot Automatic Targeting System Based on YOLOv11n and DeepSORT
Robomaster is a competitive robotics event where only hits on a robot’s four armor plates count as valid damage. To achieve fast and precise tracking of these armor plates, especially in the face of the enemy robot’s high-speed movements and the complex background of the arena, this paper proposes an automatic aiming system. The system integrates the YOLOv11n detection algorithm for target recognition and the DeepSORT tracking algorithm for continuous target tracking. The YOLOv11n model is responsible for detecting the armor plates in real-time, while DeepSORT keeps track of the target’s trajectory across multiple frames. To handle target occlusion and improve tracking accuracy, the system uses a Kalman filter to predict the robot’s movement and incorporates deep appearance features for more robust data association. This combination helps maintain precise tracking even when the target is partially blocked. Experimental results show that the proposed system significantly enhances the robot’s ability to track and hit the enemy’s armor plates, leading to improved accuracy in dynamic and challenging competition scenarios.
Plant-Based Meat as a Low-Carbon and Biodiverse Food Solution in the Anthropocene
Global food systems, particularly conventional livestock production, are among the key sources of climate change, resource degradation, and loss of biodiversity, which jeopardize planetary stability in the Anthropocene. This paper will be addressing the potential of plantbased meat as a sustainable low-carbon food that can be applied to mitigate environmental pressure and to support biodiversity conservation. This research is conducted through systematic review and synthesis of the current scientific literature, contrasting data on environmental effects of plant versus animal meat production, comparing socioeconomic barriers to uptake, and comparing proposed governance strategies. Analysis confirms plant-based meat has significant climate and resource benefits, including much lower greenhouse gas emissions, negligible land and water usage, and higher resource efficiency compared with traditional meat. Besides, it decreases ecosystem pressure via deforestation reduction, habitat loss, and pollution, thus fostering biodiversity and ecological resilience. However, adoption is constrained by socioeconomic variables such as higher cost, low consumer acceptability, and low public awareness. To the study, there is a requirement for a cooperative multi-stakeholder response including enabling government policies, business technological and marketing innovations, and civil society-led education initiatives to overcome these challenges. Plant-based meat thus provides a pragmatic and disruptive path towards building a more resilient, equitable food system for the world.
Practical Research on Big Data Analysis and Statistical Inference Using Python
The proliferation of data in modern industries has created a demand for statistical inference methods that are both predictive and scalable. This paper aims to close the gap between statistical inference theory and machine learning practice by utilizing Python’s rich functionality. This paper contributes and empirically demonstrates an end-to-end framework for a data scientist’s workflow, ranging from data cleaning and feature engineering to model construction and statistical validation. Through three real-world case studies in e-commerce, healthcare and finance, the paper empirically compares the relative merits of regularized regression, Bayesian classifiers, and ensemble methods. The findings reveal that Bayesian models offer superior uncertainty estimation in healthcare, where data is often scarce, whereas ensembles such as Gradient Boosting achieve state-of-the-art predictive accuracy in financial applications with big data. The paper emphasizes that statistical validation remains a mandatory step in building reliable machine learning systems. It also discusses practical challenges such as scalability, model interpretability, and data quality, and proposes mitigation solutions and future research directions. This research provides a practical guide to implementing statistical validation in data science workflows.
Review on Research of Control Technology of Autonomous Underwater Vehicles
The ocean serves as a vast and largely underexplored frontier, playing a critical role in global resource provision and climate regulation. In recent years, as understanding of its ecological and economic significance has deepened, underwater robots, particularly Autonomous Underwater Vehicles (AUVs), have emerged as indispensable tools for deep-sea exploration and development. Their unique capability to operate untethered from surface vessels offers distinct advantages over Remotely Operated Vehicles (ROVs), including a wider operational range, superior depth tolerance, enhanced maneuverability, and lower maintenance demands. With the growing strategic importance of the oceans in the 21st century, AUVs have proven vital in diverse fields such as resource assessment, scientific research, and military operations. This review aims to synthesize the current state of AUV control technology, identify pivotal research challenges, and outline emerging future trends. It examines a spectrum of control methodologies, ranging from conventional approaches to cutting-edge intelligent algorithms, while focusing on the unique constraints and testing challenges posed by the complex and unpredictable underwater environment.
Testing Momentum and Reversal Strategies in the Volatile New Energy Sector of China’s A-Share Market
The new energy sector, being a strategic emerging area in China’s capital market, with its characteristics of high volatility and high attention, provides a special scenario for studying behavioral finance strategies. Based on the momentum and reversal effects from behavioral finance theory, this paper conducts an empirical test on the performance differences between these two strategies, specifically for the A-share new energy sector. The research picks 50 representative new energy component stocks from 2020 to 2023 as the sample. Considering a one-way transaction cost of 0.1%, it builds and backtests a short-term reversal strategy (using past 5-day returns) and a medium-term momentum strategy (using past 20-day returns). The results show that the reversal strategy is significantly better than the momentum strategy and the buy-and-hold benchmark in terms of annualized return (18.7% vs 12.3%), Sharpe ratio (0.71 vs 0.43), and maximum drawdown control (-28.6% vs -35.2%). More analysis indicates that policy announcement events can cause market overreactions, thus creating noticeable profit windows for the reversal strategy, which can get a cumulative excess return of up to 4.1% within 5 days after policy release. Cost sensitivity tests reveal that transaction costs have a decisive effect on the strategy’s net returns. The conclusion of this paper suggests that in the new energy sector, dominated by retail investors and highly sensitive to policies, the reversal strategy has more practical value because it can effectively use the market’s short-term pricing biases, offering important strategy references for investors.
Research Progress on Different Types of Solar Cells
To address global challenges like fossil fuel shortages and climate change, developing clean and renewable solar energy is crucial for the world’s energy transition. Solar cells, which convert sunlight into electricity, are at the forefront of energy technology development. This study uses literature review and comparative analysis to assess the technical features and status of major solar cell types, including crystalline silicon and perovskite cells. Findings show that while crystalline silicon cells are mature and durable, they remain high cost. Perovskite cells, however, have achieved rapid efficiency gains (over 25% in labs), are low-cost, and can be made flexible. Recent progress in improving their stability has further enhanced their application prospects. By comparing efficiency, cost, and stability of different cells, this study helps clarify the current state of solar cell technology and offers guidance for future research and development. With continued efficiency gains and falling costs, solar cells are poised to play a key role in supporting sustainable development goals.
The Changing Trend of Precipitation Patterns along the Yellow Sea and Bohai Sea Coasts and Its Correlation with Global Warming
Anthropogenic climate change is exerting a strong influence on the water cycle, which presents a massive challenge to ecosystem and socio-economic stability, with densely populated regions especially susceptible to it. Although a lot of research has been done on the precipitation trends of the East Asian area, the relationship between early monsoon-affected Yellow and Bohai Sea coats with global warming and the trends of precipitation is still uncontrolled. To address this problem, this study compared the pattern of precipitation of the Liaodong Peninsula between 1982 and 2024. The analysis employed NASA POWER gridded precipitation data and the GISTEMP data of global mean temperature tapering data. NASA’s Goddard Institute of Space Studies manipulated such data and utilized two-sample T-tests, linear quotients, and Pearson correlations to examine monthly distinctions, extreme precipitation indexes, and how they follow global warming. Findings indicated that there were great changes in the way precipitation is distributed per month and more dramatic events of precipitation. Also, these extraordinary occurrences were positively associated with the warming of the world’s climate. This work demonstrates that global warming is increasing the intensity and concentration of summer monsoons in this area, and an increase in the odds of extreme rainfall could be valuable in localized information on climate adaptation and mitigation measures.
Op-Amp’s Application in SiC MOSFETs as a Short-Circuit Detector
Silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs) hold a significant position in high-power applications due to their excellent thermal and electrical performance. However, due to their small size, SiC MOSFETs are more susceptible to short-circuit (SC) events than their counterparts. Therefore, effective SC protection is crucial to prevent device failure under extreme conditions. With the growing demand for faster and more reliable protection systems, operational amplifiers (op-amps) play a key role in short-circuit detection systems for SiC MOSFETs. This article explores the role of op-amps in SiC MOSFET short-circuit detection and prevention systems. It will introduce several detection methods and their integration with gate drivers to improve response speed. Furthermore, this article will discuss voltage swing sensing, gate current sensing, and current derivative sensing. It will also examine the challenges of achieving consistent protection under varying conditions. To address these challenges, this article, through case studies and recent advances, aims to highlight the potential of op-amp-based solutions to improve the consistency and reliability of SiC MOSFETs, laying the foundation for more efficient power systems of the future.
Research on Prosthetic limb Design Driven by New Manufacturing Processes
Traditional prosthetic limb manufacturing has long relied on traditional processes such as subtractive manufacturing, which has inherent limitations such as low customization, high cost, and long production cycle. In recent years, new manufacturing processes such as additive manufacturing and composite material forming have provided disruptive solutions for personalized, lightweight and functional prosthetic design. This article aims to systematically study the comprehensive impact of new manufacturing processes on the functionality, economy and user experience of prosthetic limbs. Through literature review, the evolution of technology is reviewed. Through case analysis, advanced prosthetic design cases are studied. A comprehensive performance index evaluation system and a total cost of ownership (TCO) model are constructed to quantitatively compare the differences between traditional and new manufacturing processes. The research results show that the design based on 3D printing and composite materials can significantly reduce the weight of prosthetic limbs, enhance structural strength, shorten the customization cycle and lower the cost per piece. In addition, the user experience has also significantly improved in terms of aesthetics, comfort and psychological acceptance. This paper ultimately proposes an optimization framework for prosthetic limbs that integrates materials, design and manufacturing processes, providing a theoretical basis and practical guidance for the future research and development of high-performance and highly accessible prosthetic limbs.
Statistical Significance vs. Practical Significance: Analyzing the Roots of the Replicability Crisis in Modern Scientific Research
Modern scientific research is facing a severe crisis of reproducibility, with numerous published findings failing to replicate in subsequent independent verification, severely undermining the reliability of scientific knowledge. This paper systematically argues that the methodological imbalance between statistical significance and practical significance constitutes the core root cause of this crisis. We delve into how the misuse of p-values in current research practices—through p-value manipulation and selective reporting—spawns fragile findings. We also reveal the deep-seated impacts of academic incentive structures, publication bias, cognitive biases, and insufficient scientific literacy. Building on this analysis, we propose multidimensional solutions: at the research culture level, reforming incentive mechanisms to prioritize replicable studies and open science practices; and at the educational level, strengthening the cultivation of statistical thinking. The scientific community must achieve a paradigm shift from pursuing statistical significance to evaluating evidence strength and practical relevance, thereby building a more resilient and credible research ecosystem.
Core Applications and Techniques of RAG for Low-Resource Languages
Low-resource languages (LRLs) are spoken by billions of people globally, yet they continue to face limited access to reliable AI-driven tools, largely due to a lack of sufficient digital text resources. This review examines recent progress in Retrieval-Augmented Generation (RAG) that addresses the specific difficulties encountered in processing LRLs. By analyzing key studies, a range of optimization strategies are identified and analyzed across the RAG workflow—including data handling, retrieval techniques, and generation refinements. The discussion emphasizes how these methods tackle fundamental problems such as scarce data, weak model performance, and poor cultural alignment. The role of cross-lingual retrieval and knowledge distillation is also explored as a way to make AI systems more accessible and useful for speakers of low-resource languages. In addition to outlining RAG’s potential for reducing language-based digital inequality, this survey notes remaining obstacles and suggests productive avenues for further research. It is hoped that this structured summary of methods and use cases will support future efforts toward inclusive AI and the protection of linguistic diversity.
The Influence of F1 “porpoising” Phenomenon and Venturi Floor Design on the Performance of Racing Cars
Aerodynamics is the decisive factor for the performance of modern Formula 1 (F1) cars. Unlike ordinary cars which rely on mechanical grip, F1 racing cars achieve significant downforce through the coordinated operation of the floor, front wings and diffusers, thereby significantly increasing the speed of cornering.In 2022, the Fédération Internationale de l’Automobile (FIA) regulations underwent reform, reintroducing the concept of ground effect, which made the Venturi underbody the core of aerodynamic design. However, this extreme geometric design also leads to the “ porpoising “ phenomenon, where the racing car experiences periodic up-and-down vibrations due to the interruption of air flow at high speeds. This paper systematically analyzes the application of the Venturi effect in the base plate of F1 racing cars and its impact on downforce and drag through literature review, wind tunnel experiments and Computational Fluid Dynamics (CFD) simulation data. At the same time, it also explores the formation mechanism of the porpoising and its comprehensive influence on the stability of the racing car, the physical condition of the driver and the competition results. Case studies show that the Mercedes W13 frequently exhibits dolphin leaps due to the design of its base plate. In contrast, the Red Bull RB18 effectively mitigated this phenomenon through geometric optimization and material reinforcement. The conclusion of this study emphasizes that in the future development of F1 aerodynamics, a balance needs to be struck between pursuing extreme performance and maintaining stability.
Research on the Applications of Nanotechnology in Automobile Body Panels and Their Advantages
This article introduces the combination of nanotechnology and automobile car body panels. The growing demand of electric vehicles(EV) have emphasized the limitations of traditional material, such as limited battery lifecycles, overweight of body panels, and durability concerns. Nanotechnology can vary the physical, mechanical and chemical properties of the material, which brings more possibilities during the manufacture. This article focuses on metal based, carbon based, and polymer nanocomposites applied to EV body structures and paintings. Carbon nanotubes, graphene reinforced materials, and carbon fiber has greatly contributed to the strength-to-weight ratio and modulus, making car panels lighter and increase the driving range. Aluminum and stainless-steel based nanocomposites boost oxidation resistance and hardness. Polymer nanocomposites modified the paintings to increase scratch resistance, surface hardness and stain removability. Companies like BMW, Tesla has already applied such materials in use. While challenges still exist for the industry, the irreplaceable advantages of nanotechnology in optimizing EV body panels have been proven, and it undoubtedly has a bright and promising future in this field.
CVD Growth, Heterojunction Construction, and Functional Applications of Graphene
Graphene, a monolayer of sp²-hybridized carbon atoms, exhibits extraordinary electronic mobility (~200,000 cm²·V⁻¹·s⁻¹), thermal conductivity (~5000 W·m⁻¹·K⁻¹), and mechanical strength (~1 TPa), making it a core candidate for next-generation electronics, energy storage, and optoelectronics. Chemical Vapor Deposition (CVD) is the most reliable technique for high-quality graphene, enabling controllable synthesis of polycrystalline (single/ multi-layer) and single-crystal graphene. To address pristine graphene’s intrinsic zero-bandgap limitation and expand its functionality, graphene-based heterojunctions— with transition-metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), or other 2D Xenes—have been developed to tune its electronic and optical properties. This paper reviews graphene’s CVD growth mechanisms (polycrystalline/single-crystal), graphene heterojunctions’ construction/interface regulation, and their applications in the above fields. It confirms CVD’s key role in high-quality graphene synthesis and heterojunctions’ value in breaking graphene’s zero-bandgap constraint, while identifying challenges like large-area single-crystal growth and heterojunction interface defects; future research on atomic-level growth control and industrialization optimization will further promote graphene’s practical application.