Science and Technology of Engineering, Chemistry and Environmental Protection
ISSN 2959-6157(Print) ISSN 2960-1339(Online) Editor-in-chief ： Vinod A. Thale（Zhejiang University） Associate Editor： Syed Murawat Abbas Naqvi（Beijing Institute of Technology） Editorial Board Member： Faisal Rafique（Southern University of Science and Technology） Jian Hu（University of Kent） Science and Technology of Engineering, Chemistry and Environmental Protection which provides a platform for researchers, scholars, and professionals to share their latest findings and insights in the fields of engineering, chemistry, and environmental protection. One of the key reasons why people should choose Science and Technology of Engineering, Chemistry and Environmental Protection as their preferred journal is its multidisciplinary focus. The journal covers a wide range of topics in the fields of engineering, chemistry, and environmental protection, including but not limited to materials science, environmental engineering, chemical synthesis, renewable energy, and pollution control. This breadth of coverage ensures that readers are exposed to the latest and most relevant research in various interdisciplinary areas. The journal is committed to promoting research that has real-world applications and can inform industry and policy-making. As such, readers can expect to find articles that not only contribute to theoretical knowledge but also have practical implications for professionals in these fields. Introducing the chief editor of IEEE will help journals enhance their influence and introduce the most authoritative audit requirements. All articles will be submitted to Google Scholar, Crossref, U.S. Librarian Database, ProQuest(part of Clarivate’s Web of Science), Ulrich's Periodicals Directory, SEMANTIC SCHOLAR, OpenAlex.
The Methods and Mechanisms of Preserving Content and Structure in Image Style Transfer
As artificial intelligence (AI) technology continues to advance, image style transfer has gradually moved from research to practical applications, becoming widely integrated into fields such as art creation, advertising design, and virtual reality. However, existing research often struggles to maintain the fidelity of the original image's content and structure while pursuing stylistic effects. This contradiction has become a key bottleneck restricting the further development of image style transfer technology. With advances in image processing, a key challenge is preserving the original image's structure and content while achieving a stylized effect. This paper reviews and analyzes the design principles and underlying mechanisms of various AI models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Transformer architectures, in addressing the challenge of content preservation. Through the integration of existing literature, the results indicate that there is currently no universal solution, and trade-offs and choices need to be made for specific scenarios. Based on the limitations of existing methods, this paper explores potential future advancements in the technology, aiming to offer valuable insights for further development.
Copyright (c) 2026 Science and Technology of Engineering, Chemistry and Environmental Protection
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.
Application of Tolerance Analysis in Wing Design
With the increasing demand of modern aircraft for high lift ratio, low fuel consumption and high reliability, the geometric accuracy control of wings in the design and manufacturing stages has become particularly important. Based on this background, this study conducts a review and qualitative analysis and explores how geometric deviation can affect the performance of the wing through the aerodynamic mechanism. By analyzing the velocity field, pressure field, streamlined structure and regional average performance indicators, the study further reveals the typical pneumatic problems that may lead to geometric deviation. The research results indicate that the front edge shape error and surface smoothness are the most sensitive tolerance types, which are enough to cause significant changes in the lift coefficient C_L and drag coefficient C_ D even within the range of conventional manufacturing standards. In general, simulation data demonstrate that accurate tolerance analysis and surface quality control can effectively solve the instability of pneumatic performance, improve the overall aerodynamic reliability of the wing, and provide direction for future manufacturing process optimization.
Development and Outlook of Extr eme Ultraviolet (EUV) Lithography Technology
Extreme ultraviolet (EUV) lithography has emerged as a core approach for expanding semiconductor fabrication beyond the boundaries of deep ultraviolet (DUV) patterning. Due to the ever-diminishing reduction of device size, even utilizing multiple patterning techniques does not address the basic physical issue of optical resolution of conventional 193 nm lithography. This study introduces the concepts of photolithography, the technological revolution from DUV to EUV, and the architectural characteristics of current EUV exposure systems. It addresses additional issues, which include but are not limited to limited source power, mask defects, resist stochastics, multilayer mirror contamination, and pellicle transmission loss. The current research was supplemented with industry roadmaps, and it is concluded in the paper that the future scalability of EUV lithography will be dependent on the use of highnumerical-aperture (High-NA) exposure devices, novel photoresist materials (including metal-oxide resists), and improvements to computational lithography to minimize stochasticity and improve the accuracy of the pattern. This study emphasizes the ongoing relevance of optical lithography for the scalability of semiconductor devices and sheds light on the technical directions needed in order to realise semiconductor units at sub-2 nm.
AI as a Technology Bridge for Families: Practical Pathways and Challenges
Traditional AI focused on embedding human thinking to achieve intelligence, while modern AI prioritizes acquiring knowledge from data and making independent judgments. It processes data from daily life and generates outputs to realize intelligent applications. Currently, with the national policy promoting the Internet of Everything and fostering an intelligent product ecosystem, household AI has already occupied a partial position in families and brought significant convenience. This paper explores the specific paths such as technology implementation, product ecosystem construction, and user adaptation, supplemented by specific cases. Additionally, the paper analyzes potential challenges including intergenerational cognitive differences, data privacy risks, economic inequality, and dependence issues. The research aims to provide reference for accelerating the practical implementation of household AI and balancing technological development with risk mitigation.
Control Method of DC Motor Based on PID Control
DC motor control has long been a fundamental topic in industrial automation, robotics, and embedded systems. Among numerous control strategies, PID control remains the most widely applied approach due to its simple structure, intuitive tuning process, and strong engineering practicality. However, with increasing system complexity and diverse application scenarios, traditional PID methods face challenges such as parameter sensitivity, limited robustness, and difficulties in dealing with disturbances and nonlinearities. This paper provides a comprehensive review of PID-based control methods for DC motors. The survey first introduces the characteristics of DC motor models and summarizes the basic principles of PID control. It then categorizes existing research into several groups, including classical tuning techniques, improved PID structures, adaptive and intelligent PID algorithms, observerassisted PID approaches, and PID implementations within embedded platforms. For each category, representative methods, performance characteristics, advantages, and limitations are systematically analyzed. Furthermore, this review discusses key engineering challenges and identifies research gaps that remain insufficiently addressed in current literature. Overall, the paper aims to present an organized understanding of PID-based DC motor control development and to highlight future research trends in practical and intelligent control strategies.
Plant Roots and Ecosystem Stability: A Structured Review
Plant roots are multifunctional organs that not only anchor plants but also regulate water and nutrient flow, construct soil structure, and influence biogeochemical cycles. Root characteristics include systemic structure (such as taproot, fibrous roots, and basal roots), tissue anatomy (like epidermis, cortex, endodermis, and xylem/phloem), and rhizosphere processes (such as root hairs, air cavities, exudates, and mycorrhizae). These characteristics affect ecosystem functions, including water redistribution, soil aggregate formation, carbon sequestration, and resilience to disturbances. However, the complexities of the underground and methodological differences have historically made generalization difficult. This paper explores the impacts of root morphology and function on ecosystem stability, including root classification, specialized structure, and the role of roots in processes such as water and nutrient uptake, plant-microbe interactions, soil structure, and carbon cycling. And it also discusses the contributions of roots to ecological stability and resilience, such as controlling erosion, mitigating drought and nutrient changes, and promoting recovery, while identifying current challenges and methodological needs. The results indicate that root morphological diversity and functional differentiation significantly influenced water redistribution, soil aggregation, and carbon sequestration. Rhizosphere microorganisms, through interactions with roots, promoted nutrient acquisition and enhanced ecosystem stability. Roots also played a crucial role in preventing soil erosion and mitigating drought.
A Literature Review of Agile Development Methods in the Past Decade: Evolutionary Paths, Core Controversies, and Future Trends
Nowadays, agile methodology has emerged as a core and indispensable framework for project execution across diverse industries. Beyond mere process optimization, it is a people-centric, adaptive approach that relies on continuous feedback loops and incremental delivery cycles to enable teams to respond swiftly and effectively to evolving market demands. It also consistently delivers high-quality software products that create tangible value for customers. Through systematic literature analysis and in-depth comparative research, this study systematically delineates the evolutionary trajectory of agile methodology over the past decade. It traces the methodology’s expansion from initial cross-role collaboration within small teams to enterprise-wide adoption across entire organizational ecosystems. The study further identifies core controversies in its practical implementation, clarifies context-specific applicable scenarios, and offers forwardlooking projections of its future development trends. This study reveals that agile methodology has undergone a distinct evolution over the past decade—shifting from cross-role collaboration (2014–2018) to enterprise-wide extension (2019–2024)—with core controversies centered on cross-scenario adaptability, integration with emerging technologies, and ethical risks, while its future will move toward in-depth AI integration and cross-industry fulldomain adaptability, and enterprises need to balance agility and structure for effective agile transformation.
Research on the Evaluation Method of Pilot Workload in High Load Scenarios during Aircraft Climb Phase
To address the challenge of accurate evaluation of pilot workload during aircraft climb phase, an innovative evaluation method combining subjective and objective factors was proposed in this paper. Three high load scenarios were defined by systematic analysis of the climb phase mission. In terms of research methods, the AWDS subjective scale is optimized, and the degree problem and closed loop logic verification are used to improve data reliability. Meanwhile , eye movements and heart rates were collected with non-contact instruments, with precise calibration achieved by establishing individual physiological baselines. Experimental validation demonstrated a significant correlation between subjective workload scores and objective indicators, such as pupil diameter change rate and blinking frequency change rate, indicating high consistency between subjective and objective data.. The final comprehensive evaluation model provides an effective solution for real-time monitoring of pilot workload and cockpit human-computer interaction optimization.
The Study of Policy Coordination Mechanisms and Industry-Regional Emission Reduction Pathways in China’s Carbon Peak Process
China’s proposed goal of achieving carbon peaking before 2030 incorporates carbon peaking and carbon neutrality into a major national strategy. To achieve this target, it is crucial to create an effective policy coordination mechanism and enhance the overall effectiveness of emission reduction efforts. Given its complexity, carbon peaking is not an isolated task but a systemic project that involves the comprehensive transformation of the economy and society. As such, this paper takes the construction of China’s policy system in the carbon peaking process as its research topic, exploring the differentiated paths, implementation strategies, and collaborative governance mechanisms across regions and industries. Through a combination of literature review, policy text analysis, and case studies, the study draws on national and local policy documents, statistical data, and practical experience from representative regions. The results indicate that China’s carbon peaking policy system is gradually evolving from a single-dimensional control approach to one of systematic coordination. However, challenges persist in areas such as regional coordination, industrial transformation, and policy alignment. This study argues that future efforts should further enhance the synergy framework, strengthen local capabilities, and optimize market mechanisms. These measures will improve overall energy efficiency and offer feasible pathways, along with theoretical support, to help China meet its carbon peaking goal on time.
A Review of Multimodal Sensor Technologies and Fusion Methods for Intelligent Robots
With the rapid development of artificial intelligence and robotics, intelligent robots are gradually transitioning from structured environments to open and complex scenarios, posing unprecedented requirements for the depth, breadth, and precision of environmental perception. Single-modal sensors can no longer satisfy the requirements of complex tasks, and multimodal sensor fusion technology has become a key approach to enhance the robot’s environmental perception, state estimation, and decision-making capabilities. This paper presents a systematic review of multimodal sensing technologies and fusion methodologies for intelligent robots. First, the paper outlines the principles and advances of various core sensors; then it delves into key technologies from signal preprocessing to fusion algorithms spanning classical filtering and deep learning; and it synthesizes their application performance in typical scenarios such as navigation, operation, and human-robot collaboration. Finally, confronting current challenges, the paper envisions the future trends of sensing technologies towards intelligence, flexibility, and chipbased development, aiming to offer insights for researchers in the field.
CMUeXt network model: Analysis and Verification of Insufficient Segmentation Performance on CT and MRI Images
Multimodal medical image segmentation is a critical auxiliary technique for clinical diagnosis, but it still confronts notable challenges in addressing noise interference, boundary blurring, and complex anatomical structures in CT, MRI, and other images. This article systematically evaluates the practical performance of the lightweight network CMUeXt, which integrates large convolution kernel and skip fusion module in CT and MRI segmentation tasks. Experiments are conducted on an NVIDIA RTX 4090 GPU using public datasets: CT (lung/liver), MRI (brain tumor), and mixed-modal datasets. The evaluation adopts Intersection over Union (IoU) and F1-score as core metrics, supplemented by visual validation. Results demonstrate significant limitations of CMUeXt: weak boundary perception ability, ambiguous probability maps, and poor cross-modal generalization. Its large-kernel design and skip fusion module fail to adapt to inherent differences in multimodal data. Future optimizations should integrate dynamic convolution, attention mechanisms, and domain adaptation to enhance its practical clinical applicability.
Analysis of AI-Enhanced Next-Generation Wireless Communication Systems
The transition from 5G to 6G in mobile communication technology presents challenges, including achieving ultrahigh spectral efficiency, minimizing latency, and supporting large-scale connectivity. In dynamic and complex wireless settings, traditional model-driven methods are approaching their performance limits. Based on existing literature, this paper analyzes the core technical pathways through which artificial intelligence and machine learning can enhance 6G communications. The results indicate that, at the physical layer, data-driven deep learning models achieve accuracy levels comparable to or exceeding those of traditional methods in channel estimation and signal detection tasks, while simultaneously reducing pilot overhead. Meanwhile, reinforcement learning enables real-time intelligent optimization of massive multiple-input multipleoutput (MIMO) beam management, increasing spectral efficiency by 15-30%. At the network layer, federated learning serves as a distributed intelligence framework that safeguards data privacy. Its collaborative optimization approach improves model convergence, accelerating the process to twice the rate of conventional approaches. In addition, the semantic communication paradigm focuses on transmitting the meaning of information instead of raw bits, which reduces data volume by more than 80% in bandwidth-limited scenarios, thus boosting communication efficiency. The study further suggests that future networks will be AI-native, with AI deeply integrated into network architectures and protocols. This shift will transform the network’s role from a passive tool to an active AI-enabled service. poor model interpretability and high computational complexity, AI-driven enhancement remains a key pathway to advancing 6G performance.
The Comparison of Alignment Fine-Tuning Methods for Large Language Models on Honesty, Helpfulness, and Harmlessness
The alignment of large language models (LLMs) with human values of Honesty, Helpfulness, and Harmlessness (HHH) is a critical issue for their safe deployment. However, a comparative analysis across these three dimensions remains lacking for primary alignment approaches such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO). This paper aims to conduct a comparative study of the performance of these alignment methods on the HHH dimension through a review and synthesis of relevant research literature. The results indicate that there are performance trade-offs between the different alignment algorithms. In particular, SFT is the foundation of the alignment process, but is generally outperformed by preference-based methods. RLHF excels in usefulness and harmlessness, but involves greater implementation complexity. DPO matches RLHF in terms of usefulness in a cleaner and more efficient way and has an advantage in honesty, but is more sensitive to dataset quality and slightly weaker in harmlessness.
Application of the Golden Ratio in Architectural Design and Structural Optimization
In modern architectural design, architects need to pay attention to a space’s visual appeal while maintaining its structural soundness and practical functionality. In practice, the balance between visual appeal and structural integrity under the limitations of space and materials remains a significant challenge. In this context, the golden ratio, a time-honored mathematical principle, has found extensive application in architectural design because of its distinctive proportional characteristics. Thus, this study explores the application of the golden ratio in architectural design and its role in structural optimization. Through a review of the literature and case analyses, it investigates historical architectural examples and the strategies for applying the golden ratio in contemporary design. The results indicate that the proper use of the golden ratio can enhance the visual balance of spatial layouts while also improving structural proportions and load distribution. However, real-world applications are still limited by material costs and design practicality.
Evolution and Challenges of Natural Language Processing Technologies Based on Text Understanding and Generation
Natural Language Processing (NLP) began in 1950, initially relying on rules established by linguists to parse text, but this approach proved insufficient for handling complex linguistic phenomena. With the development of technology, machine learning methods have been introduced to improve processing efficiency and accuracy through data-driven approaches. Today, neural networks and pre-trained models, such as BERT and GPT, have become mainstream. With their powerful data learning capabilities and deep semantic understanding, they have greatly expanded the application boundaries of natural language processing, showing unprecedented performance in everything from machine translation to text generation. This paper systematically reviews the development of natural language processing technology, analyzes the evolution from early rule-based methods to modern neural networks and pre-trained models (such as RNN, Transformer, BERT, GPT, etc.), and discusses the current status and problems of these technologies in text understanding, generation and cross-modal applications. The findings indicate that although natural language processing has shifted from “understanding” to “generation” and has gradually achieved “crossmodal intelligence,” problems such as model illusion, bias propagation, high energy consumption, poor interpretability, and data quality constraints remain current bottlenecks. Future research should focus on improving the reliability and security of models, optimizing resource utilization, designing interpretable artificial intelligence systems, and exploring knowledge enhancement and dynamic update mechanisms.
Design of an Embedded Lightweight System for Traffic Signal Detection in Adverse Weather and at Night Based on YOLOXNano
With the rapid advancement of intelligent transportation systems (ITS) and autonomous driving technologies, the robustness of traffic signal detection under complex meteorological and nighttime conditions has emerged as a critical concern. To address challenges such as diminished recognition accuracy in adverse weather and low-light conditions, as well as deployment constraints on edge devices, this paper proposes an embedded lightweight traffic signal detection system based on YOLOX-Nano. The system enhances environmental adaptability via a diversified meteorological scene data augmentation strategy, while achieving model lightweighting through network pruning and mixed-precision quantization, effectively balancing detection accuracy, real-time performance, and resource efficiency within the constraints of limited hardware resources [1]. During the deployment phase, the system utilizes ONNX and TensorRT to establish an efficient inference toolchain, enabling cross-framework model conversion and acceleration optimization, ultimately achieving low-power, high-performance real-time traffic signal detection on the Jetson Nano platform. The primary focus of this paper is on the system architecture design and its implementation pathway, aiming to provide an embedded lightweight solution with engineering feasibility and scalability for traffic signal detection under complex meteorological conditions.
Bio-genetic Engineering: Breakthroughs, Challenges, and Future Prospects
Bio-genetic engineering, which enables the precise modification of biological traits through targeted manipulation of genetic material, stands as a pivotal component in the life sciences. This paper focuses on three aspects: the current applications, challenges, and strategies of bio-genetic engineering. It highlights innovative applications in medicine and agriculture, such as insulin production, biopharmaceuticals, genetically modified crops, and precision breeding. Despite significant achievements, the field still faces challenges related to precision, efficiency, social ethics, and regulatory frameworks. To address these issues, this study puts forward systematic and innovative strategies in three key areas: technological innovation and precision enhancement, ethical governance and public engagement, and patent sharing and regulatory coordination. These strategies aim to guide bio-genetic engineering toward greater societal benefits.
The Application of Artificial Intelligence in Robotic Control
The pursuit of automated robotics took shape in the mid-20th century. While industrial robots relied on fixed programs to perform repetitive tasks in structured environments, they faced significant limitations in adaptability and flexibility. By the early 21st century, technological advancements enabled robots to make basic decisions, though complex tasks still required human programming. Following the AI revolution, robots now possess autonomous learning and decision-making capabilities through AI algorithms, transitioning from rulebased to intelligence-driven systems. Current challenges in robotic control primarily involve three aspects: enhancing environmental adaptability in unstructured settings to improve interference resistance, and optimizing real-time coordination efficiency for complex tasks. This paper analyzes the technical principles, practical applications, and countermeasures to address these challenges. It explores how AI can reshape robotic control. Research conclusions indicate that AI holds vast potential in robotic control. Through continuous optimization of technical pathways and resolution of existing issues, comprehensive intelligent upgrades in robotic control are achievable, delivering more efficient and precise automation solutions across industries.
Recycled Aluminum Powder 3D Printing Technology and Its Application in Automotive Components
The automotive industry is increasingly moving toward lighter and more sustainable manufacturing practices to improve fuel efficiency and limit environmental impact. Aluminum alloys play a vital role in this transformation due to their superior strength-to-weight ratio and recyclability. In recent years, powder-based additive manufacturing (3D printing) has emerged as a key technology for producing complex automotive components. However, the high cost and limited availability of primary aluminum powder restrict its large-scale application. This paper reviews the research progress and applications of recycled aluminum powder in 3D printing of automotive components. It systematically summarizes its sources, preparation processes, and particle properties, and further analyzes its applicability in additive manufacturing processes such as Selective Laser Melting (SLM) and Directed Energy Deposition (DED). Additionally, comparisons are made between components fabricated from recycled powder and virgin powder regarding their mechanical properties, thermal performance, and corrosion resistance. Key challenges were identified, including impurity control, process stability, and quality inspection. The results demonstrate that proper powder recycling and processing enable recycled aluminum powder to replace virgin powder in certain automotive components, thereby achieving cost reduction and enhanced material utilization efficiency while offering a potential solution for sustainable automotive manufacturing.
The Impact of Thermal Management Technology on Engine Performance
With the continuous development of the automotive industry and the increasing stringency of emission regulations, engines are required to achieve high efficiency, low fuel consumption, and low emissions simultaneously, which places higher demands on the thermal management system. As a core approach for regulating engine temperature fields and energy flows, thermal management technology directly affects the engine’s thermal efficiency, durability, and emission levels. Through a literature review, this paper systematically analyzes the mechanisms by which thermal management technologies influence engine performance. It further discusses the applications of advanced cooling systems, waste heat recovery, nanomaterials, intelligent materials, and thermal management in new energy vehicles. The results indicate that advanced thermal management technologies can significantly enhance engine efficiency, extend service life, and reduce environmental impact. However, current systems still face challenges such as high complexity, increased costs, and limited material reliability. Future trends point toward intelligent control, multifunctional material applications, and deeper integration with new energy vehicle thermal management. This study provides valuable references for researchers and engineers working on energy optimization and engine performance improvement.
A Review of Physical Vapor Deposition of Two Dimensional Materials
As the semiconductor industry demands higher chip integration, Moore’s Law is increasingly constrained by physical limits of chip miniaturization and quantum tunneling effects. Traditional semiconductor materials can no longer meet the performance requirements of next-generation electronic devices. Two-dimensional materials, with their atomic-level thickness and exceptional physicochemical properties, have emerged as a critical breakthrough for overcoming these challenges. Physical vapor deposition (PVD), a core technology for synthesizing two-dimensional materials, offers advantages such as controllable deposition processes, high-fidelity thin film purity, and excellent compatibility with existing semiconductor processes, making it essential for large-scale production. This paper reviews common PVD methods, including thermal evaporation, electron beam evaporation, and sputtering (DC sputtering, RF sputtering, and magnetron sputtering), analyzes their pros and cons, and discusses innovations like contactless heating and pulse techniques. It also explores verified applications of PVD in cutting-edge research on two-dimensional materials such as graphene and TMDs. This review provides valuable references for optimizing PVD processes and improving equipment in two-dimensional material synthesis, while offering insights for future applications in semiconductor devices and optoelectronic detection.
Application and Development of Blockchain-Based Distributed Energy Trading Systems in the Power Market
The global energy transition has accelerated the large-scale adoption of distributed renewable energy sources such as photovoltaics and wind power. However, their “decentralized and volatile” characteristics make traditional centralized power trading models difficult to adapt. The inherent features of blockchain technology—decentralization, immutability, transparency, traceability, and smart contracts—offer a technological foundation to resolve these issues. Although several pilot projects have been implemented globally, a mature system solution and large-scale promotion model have yet to be established. Current trading systems rely heavily on intermediaries, leading to inefficiency, trust deficits, and high intermediary and labor costs. Moreover, challenges such as data silos and dispatch difficulties persist. Domestically, shortcomings remain in cross-regional interconnection, regulatory integration, and cost control, while a compliant yet decentralized system model and multidimensional evaluation framework are still lacking. This study constructs a “regulatory-node-embedded” consortium blockchain five-layer architecture (application, contract, consensus, network, and data layers) with four core functional modules, employing the PBFT algorithm (TPS ≥ 150) and achieving transaction latency ≤ 5 seconds. Empirical results show a cost reduction of 20–30%, photovoltaic absorption rate improvement from 75% to 92%, and peak-valley gap narrowing by 18%. Blockchain technology can effectively address core pain points, and the consortium blockchain is identified as the optimal configuration. System promotion requires coordination among policy, technology, and market sectors, while existing issues—throughput, interface, and audit cost—can be optimized through sharding technology. This study fills the research gap in systematic “Blockchain + Energy Trading,” proposing a compliant model and three-dimensional evaluation framework that provides a practical solution for power market reform, enhances the participation of small and medium entities, improves energy consumption rates, and contributes to carbon neutrality goals.
Impact of Linear and Nonlinear Robust Control on Robust Stability
Linear robust control methods, like H-infinity and μ-synthesis, offer clear ways to design controllers. They help control systems stay stable even with unknown factors and outside shaking. These methods guarantee the system stays stable and performs well under changing conditions. Besides, they are used a lot in industrial systems that are well-modeled. In addition to these, nonlinear robust control techniques, like sliding mode control, adaptive robust control, and robust backstepping, handle nonlinear forces well. And they also adjust easily to unknown factors that change over time. Thus, this paper compares how linear and nonlinear robust control methods differ in staying stable, how hard they are to design, and how well they block outside shaking. By looking at key theories and existing study findings, this work shows how the system type, the kind of unknown factors, and the use case change the selection between linear and nonlinear robust controllers. Moreover, it discusses mixed methods that combine linear and nonlinear ways to achieve improved stability in complex engineering systems.
Exploration of Synthesis Pathways for Multifunctional Nanobiomaterials Based on Biomedical Demands
Recent progress in multifunctional nanobiomaterials has paved novel avenues for precision medicine. These materials enable the integration of targeted delivery, imaging, and therapeutic functionalities within a single system. They include chemical ways like coordination self-assembly and polymerization, biomimetic methods such as cell membrane coating and biotemplate synthesis, and nucleic acid scaffold methods like DNA origami. We evaluate the performance of these methods in physiological environments. For example, carbon nanotubes with branched PEG have a longer blood half-life of about 22.1 hours, while those without PEG last only 5.4 hours. Also, nanocarriers coated with red blood cell membranes exhibit robust immune evasion capabilities in animal models. We discuss how cross-linking, multilayer coating, and dynamic shielding can improve biocompatibility, and how to design materials that respond to outside signals. At the end, we look at problems like reproducibility, scale-up, and clinical translation, and suggest future directions. This review aims to provide a comprehensive overview for the rational design of clinically applicable nanobiomaterials.
An Application Study of Data Science in Enhancing Time Management and Learning Efficiency Among High School Students
This study explores the application of data science methods in optimizing time management and enhancing learning efficiency among high school students. Against the backdrop of the “Double Reduction” policy and concurrent academic anxiety, high school students urgently need to transition from a “time-based” to an “efficiencybased” learning paradigm. Inspired by “Quantified Self” and “Precision Learning” theories, this research attempts to construct a data-driven time management diagnostic and optimization model tailored for individual high school students. The author conducted a four-week data tracking study on themselves, collecting multidimensional data including study duration, subject distribution, sleep time, screen usage time, and subjective efficiency ratings. Utilizing Excel tools for data cleaning, descriptive statistics, and visualization analysis, patterns and potential issues within personal learning habits were identified, such as peak efficiency during specific time periods and the positive correlation between sleep and efficiency. Ultimately, personalized time management optimization strategies were developed based on these data insights. This study demonstrates the feasibility and effectiveness of the “quantified self” concept for high school students in self-awareness and self-optimization, proving the immense potential of data science as a “personal learning advisor” in enhancing students’ self-awareness and self-regulation capabilities.
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.
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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.