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.
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.
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.
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.
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.
Multi-factor Interactions in Global New Energy Policies: Comparative Practices, Impacts, and Optimization Paths
Against the backdrop of the global energy transition and worsening environmental issues, new energy has become the key to addressing energy and environmental dilemmas. This article systematically reviews the development paths and policy practices in the new energy sector of representative countries such as Thailand, France, the United States, and Germany, and analyzes policy impacts from three dimensions: public participation, environmental protection, and urban sustainable development. The research finds that each country has formed a differentiated development model based on its resource endowment. Policies have promoted industrial upgrading through legal guarantees and economic incentives, but challenges exist, such as uneven cost sharing, insufficient policy targeting, lack of stability, and poor interest coordination. Data shows that new energy policies have effectively raised public environmental awareness, reduced carbon emissions and pollutant concentrations, optimized urban energy structures, and facilitated economic transformation. Based on this, the article proposes suggestions such as reasonable cost sharing, precise policy optimization, maintaining stability and consistency, strengthening multi-party collaboration, and international cooperation, to provide theoretical and practical references for improving the new energy policy system and promoting global energy transition.
Development and Application Prospects of 5G Communication Technology
The rapid development of information and communication technologies has significantly reshaped economic and social structures, with 5G emerging as a key driver of digital transformation. This paper examines the evolutionary trajectory and application prospects of 5G communication technology, shedding light on its technical underpinnings, challenges, and potential solutions. The study addresses the research problem of how 5G can balance performance improvements with limitations in spectrum resources, network security, and energy consumption. By analyzing core technologies such as massive MIMO, millimeter-wave transmission, and network slicing, this paper demonstrates how 5G supports applications across smart cities, remote healthcare, industrial automation, and autonomous driving. Case studies drawn from China’s 5G commercial deployment further illustrate the transformative impact of this technology. The conclusion emphasizes that while 5G accelerates digitalization and intelligent connectivity, future research must focus on sustainable, secure, and efficient pathways toward 6G development.
A Study of Carbon Colonialism and Inequality in the Global South Based on Ecocritical Theory
In response to the accelerating impacts of global climate change and the realities of the Anthropocene, nationstates have implemented international agreements such as the Kyoto Protocol and the Paris Agreement to mitigate climate change and curb environmental degradation stemming from excessive carbon emissions. Through carbon market operations, these nation-states aim to achieve carbon neutrality and uphold the integrity of the Earth’s ecological system. Nevertheless, the nations of the Global South are enduring some of the harshest effects of environmental degradation, despite their limited historical responsibility for greenhouse gas emissions. This inequality is further exacerbated within climate change response strategies, where developed nations displace the costs of emissions reduction and ecological crises onto developing countries via policy mechanisms, industrial transfers, and technological restrictions, thus maintaining inequality. Based on a literature review and case studies, this paper employs an ecocritical framework to investigate how carbon colonialism shapes the use and management of environmental resources in the Global South through political-economic structures. Besides, it further explores the underpinning power dynamics and the logic of capital accumulation. The results reveal that carbon colonialism shapes the dynamics of traditional colonialism and North-South inequalities through climate politics, carbon markets, and green technology. In addition, a political-economic approach examines global environment and guides systemic change for justice and sustainability.
Force Feedback and Visual Fusion in Minimally Invasive Surgical Robots
The precise operation of minimally invasive surgical (MIS) robots relies on multi-dimensional perception of the surgical scene, and the fusion of force feedback (tactile perception) and visual perception is the core technology to break the bottleneck of the “perceptiondecision- control” loop. This paper systematically reviews the development of force-visual fusion technology for MIS robots, analyzing from sensor layer innovation, and algorithm layer breakthroughs, to system layer integration. It examines technical adaptability and performance bottlenecks by combining typical clinical scenarios such as tumor resection, vascular suturing and nerve dissection. Finally, addressing key issues including poor generalization in in-vivo environments, difficulties in multi-modal spatiotemporal synchronization, and lack of clinical translation standards, the paper proposes future development paths based on “surgical metaverse,” “hybrid dataset training,” and tele-surgery specifications, providing references for technology R&D and clinical translation in the interdisciplinary field of medicine and engineering.
Multivariate Modeling and Feature Attribution in Consumer Product Ratings: A Case Study on Nutritional Profiles of Breakfast Cereals
Generally speaking, consumer product ratings play a central role in shaping food choices, yet the relationship between nutritional attributes and perceived product quality remains under-quantified. This paper explores how nutritional components—such as sugar, fiber, protein, and calories—influence consumer ratings through a literature review and empirical case study of breakfast cereals, while examining how interpretable predictive models reveal the most critical drivers. Using a publicly available cereal dataset encompassing approximately 77–80 products (with detailed nutritional content and rating variables), this study integrates descriptive statistics, linear regression, regularized regression, and ensemble tree models. We employ both model-based and model-agnostic feature attribution methods. Our findings reveal that linear models explain nearly all out-of-sample rating variance, indicating a near-deterministic mapping between nutritional components and ratings. While random forest models demonstrate robust performance, their accuracy remains relatively low. Cross-model analysis identifies sugar (negative impact) as the most critical variable, followed by calories and sodium (negative), and dietary fiber (positive). Finally, we translated these insights into health-oriented recommendation strategies, prioritizing products with low sugar, high fiber/protein, and moderate calories. Under these constraints, we generated the top 10 predicted-score cereal products. Our findings support current guidelines limiting free/added sugar intake and increasing dietary fiber, illustrating how explainable modeling informs healthier product recommendations and formulation improvements.
Analysis of the Application of Atoms in Nuclear Fission and Fusion
This paper delves deeply into the application of atoms in nuclear fission and nuclear fusion. Nuclear fission and nuclear fusion, respectively originated from the exploration and utilization of the energy mysteries of atomic nuclei. Nuclear fission is often used in military applications and civilian nuclear power plants, while nuclear fusion began with the theoretical exploration of the energy sources of stars. Due to the high difficulty of controllable and drastic changes, it is still in the process of exploration and development. First, the basic principles of nuclear fission and fusion are expounded. Then, the applications of nuclear fission in energy, military, medicine and other fields are introduced in detail, as well as the potential applications and challenges of nuclear fusion in the energy field. By comparing the characteristics of the two in terms of energy utilization, safety, waste treatment, etc., the importance and application prospects of atoms in these two reactions are demonstrated, providing references for related fields and decision-making.
Application of Energy Storage Systems in Renewable Energy
The global energy transition is accelerating, with renewable energy becoming the primary driver of power capacity expansion. However, the intermittent and volatile nature of solar and wind energy poses challenges to grid stability. Energy storage systems are key to addressing these challenges. This paper systematically examines the application of energy storage systems in renewable energy, covering their mechanisms, technical classifications, typical application cases, and future development directions. Combining these with photovoltaic, wind, and hydropower applications, it analyzes their value in smoothing power output, increasing renewable energy consumption, and enhancing grid peak and frequency regulation and reliability. The study finds that energy storage systems can control photovoltaic output fluctuations to ±5%, achieve 100% compliance for wind power primary frequency regulation, and increase renewable energy consumption by 15%–35%. Case studies demonstrate the economic viability and feasibility of energy storage in various scenarios. While current challenges remain, such as high costs, efficiency limitations, and inadequate recycling systems, future development directions include innovations in solidstate and flow batteries, hybrid energy storage integration, digital twins and intelligent operations and maintenance, and improved market and policy mechanisms. The paper argues that energy storage systems will become a core supportive force in promoting high-proportion renewable energy integration and achieving the “dual carbon” goals.
photovoltaic and wind power applications
Research Trends in Human-AI Collaboration: A Five-Year Density Analysis Based on Domain Categorization
Human-AI teamwork is rapidly becoming one of the most energetic and promising frontiers in artificial intelligence. It drives a wide array of applications, from intelligent virtual assistants to collaborative creative platforms and interactive dialogue systems. This research offers a datadriven perspective on how this field has progressed over the past five years. By analyzing comprehensive reports such as the AI Research Progress Report (2020–2024) and using a specially developed keyword index for subfield classification, we identified which domains are expanding the fastest and where most research efforts are concentrated. This research integrates publication data, growth metrics, and comparative analysis across various subfields, employing visual tools like charts and linear regression models—to determine emerging hotspots and developing trends. Notably, dialogue generation systems and cooperative AI agents consistently produce the highest volume of research, while innovative AI-powered creative tools are experiencing swift growth . These findings not only provide a snapshot of the current environment in Human-AI collaboration but also suggest focal points for future breakthroughs.
Fundamentals of Signal Processing, Characteristics of Common Signals, and Applications
Signal processing serves as a fundamental technology for extracting information and optimizing systems in many application areas that include communications, radar and medical imaging. However, on the one hand, the research is not very deeply concerned with diversified application of multi-type signal characteristics, and on the other hand the adaptability of Fourier transform in complex environment or scene is not studied enough. Signal classification, including the properties of signals, Fourier Transform, and its applications are the subject of this paper, based on a mixture of theory and example cases. This paper finds that various signals (e.g., 1 5 continuous/discrete, periodic/ non401 6 periodic) have different processing strategies, and Fourier Transform is capable of effectively transforming signals from time to frequency domain, it is reliable for representing information, and extracting information. This work provides theoretical proofs for cross-domain application in signal processing.
From Traditional Lenses to Superlenses: a Revolution in Imaging Technology
Since ancient times, lenses have been a crucial tool in human history. They were used in telescopes during navigation to determine the surrounding environment; in biology, lenses were incorporated into microscopes to magnify details invisible to the human eye. However, due to their design and structural characteristics, traditional optical lenses have inherent drawbacks, resulting in physical bottlenecks in their development. These include insufficient image detail, severe detail loss, and excessively large and heavy lens assemblies. In the 21st century, with the intersection of optical physics and nanotechnology, scientists proposed the concept of metamaterials and began developing metamaterial-based superlenses. The advent of superlenses has truly provided tools capable of clearly magnifying nanoscale details. This represents not only a breakthrough in optical physics but also a foreshadowing of the importance of interdisciplinary exchange. This article, through a literature review, illustrates the role that cuttingedge technologies and research directions in superlenses developed in other countries can play in enabling the development of superlenses in China.
The Advancements and Applications of Machine Learning in Predicting Football Scores
Football stands as one of the world's most popular sports, where match prediction significantly contributes to its development and enhances its economic and cultural value. This calls for a comprehensive review of the technology used to predict football scores. The study first examines three traditional machine learning methods with simple single-layer architectures. While these methods are straightforward to understand, they suffer from limitations such as low accuracy and slow computational speed, rendering them less advantageous for practical applications. Subsequently, this paper explores four deep learning approaches, whose multi-layered structures outperform traditional methods in model capacity, learning efficiency, and computational power. Consequently, deep learning has become a primary direction for both current and future research in football scoring prediction. However, these approaches face challenges including generalization, accuracy, and explainability. To address these challenges, this paper proposes adversarial domain adaptation techniques to tackle generalization challenges. The introduction of high-dimensional dynamic feature data enhances accuracy. By integrating models, their respective strengths are leveraged to resolve interpretability challenges, while presenting optimistic prospects for deep learning applications in football scoring prediction. This article provides a comprehensive review of machine learning in football score prediction, which is conducive to readers' in-depth and comprehensive understanding of this field, and provides readers with reference for subsequent independent exploration and innovation.
Effects of Nanomaterials on Drug Co-encapsulation and Targeted Drug Delivery
Porous nanocarriers have emerged as versatile platforms for overcoming key limitations of traditional drug delivery systems by enabling precise co-encapsulation of multiple therapeutics and highly selective tumor targeting. This review surveys three major categories of nanocarriers, such as lipid-based, polymeric, and inorganic architectures, and highlights representative strategies such as liposome-in-liposome concentrisomes, mesoporous lipid nanoparticles (MLNPs), polymer-gatekeeper hollow silica nanoparticles (PHMSNs), and pH-sensitive gold nanoclusters. The article discuss how co-loading hydrophilic and hydrophobic agents at fixed ratios enhances therapeutic synergy and bypasses multidrug resistance and how surface functionalization or environmental triggers drive tumor-specific accumulation. Key challenges including complex syntheses, high manufacturing costs, immune clearance, premature payload leakage, and regulatory hurdles are examined. Finally, the article outlines current limitations of multi-stage delivery architectures, insufficient deeper tumor penetration, and the absence of robust and scalable production frameworks. By integrating these advances, nanocarriers hold promise for more effective, patient-friendly therapies across oncology and beyond.
Beta-Ga2O3 power semiconductor: material properties, doping and devices
Doping and heterojunction engineering are essential to modern semiconductor technologies, enabling them to have impressive performances. N-type and P-type doping introduce electrons and holes into semiconductor crystals, which are the basis for many electronic devices. Over the years,researchers have achieved precise control of doping in common materials like silicon, but new challenges have emerged with novel materials and nanoscale devices. For instance, some wide-bandgap materials show intrinsic resistance to being doped. At the same time heterojunctions, interfaces between different semiconductor materials, have revolutionized efficiently. The purpose of this paper is to synthesize recent advances and persisting challenges in n-type doping, p-type doping, and semiconductor heterojunctions. Highlighting how improved doping techniques and clever material combinations are addressing these challenges, and providing suggestions for further research. The main conclusion is that while enormous progress has been made -from stabilizing n-type doping in organic films to designing lattice-matched heterostructures for minimal defectsfurther innovation in doping methods remains crucial.
Survey of Communication Modification on Federated Learning
The widespread adoption of artificial intelligence technologies, particularly deep learning, has exposed significant security vulnerabilities that pose substantial challenges to cyberspace safety. Traditional cloud-centric distributed machine learning relies on centralized data collection from participants, making the system vulnerable to security breaches and privacy violations during data exchange and model updates. These risks often result in system performance degradation or sensitive data exposure. Federated Learning emerges as a privacy-preserving distributed machine learning paradigm that mitigates these issues. By facilitating encrypted model parameter exchanges between clients and a central parameter server, while retaining raw data locally, Federated Learning, unlike other training model, enables collaborative model training with significantly reduced privacy leakage risks, at the same time it maintains the performance of the whole training process. However, as deep learning models scale and Federated Learning tasks grow more complex, communication overhead becomes a major barrier to deployment. Consequently, optimizing FL communication efficiency has become a critical research focus.
Highway Traffic Flow Prediction: Methods, Challenges, and Trends
Traffic flow prediction is a core function of intelligent transportation systems. Accurate short‑term forecasts help reduce congestion, support signal control, and improve travel reliability. This paper reviews major methods for highway traffic flow prediction in clear, simple English. We organize the literature into four families: traditional statistical models, classic machine learning (ML), modern deep learning (DL), and hybrid approaches that combine multiple ideas. Statistical models (e.g., ARIMA) are simple and fast but struggle with nonlinear and spatial effects. ML methods (e.g., SVM, random forests, shallow neural networks) capture nonlinear patterns better but often require manual feature design. DL methods (e.g., LSTM/RNN, CNN, and graph neural networks) learn complex spatiotemporal patterns directly from data and now achieve state‑of‑the‑art accuracy on common benchmarks. Hybrids (e.g., ConvLSTM, graph‑attention networks, decomposition+DL) further improve robustness and accuracy. We also summarize widely used datasets and metrics, discuss open challenges (generalization, long‑horizon prediction, real‑time deployment, interpretability), and outline future directions (transfer learning, adaptive graphs and attention, uncertainty estimation, and cloud–edge deployment). The discussion is supported by real, citable studies and public datasets.
Progress in Cathode Materials for Sodium-Ion Batteries and Industrialization Research
With the development of society, humans have begun to pursue and explore more environmentally friendly and cost-effective lifestyles. And sodium-ion batteries, as an emerging industry, are constantly disrupting the traditional battery sector. Since sodium is more abundant than lithium and its cost is also much lower, and it has a high safety factor; it is also an indispensable development direction in the future. This article summarizes the advantages and disadvantages of the anion compounds, Prussian blue-like substances, and layered oxides of the current mainstream sodium-ion battery positive electrode materials, and finds that the cyanide wastewater in the synthesis of Prussian blue needs to be strictly treated, while vanadium-based materials face the problem of mining pollution. In contrast, sodium iron phosphate (NaFePO₄) has the advantages of low cost and low toxicity, and may become the mainstream choice for energy storage stations in the future. This article can promote the development of the research on sodium-ion battery positive electrodes and provides a research basis and direction for the design and improvement of subsequent sodium-ion battery positive electrode materials and manufacturing processes.
Research Progress in CO2 Capture and Storage Technology
With the intensification of global climate change, carbon dioxide (CO₂), as a major greenhouse gas, has become a key focus in achieving sustainable development and carbon neutrality. Carbon Capture and Storage (CCS) technology, an essential method for controlling emissions in industrial and energy sectors, has attracted wide attention. This paper first outlines the primary sources of CO₂ emissions from both daily life and industrial production, highlighting key sectors such as power generation, steel, and cement as major targets for emission control. It then introduces two typical CO₂ capture methods—adsorption and cryogenic separation—explaining their technical principles, advantages, limitations, and applicable scenarios. On the storage side, geological storage technology has matured and has been commercialized in regions such as Northern Europe. Based on this, the paper further explores the future prospects of CCS, emphasizing the need for progress in four areas: technology integration, cost optimization, policy incentives, and market mechanisms. By systematically reviewing current research and practical applications, this paper aims to provide theoretical support and strategic references for CCS development both in China and globally.