Aiming at the problem of accuracy degradation of laser velocimetry signals in high-speed flight scenarios due to complex noise interference, this study constructs a frequency estimation method based on the noise feature analysis of Butterworth filter and multi-domain spectrum fusion. Through the quantitative analysis of noise mean, variance and power spectral density, combined with the joint spectral analysis of Fourier Transform (FT) and Discrete Fourier Transform (DFT), the high-precision frequency estimation of noise-containing signals is achieved in MATLAB simulation environment, and the estimated frequency is 40999836.00Hz, with the relative error less than 0.01%. The proposed algorithm improves the signal-to-noise ratio by more than 35% while maintaining the signal integrity, which provides a reliable theoretical and methodological support for the noise suppression and parameter measurement of the laser speed measurement system under the high dynamic flight environment.

This paper proposes an accurate positioning method for rocket debris based on genetic algorithm. The rocket debris will produce supersonic sonic boom in the process of falling, this paper realises the high precision positioning of the sonic boom position and time of the rocket debris by arranging multiple vibration wave monitoring devices in the debris theoretical fallout area, constructing a set of multilateral measurement equations by using the difference in the arrival time of the sonic boom, and introducing genetic algorithms to optimally solve the hyperbolic equations to achieve high precision positioning of the sonic boom position and time of the rocket debris. The results show that the genetic algorithm can effectively avoid the local optimum problem of the traditional method and significantly improve the positioning accuracy. In addition, this paper also explores the error analysis model and the optimisation method of monitoring equipment arrangement, which further improves the reliability and adaptability of the positioning system.

With the rapid development and increased complexity of global financial markets, the effectiveness of risk management becomes more and more important. In this paper, a Bayesian network-based financial market risk prediction and decision support system (DSS) is proposed to enhance the accuracy and efficiency of financial decision making. The system architecture includes an input data module, an inference engine, and a user interaction interface, emphasizing data integration and real-time processing capabilities. The input module is responsible for collecting real-time and historical financial data, and the inference engine utilizes Bayesian inference algorithms to assess risks and generate decision recommendations. It is shown that the system is able to effectively capture the complex dependencies of variables in the financial market, provide accurate risk prediction, and thus help decision makers to make scientific judgments in a dynamic environment.

In addition, the user interaction interface is designed to make the system more user-friendly for quick understanding and application of risk assessment results. The integration and maintenance strategy of the system ensures the stability and scalability of the DSS and lays the foundation for future functionality enhancements. Despite the positive results of this study in financial market risk management, there are still some challenges, such as the dependence on the quality of input data and the increase in computational complexity. Future research could explore the integration with other machine learning techniques to further enhance the prediction capabilities

As a key link in the global supply chain, the aviation logistics supply chain is of great significance in protecting the security and development of China's supply chain, and in promoting the economy of air cargo and civil aviation. In recent years, the aviation logistics industry has been driven by both technological innovation and market demand, and has both opportunities and challenges. In order to make the aviation logistics supply chain more resilient when encountering risks and shocks, this paper constructs a set of factors influencing the resilience of aviation logistics supply chain, adopts the DEMATEL-ISM model, analyzes the causality and hierarchical structure between the resilience influencing factors, and puts forward the strategic suggestions to maintain the resilience of aviation logistics supply chain with respect to the important influencing factors, so as to enrich the related research in the field of resilience of aviation logistics supply chain.

In the context of globalization and rapid development of e-commerce, cross-border air logistics still becomes an important factor restricting cross-border e-commerce. On the basis of analyzing the constituent elements and functions of cross-border air logistics system as well as the multiple structures of cross-border air logistics system, this paper constructs a four-dimensional conceptual model based on Hall's three-dimensional model, and discusses in detail the dimensions of the model as well as the role modes between the dimensions, which provides theoretical foundations and practical guidance for the construction and optimization of cross-border air logistics system in China.

The Parlung Zangbo River Basin, located in the Tibet region, is a typical area prone to frequent debris flows. As a highly destructive geological hazard, debris flows pose a serious threat to the ecological environment and human activities in this basin. This paper aims to comprehensively assess the risk factors of debris flow in the Parlung Zangbo River Basin by integrating Geographic Information System (GIS) and Analytic Hierarchy Process (AHP), and to generate a debris flow risk zoning map. Firstly, the paper analyzes the main factors influencing the occurrence of debris flows in the basin, including topographic features (such as slope aspect, slope gradient, and Melton ratio), precipitation intensity, vegetation cover, and the nearest distance to faults. Based on this analysis, the AHP model is employed to assign weights to these factors and conduct consistency checks to ensure the scientific validity and reliability of the assessment results. The study results indicate that the Melton ratio, precipitation index, and slope aspect and gradient are the primary factors affecting the occurrence of debris flows, with varying degrees of influence across different areas. By applying the weights of these factors to the GIS platform, this study generates a debris flow risk zoning map for the Parlung Zangbo River Basin, providing a scientific basis for disaster prevention and planning in the region. The findings of this study not only hold significant implications for disaster control in the Parlung Zangbo River Basin but also offer valuable insights for debris flow risk assessment in similar geological environments.

This paper aims to investigate the analysis of audio waveforms, spectrograms, amplitude spectra after Fast Fourier Transform (FFT), and Mel spectrograms of different genres of music in the Python programming environment. By quantifying music, the paper seeks to achieve classification of various music genres. Regression models in machine learning are employed to analyze the amplitude spectra and classify different genres of music. The paper trains the model using several common music genres, validates its feasibility through testing, and concludes by applying the insights from the analysis to guide the composition of music and classify musical compositions.

This study explores the application and comparison of the Minimum Steiner Tree (STP) algorithm and the Minimum Spanning Tree (MST) algorithm in optimizing the communication and transportation network in the central area of Kanazawa City. Through a detailed analysis of the classic STP problem and related algorithms, this paper proposes an optimized Ant Colony Optimization algorithm (IACO) and verifies its efficiency on standard datasets. The results show that this algorithm significantly reduces the total connection cost and improves network efficiency and robustness when connecting key nodes. Therefore, this paper suggests that the IACO algorithm has significant application value in urban network planning and recommends further exploration of its potential in larger and more complex networks to optimize the design of urban communication and transportation networks.

In network system construction, the issue of network traffic security is of paramount importance. Against the backdrop of strengthening national cybersecurity, traffic inspection plays a significant role in continuously enhancing the security capabilities of network systems. However, as more and more traffic is transmitted using the HTTP protocol over SSL/TLS encryption, this not only provides protection for legitimate communications but also facilitates covert attacks by malicious actors. Traditional traffic detection systems (such as Snort and Suricata) struggle with the challenge of inspecting encrypted traffic, and their detection rules are typically configured based on known attack patterns, making them less effective against evolving new types of attacks.

To address the aforementioned issues, this paper innovatively proposes a comprehensive traffic detection frame. The main contributions of this work are summarized in three parts:

1.Real-time Traffic Decryption and Detection Framework: We have designed and implemented an efficient real-time traffic decryption and detection architecture that cleverly integrates man-in-the-middle proxy technology to enable real-time decryption of HTTPS encrypted traffic.

2. Model Training and Validation Using an SQL Injection Dataset: To validate the effectiveness of the framework, we utilized a dataset containing SQL injection attacks to train the detection model and applied it within the framework to evaluate its capability to detect unknown attack traffic.

3. For the development and testing of the SnortML plugin: To enable real-time traffic feature extraction in SnortML, we added feature extraction capabilities to the SnortML detector and used the tcpreplay tool to simulate complex network traffic scenarios.

Through our research, we have achieved automatic detection and efficient defense against real-time, encrypted, and potentially unknown attack traffic. Experimental result clearly demonstrate that the framework has significantly improved the accuracy of encrypted traffic detection, providing strong technical support and innovative ideas for building a safer and more robust network environment.

This paper focuses on predicting the development of new energy vehicles (NEVs) using random forest model and gray Prediction models. New energy vehicles, including hybrid, pure electric, and fuel cell electric vehicles, have seen rapid growth due to their low pollution, low energy consumption, and strong peak load capacity. This research aims to analyze the factors influencing the NEV industry and forecast its future development. The gray Prediction method, known for addressing small sample sizes and incomplete data, is used to forecast the conservative quantity of NEVs in this paper. Meanwhile, the random forest model regression model evaluates the impact of various factors on the market share of traditional fuel vehicles. Key variables include production, fuel prices, charging stations, subsidies, and sales volumes. Results indicate that the NEV market in China will experience rapid growth over the next decade, with increasing market penetration and sales. Factors such as government subsidies and technological advancements significantly influence the traditional fuel vehicle market.

This paper aims to accurately determine the position and time of sonic explosions in the air by analyzing data from single and multiple debris cases. It focuses on identifying the minimum number of monitoring devices needed, considering random recording time errors, and correcting models to improve positioning accuracy. Additionally, it explores precise positioning strategies when time errors can't be effectively reduced, enhancing airspace debris management. Problem 1: Understanding sound wave propagation, we used a three-ball positioning method. Since both the detection point and the sonic explosion debris are high in the air, the fourth point was used for accuracy. Using least squares method with randomly selected data, we determined the position (longitude: 110.4989, latitude: 27.3105, height: 857.4884m) and time (19.3128s). Problem 2: Theoretically, 44 devices are needed, but four can classify unknown time data groups. Calculations are based on extending the first problem's theoretical basis, introducing relative time and position error solutions. Problem 3: Using actual data, four devices calculate correct locations, but time uncertainty causes larger errors. Introducing additional equipment for secondary screening or multiple data exclusion improves accuracy. Problem 4: Random perturbations of time data (ranging from -0.5 to 0.5) tested the model's anti-interference capabilities. Adjustments included adding an error function and repeating data perturbations until the solution met error range criteria. This significantly reduced time and position errors, with position errors within 1km. This study enhances the efficiency and safety of airspace debris management by improving the accuracy of sonic explosion positioning.

With increasing coal mining depth, the likelihood of rock bursts has significantly risen, posing a major threat to coal mine safety in China. This paper aims to develop classification and prediction models to identify and predict rock burst precursor signals, thereby mitigating this hazard in deep mining. Using acoustic emission (AE) and electromagnetic radiation (EMR) data, we extracted time-frequency domain features and constructed decision tree and grey prediction models with a sliding window approach. For interference signal identification, we analyzed class C signals, determining their mean, kurtosis, and spectral peak values. For predicting class B signal trends, similar features were used to construct a decision tree model, which successfully identified precursor signals. The model demonstrated excellent classification performance in ROC curve tests. This research provides a scientific basis for preventing and controlling rock bursts, reducing engineering efforts, and enhancing coal mine safety.

This study explores the use of simulated natural drainage systems for sustainable urban landscape planning to address flood disasters in low-lying urban areas. Traditional drainage methods lack sustainability, whereas simulated natural drainage systems, such as green infrastructure, rain gardens, and wetland parks, can enhance urban flood resilience and environmental quality through scientific design and strategic layout. In San Francisco, the issue of flood disasters in low-lying areas has become increasingly severe, with traditional drainage systems struggling to effectively manage flood risks under extreme weather conditions. This study employs R programming to simulate natural drainage systems, evaluating their effectiveness in mitigating flood disasters and improving ecological environments. By utilizing rainfall data, topographic data, and drainage system data from San Francisco, a simplified hydrological model was constructed to calculate catchment areas and runoff, simulating the effects of natural drainage systems. Results indicate that natural drainage systems significantly reduce flood risks and enhance ecological benefits. The findings provide urban planners with new flood management strategies, emphasizing the importance of integrated management and systemic thinking, and highlight the future potential for widespread application in urban planning. By integrating landscape design with water resource management principles, this study proposes a systematic solution validated through case studies in different cities, demonstrating its feasibility and effectiveness. This research offers new insights and methods for urban flood control engineering and landscape planning, contributing to enhanced urban disaster resilience and improved urban living environments. The simulation of natural drainage systems facilitates sustainable urban development, providing valuable references for the advancement of sustainable urban landscape planning.

Considering the issue of low control accuracy in joint trajectory tracking control for manipulator systems with actuator saturation due to external disturbances, modelling inaccuracies, and joint friction, a sliding mode active disturbance rejection control approach was proposed. An improved extended state observer is employed to observe and estimate the lumped disturbances affecting the system, providing feedback compensation. A variable gain reaching law is devised, coupled with a fast non-singular terminal sliding mode to design the system control law, which mitigates chattering inherent and ensuring precision control. Additionally, a novel output error compensation-based anti-windup scheme is introduced to compensate for the detrimental impacts caused by actuator saturation. Simulation results collectively demonstrate that the proposed tracking control algorithm exhibits better control performance and robustness against disturbances.

This paper delves into the realm of biomedical engineering and explores innovative approaches aimed at advancing healthcare technologies for enhancing patient outcomes. Drawing on interdisciplinary research from engineering, medicine, and biology, the paper examines how cutting-edge engineering solutions are revolutionizing diagnostics, treatment modalities, and patient care across various medical fields. Through a comprehensive review of empirical studies and theoretical frameworks, the paper discusses the role of biomedical engineering in developing novel medical devices, imaging techniques, regenerative therapies, and personalized medicine approaches. It explores the principles of bioinformatics, biomaterials, biomechanics, and biotechnology as key pillars of biomedical engineering research and innovation. Additionally, the paper highlights the importance of interdisciplinary collaboration between engineers, clinicians, researchers, and industry partners to accelerate the translation of engineering innovations into clinical practice. It addresses the challenges and opportunities associated with regulatory approval, clinical validation, and commercialization of biomedical technologies, emphasizing the need for rigorous testing, ethical considerations, and patient safety measures. Through case studies and examples, the paper showcases successful biomedical engineering projects that have made significant contributions to healthcare delivery, disease management, and quality of life improvement for patients worldwide. The paper concludes by advocating for continued investment in research, education, and healthcare infrastructure to foster innovation and address unmet medical needs in the pursuit of better health outcomes for all.

This paper explores the concept of smart cities and investigates the integration of urban planning and engineering innovations to foster sustainable development in urban areas. Drawing on literature from urban studies, engineering, and sustainability, the paper examines how smart city initiatives leverage advanced technologies to improve efficiency, resilience, and quality of life for residents. Through a comprehensive review of empirical studies and theoretical frameworks, the paper discusses the role of engineering innovations such as Internet of Things (IoT), artificial intelligence (AI), data analytics, and renewable energy systems in transforming urban infrastructure and services. It explores key smart city domains including transportation, energy, water management, waste management, and public safety, highlighting innovative solutions and best practices from around the world. Additionally, the paper addresses the importance of citizen engagement, participatory planning processes, and inclusive governance models in shaping the development of smart cities that meet the needs of diverse populations. Through case studies and examples, the paper showcases successful smart city projects that have demonstrated tangible benefits in terms of environmental sustainability, economic prosperity, and social equity. The paper concludes by advocating for continued collaboration between urban planners, engineers, policymakers, and community stakeholders to drive the evolution of smart cities towards more livable, resilient, and inclusive urban environments.

This paper explores recent innovations in renewable energy technologies and engineering solutions aimed at advancing sustainability and mitigating climate change. Drawing on research from engineering, environmental science, and energy policy, the paper examines the development and deployment of renewable energy sources such as solar, wind, hydroelectric, and bioenergy. Through a comprehensive review of empirical studies and case examples, the paper discusses technological advancements in renewable energy generation, storage, and distribution systems. It explores the integration of renewable energy into existing power grids, microgrid systems, and smart energy networks to enhance reliability, resilience, and efficiency. Additionally, the paper addresses key challenges and opportunities in scaling up renewable energy deployment, including policy frameworks, investment incentives, and public acceptance. Through case studies and examples from around the world, the paper highlights successful renewable energy projects that demonstrate the transformative potential of engineering innovations in achieving a low-carbon and sustainable energy future.

This paper explores the rapid advancements in artificial intelligence (AI) and machine learning (ML) technologies and their transformative impact on various industries and societal domains. Drawing on interdisciplinary research from computer science, engineering, and social sciences, the paper examines the evolution of AI and ML algorithms, models, and applications. It discusses how AI and ML are revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and education by enabling automation, predictive analytics, and personalized services. Through a comprehensive review of empirical studies and case examples, the paper explores the ethical, societal, and economic implications of AI and ML adoption, including issues related to privacy, bias, job displacement, and algorithmic accountability. Additionally, the paper highlights the role of interdisciplinary collaboration, data governance, and regulatory frameworks in harnessing the potential of AI and ML for positive societal impact. Through case studies and examples of AI-driven innovations, the paper showcases how these technologies are reshaping industries, improving efficiency, and driving innovation across diverse sectors. The paper concludes by discussing future trends and challenges in AI and ML research and deployment, emphasizing the need for responsible AI development and inclusive technology policies to ensure equitable access and benefits for all.

This paper examines emerging trends in biotechnology and the role of engineering solutions in addressing challenges related to human health, environmental sustainability, and industrial applications. Drawing on interdisciplinary research from biotechnology, bioengineering, and environmental science, the paper explores recent advancements in biotechnological tools, techniques, and applications. It discusses how engineering principles are applied in areas such as genetic engineering, synthetic biology, bioprocessing, and bioremediation to develop novel solutions for healthcare, agriculture, energy production, and environmental protection. Through a comprehensive review of empirical studies and case examples, the paper highlights the potential of biotechnology to revolutionize medical treatments, enhance crop productivity, produce renewable fuels, and mitigate environmental pollution. Additionally, the paper addresses ethical, regulatory, and societal considerations associated with the use of biotechnological innovations, including concerns related to biosafety, biosecurity, and genetic privacy. Through case studies and examples of successful biotechnology applications, the paper illustrates the transformative impact of engineering-driven biotechnological advancements on human well-being and planetary sustainability. The paper concludes by discussing future directions and challenges in biotechnology research and deployment, emphasizing the need for interdisciplinary collaboration, responsible innovation, and stakeholder engagement to realize the full potential of biotechnology for the benefit of society and the environment.

This paper investigates recent innovations in materials science and engineering and their significant contributions to technological advancements and sustainability. Drawing upon interdisciplinary research from materials science, engineering, and environmental studies, the paper explores novel materials, manufacturing processes, and applications that are driving progress across various industries. It discusses the development of advanced materials such as nanomaterials, biomaterials, and smart materials, highlighting their unique properties and potential applications in areas such as electronics, energy storage, healthcare, and environmental remediation. Through a comprehensive review of empirical studies and case examples, the paper examines how materials engineering is facilitating the transition towards a more sustainable and circular economy by reducing resource consumption, minimizing waste generation, and improving energy efficiency. Additionally, the paper addresses challenges and opportunities associated with the adoption of innovative materials, including issues related to scalability, cost-effectiveness, and environmental impact assessment. Through case studies and examples of successful material innovations, the paper demonstrates the transformative impact of materials science and engineering on technological innovation and sustainable development. The paper concludes by discussing future directions and potential breakthroughs in materials research and engineering, emphasizing the importance of interdisciplinary collaboration, materials informatics, and lifecycle analysis in driving continued progress towards a more sustainable and technologically advanced society.