Abstract.Hyperledger Fabric is a consortium chain framework that is widely used at home and abroad. When conducting business with Fabric technology, high-concurrency transaction conflicts are often encountered. To optimize the high concurrent transaction processing performance of Hyperledger Fabric, this paper proposes a targeted fine-grained control strategy, relying on the Dynamic Priority Reordering - Chain Code Cache mechanism (DPRC-Cache) framework and attribute-based Access Control (ABAC). Experiments show that fine-grained resource access control can effectively reduce the transaction conflict … Read more →

Abstract.Addressing the current data privacy leakage challenges faced by BERT-based sequential recommendation systems, this study innovatively proposes a gradient attribution-based machine unlearning scheme for recommendation systems. The scheme first leverages gradient attribution techniques to precisely evaluate the contribution of each intermediate neuron to the prediction of user behavior information. Based on these evaluation results, we adopt a filtering strategy designed to identify and remove redundant neurons while retaining those "privacy neurons" closely associated with specific sensitive … Read more →

Abstract.This paper systematically expounds the practical paths of big data visualization in medical engineering within the domains of medical resource optimization, medical quality improvement, patient service enhancement, and financial performance management. By transforming complex medical data into intuitive visual charts and integrating big data analysis technologies with algorithmic models, it achieves the refinement and intelligence of hospital operational management. Studies have shown that big data visualization technology significantly improves hospital operational efficiency, medical service … Read more →

Abstract.This paper provides an overview of the fundamental principles, historical evolution, and current security challenges of the Network Time Protocol (NTP), along with a detailed exploration of various enhancement methods proposed to address existing vulnerabilities. Initially, the paper analyzes the working mechanisms of NTP and its version progression, highlighting the protocol's critical role in ensuring consistent time synchronization and event ordering within distributed systems. Subsequently, it delves deeply into typical security threats faced by NTP, including packet spoofing, replay … Read more →

Abstract.In response to the urgent need for ranging resolution, real-time target detection, and point cloud clustering in high-precision 4D millimeter-wave radar imaging, this paper systematically reviews the research progress of key signal processing algorithms. Regarding ranging accuracy, while spectrum refinement algorithms (such as ZFFT and CZT) and super-resolution algorithms (such as MUSIC and compressed sensing) improve resolution, they generally suffer from high computational complexity and insufficient utilization of phase information. In the field of constant false alarm rate (CFAR), … Read more →

Abstract.Precision Time Protocol (PTP) is the core implementation of the IEEE 1588 standard and is widely used in areas such as power, industrial Internet, financial transactions and communications that require extremely high time synchronization accuracy. However, with the deployment of PTP in an open network environment, the security mechanism it lacked at the beginning of its design gradually exposed serious vulnerabilities and became an important target for attackers. In recent years, the security research on PTP has been deepened, covering multiple directions such as attack classification, … Read more →

Abstract.It is selected the stock data of Kweichow Moutai for a certain period of time, uses python to model and analyze and predict the stock price, compares the predicted stock price with the real stock price, and then uses root mean square error, mean square error, and mean absolute error to evaluate the prediction model. RNN neural network can make good use of the nonlinear stock data and can memorize the effective information in the sequence data. Numerical experiments show that RNN neural network is a desirable stock forecasting method. Read more →

Abstract.Single Image Super-Resolution (SISR), a fundamental task in computer vision, aims to reconstruct high-quality images from their low-resolution counterparts. To address the limitations of existing methods in recovering high-frequency details and achieving efficient feature modeling, we propose a lightweight and effective Frequency-Aware and Fusion-Modulated Network (FAFMNet). The proposed network jointly enhances local texture representation and global structure modeling by incorporating a Frequency-Aware Modulator (FAM) and a Multi-Scope Fusion Block (MSFB), thereby enabling efficient … Read more →

Abstract.With the rapid development of high-speed railway, the traditional catenary operation and maintenance mode can no longer meet the growing demand. In this paper, a new type of overhead contact line vehicle is taken as the research object, and an evaluation system covering three dimensions of operation and maintenance efficiency, cost-effectiveness, safety and technical empowerment is constructed, and it is analyzed with the empirical data of Beijing-Shanghai high-speed railway and other lines. The results show that the new type of overhead contact system operation vehicle has significant … Read more →

Abstract.Metal dampers demonstrate extensive application potential in construction and mechanical engineering, serving as critical components with exceptional vibration damping capabilities. Their compact design and versatile interface configurations enhance structural safety while optimizing project efficiency. These devices effectively protect machinery systems and improve equipment reliability and stability. This paper reviews recent advancements in metal damper research, providing valuable insights for future engineering development. Read more →

Abstract.Volumetric fracturing is crucial for developing low-permeability, tight reservoirs and unconventional formations, as its complex fracture networks greatly enhance reservoir flow capacity. However, oil-water two-phase flow in volumetrically fractured horizontal wells exhibits complex behaviors that traditional single-phase flow models fail to capture, reducing the accuracy of well test interpretations. To overcome this limitation, this study develops physical and mathematical models for oil-water two-phase flow by integrating two-phase flow theory with the fracture network characteristics of … Read more →

Abstract.With the continuous development of deep learning technology in the field of medical imaging, objective and intelligent tongue diagnosis in Traditional Chinese Medicine has gradually become a research hotspot. However, due to the complex structure of tongue image features and their high subjectivity, as well as the limited computational power of embedded devices, achieving an efficient, accurate, and low-power tongue diagnosis system still faces many challenges. The goal of this paper is to explore a deep learning-based embedded tongue image segmentation method and to design an efficient … Read more →