Zhirong Zhang

With the evolution of Software-Defined Networking (SDN) toward distribution, cloudification, and servitization, multi-controller architectures have been widely adopted to enhance scalability. However, such architectures face challenges such as degraded inter-domain communication stability and security risks inherent in centralized trust mechanisms. To address these issues, we propose a Blockchain-enabled Trusted Routing scheme for SDN interdomain, called BTR-SDN. First, a multi-dimensional dynamic trust assessment model is designed to characterize and dynamically update the trustworthiness of participating entities by quantifying nodes’ historical behavior, performance metrics, and inter-domain reputation propagation. On this basis, a trust-aware hierarchical topology reconstruction and route pruning mechanism is introduced to adaptively adjust network views and path selection strategies according to trust assessment results, thereby enhancing the reliability of inter-domain communication. Furthermore, by leveraging the immutability of blockchain, critical information such as flow rules and routing decisions is anchored on-chain for attestation, while smart contracts are employed to automate policy enforcement, forming a closed-loop trustworthy assurance system. The experimental results demonstrate that BTR-SDN can not only allocate highly trusted paths suitable for various types of services, improving end-to-end transmission quality, but also effectively identify and isolate malicious nodes, significantly enhancing the security of inter-domain communication.

In situ rapid classification of rock lithology is crucial in various fields, including geological exploration and petroleum logging. Laser-induced breakdown spectroscopy (LIBS) is particularly well-suited for in situ online analysis due to its rapid response time and minimal sample preparation requirements. To facilitate in situ raw rock discrimination analysis, a portable LIBS device was developed specifically for outdoor use. This device built upon a previous multi-directional optimization scheme and integrated machine learning to classify seven types of original rock samples: mudstone, basalt, dolomite, sandstone, conglomerate, gypsolyte, and shale from oil logging sites. Initially, spectral data were collected from random areas of each rock sample, and a series of pre-processing steps and data dimensionality reduction were performed to enhance the accuracy and efficiency of the LIBS device. Subsequently, four classification algorithms—linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost)—were employed for classification discrimination. The results were evaluated using a confusion matrix. The final average classification accuracies achieved were 95.71%, 93.57%, 92.14%, and 98.57%, respectively. This work not only demonstrates the effectiveness of the portable LIBS device in classifying various original rock types, but it also highlights the potential of the XGBoost algorithm in improving LIBS analytical performance in field scenarios and geological applications, such as oil logging sites.

The wide diversity of species and the remarkable variation in morphological features that allow plants to adapt to a wide range of terrestrial environments is a fact that highlights the fundamental and crucial role of plants in the field of biodiversity studies. Currently, research on leaf classification is limited and in its early stages. A novel classification system based on laser-induced breakdown spectroscopy (LIBS) technology was proposed in this paper, integrated with machine learning for real-time, in situ detection and analysis of leaves. Four representative leaf samples—Ilex chinensis, Camellia japonica, Cinnamomum camphora, and Osmanthus fragrans—were subjected to spectral analysis and machine learning techniques. Spectral analysis revealed distinct spectral lines corresponding to elements such as Ca, Al, Mg, Na, and Fe, alongside common elements including C, N, and O. Principal component analysis (PCA) was employed to reduce the dimensionality of the spectral data, and the first 13 principal components used in this study captured 98.76% of the total variance. Following this, support vector machine (SVM), backpropagation artificial neural network and convolutional neural network (CNN) algorithms were applied for machine learning on the principal components to develop leaf recognition classification models. Through comparison, the CNN algorithm, boasting a classification accuracy of up to 94.44%, was ultimately selected. The models established by SVM and back propagation artificial neural network achieved accuracy of only 83.33% and 90.00%, respectively. The results suggest that integrating LIBS with machine learning is an effective and precise approach for leaf classification, offering promising applications in biodiversity research.

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

With the increase in the number of oil and gas pipelines laid in China, more attention needs to be paid to pipeline maintenance work. At present, the main methods of detecting natural gas leaks in oil and gas transmission stations include manual inspections, opposing natural gas detection equipment, and cloud desktop natural gas detection equipment. Hand held natural gas detection equipment is used for manual inspection, which requires regular manual inspection. However, the response speed is poor and gas leaks cannot be detected in a timely manner. The opposed laser gas detection method can only detect the presence of gas on the beam path. If a larger area of leakage detection is desired, more equipment needs to be installed, resulting in a greatly increase in hardware costs. The existing cloud desktop laser gas detection method controls the deflection of the laser beam through the cloud platform to achieve leak detection at various points in the area to be tested. However, the rotation speed of the cloud platform is slow, and a complete detection cycle takes dozens of minutes, and only the presence of gas can be detected. For accurate leak location, manual on-site survey is also required to further determine the leak location. In order to meet the needs of the real-time monitoring and rapid positioning of oil and gas pipeline leaks, in this work, a fast and accurately controlled dual wedge scanning mirror system is designed, which combines tunable semiconductor laser absorption spectroscopy technology to convert the gas measurement laser beam from point measurement to surface measurement, thereby obtaining the two-dimensional distribution of gas, which is conducive to subsequent analysis and positioning of gas leakage sources. By using the inverse solution iterative optimization algorithm, the angle of the wedge mirror is controlled to obtain an efficient and uniform beam scanning trajectory. The deflection direction and detection position of the laser beam are fused with the corresponding methane concentration information, and a methane concentration data containing position information is constructed. In order to quantitatively verify the measurement accuracy and spatial resolution in the experiment, a standard air bag is used to simulate the methane leakage distribution. The results show that the minimum detection limit of the system can be lower than 5×10–4 m, and the spatial resolution can be less than 6 cm. At the same time, this method can adjust the scanning step node based on the measurement distance of the oil from gas station, thereby achieving adjustable imaging resolution. This imaging method provides a new idea for accurately positioning and detecting the methane leakage location and amount.