Lithium slag holds significant potential for recycling and reuse. The barriers in the recycling process were addressed, such as surface protectants and surface by-products (diffusion pump oil and lithium hydroxide), that impeded efficiency, and the risk of uncontrolled reactions leading to explosions. A method of using water jet impact to desorb surface protectants and by-products from lithium slag and prevent reactive explosions was proposed. Based on the binding relationship between the lithium slag, surface protectants, and lithium hydroxide by-products, a bridging model for oil-lithium-hydroxide lithium particles was proposed. Subsequently, a water jet computational fluid dynamics-discrete element method (CFD-DEM) triple-component coupled depolymerization action model was proposed to explore the depolymerization characteristics of the oil-lithium-hydroxide lithium bridging model under different water jet pressures. The results show that the destruction time of particle adhesive bonds in the oil-lithium-hydroxide lithium model is inversely proportional to the jet pressure. At a jet pressure of 0.1 MPa, a particle adhesive bond destruction rate of over 95% can be achieved within 0.05 s. When the jet pressure exceeds 0.5 MPa, the time to reach a 95% bond destruction rate is just 0.015 s. This method effectively removes the surface protectant of the lithium slag and promptly eliminates lithium hydroxide and the foam it forms while ensuring safety, efficiency, and continuous digestion operations. These findings provide significant guidance for the application of water jet technology in the recovery of reactive metals and can be specifically applied to the high-efficiency, controlled, and safe recycling field of lithium slag.

To enhance the safety monitoring of power transmission lines, the distributed fiber Bragg grating array sensing technology was employed to measure the dynamic motion characteristics of optical power ground wire (OPGW) cables in laboratory conditions. The results show that this technology can effectively monitor the dynamic behavior of OPGW under simulated aeolian vibrations and galloping states, clearly recording various vibration patterns. The experimental data reveal that by increasing the spatial density of the grating array sensors and reducing the system's low-frequency phase drift, the monitoring performance can be further enhanced. It is evident that the distributed vibration sensing technology based on fiber Bragg grating arrays provides a novel technical approach for the distributed dynamic structural health monitoring of OPGW cables.

In order to improve the prediction accuracy of pedestrian crossing patterns by conventional vehicles in unsignalized crosswalk road sections, a pedestrian crossing pattern prediction model integrating extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms was proposed. First, the pedestrian-vehicle interaction data in the unsignalized crosswalk section were collected based on the cameras and LiDAR installed on the roadside, and the behavioral characteristics of pedestrians and vehicles were analyzed, and then the factors affecting the pedestrian crossing patterns were screened. Next, the predictive effects of different combinations when used as model inputs were explored. Finally, vehicle speed, vehicle-to-zebra crossing distance, time to collision(TTC) and pedestrian step speed were used as model inputs, and pedestrian crossing patterns were categorized into direct crossing and waiting crossing and used as model outputs, and the XGBoost-MLP model for pedestrian crossing pattern prediction was established. The prediction accuracy of this model for pedestrian crossing patterns reaches 88.65%, which compares with the single XGBoost model and the MLP model, and its accuracy is improved by 3.85% and 2.61% compared to the single XGBoost model and MLP model, respectively.

The armature structure and characteristic parameters in electromagnetic launch systems have a significant impact on the launch performance. In order to explore the dynamic characteristics and influencing factors of composite armatures, based on Maxwell's equations and electromagnetic field theory, a mathematical model of dynamic emission coupling between embedded and semi embedded composite armatures was derived. The influence of factors such as the capacity and initial working voltage of energy storage capacitors, the number of turns of driving coils, and the structural parameters of composite armatures on acceleration performance were analyzed and studied. The influence of structural parameters a, c, and d of composite armatures on emission performance was calculated. The results show that the higher the initial working voltage and capacitance of the energy storage capacitor, the higher the emission efficiency of the composite armature. The number of turns of the driving coil in the synchronous induction coil gun also affects the firing performance of the composite armature. As the number of turns increases, the firing efficiency does not always increase. The parameters a and c have little impact on the emission performance, while parameter d has a significant impact on the emission performance. The proposed mathematical model and analysis results can provide a theoretical basis and data reference for the design of composite armatures.

In order to study the mechanical properties of the column foot joint of a self-resetting cylindrical pier, the mechanical mechanism of self-resetting cylindrical pier column foot joint with circular section was analyzed theoretically. The calculation formula of key points of the whole force and displacement hysteresis curve of the pier was derived, including yield point, failure point, etc. The theoretical analysis model of the relationship between pier jacking force and displacement was established. Based on OpenSees platform, fiber hinge model was used to model the section of pillar foot of self-resetting energy-consuming pier. Combined with pseudo-static test results, the feasibility and accuracy of the fiber hinge model were verified. On this basis, a self-resetting cylindrical pier was set up by fiber hinge model, and the key point of the deduced force-displacement hysteresis curve was compared with the skeleton curve of the simulated cylindrical pier to verify the accuracy of the deduced method. The results show that the derivation method can obtain the pressure relief point, yield point and failure point of self-resetting cylindrical pier, which can provide reference for the research of self-resetting pier with circular section in the future.

In order to investigate the role of rheumatoid arthritis (RA)-related pathways in lung squamous cell carcinoma (LSCC). By obtaining gene expression data for RA and LSCC from the GEO and TCGA database, differentially expressed genes were screened using GEO2R tool and Rstudio software. GO/KEGG functional enrichment analysis identified key genes in the RA signaling pathway. Combining SNP data from the IEUopenGWAS database, Mendelian randomization analysis was used to assess the causal relationship between the RA signaling pathway and LSCC. The constructed gene-drug and ceRNA networks, along with immune cell infiltration analysis, revealed 188 co-expressed differential genes, mainly enriched in the RA signaling pathway. Mendelian randomization analysis showed that increased activity of the RA signaling pathway is associated with a reduced risk of LSCC. This study provides new insights into the pathogenesis and potential research directions for the treatment of LSCC.

Aiming at obvious load fluctuation trend, strong randomness and low accuracy caused by unreasonable parameter values of the prediction model involved into the power load forecasting process, a combined prediction model composing of ALIF (adaptive local iterative filtering), VMD (variational mode decomposition), NGO (northern goshawk optimization) and CNN-LSTM (convolutional neural networks - long short-term memory) was established. Firstly, CCM (convergent cross-mapping) method was used to identify the key factors affecting the power load. Secondly, an innovative combination of ALIF, NGO-based VMD and FE (fuzzy entropy) was employed for combinatorial decomposition and necessary recombination of original load sequence. Next, based on the modal components generated after decomposition and recombination, combined with optimal hyperparameter combination of CNN-LSTM determined by NGO method, an NGO-CNN-LSTM day-ahead power load combination prediction model with the high prediction accuracy, short training time and fast convergence speed was formulated. Compared with other benchmark models, the obtained results demonstrated that the proposed model has the better adaptability and prediction accuracy, and can provide important technical support for the safe, reliable and economical operation of power system.

To investigate the mechanisms of floor heave and support methods for large-section inclined shafts in highly expansive and weak rock, a case study was conducted on the main inclined shaft of a mine in the Yaojie Coal and Electricity Group. Through field surveys and laboratory tests, characteristics such as low rock mass strength, poor integrity, high clay content, and softening and swelling upon water exposure were identified. A Flac3D numerical model was established to simulate the deformation patterns of the tunnel under actual engineering conditions, elucidating the mechanisms of floor heave deformation in large-section soft rock inclined shafts. Three optimized prevention strategies were summarized, and an optimized support scheme of “inverse floor arch and anchor cable” was proposed based on the original support design. Field measurement data indicated that the optimized support scheme significantly improved the floor heave control in soft rock tunnels, enhanced the bearing capacity of the surrounding rock, reduced floor heave deformation, and achieved stability control of the tunnel surrounding rock. This provides an important reference for the control of floor heave in large-section shallow soft rock inclined shafts.

Defect detection is regarded as an indispensable step in the industrial production process. At present, manual detection is faced with the problems of low efficiency and high cost. A ceramic small target defect detection algorithm based on deep learning was proposed. For small target defects, a slice pre-training layer was first added to reduce the loss of graphics memory resources by large-size images. Secondly, a small target detection layer was added for the detection of small target defects, and a large target detection layer was removed to reduce the number of parameters. In addition, a feature selection fusion module based on MLCA (mixed local channel attention) was proposed to improve the perception of small target defects. Finally, a detection head with shared parameters was designed to further reduce the number of learnable parameters of the algorithm. By comparing with the baseline model, taking the ceramic cup as an example, the detection accuracy of this algorithm has been improved by 20.9%. Combined with the developed detection software and experimental platform, the detection efficiency of the ceramic cup has been enhanced by about 46.9%.

To address the issue that traditional fault diagnosis methods struggle to accurately diagnose faults in the nuclear reactor coolant system (RCS) of nuclear power plants under uncertain conditions, a dynamic fuzzy radial basis function neural network (DFRBFNN) model was established for RCS fault diagnosis following these steps. First, based on the fault types and sample data of the RCS, the initial structure of the DFRBFNN model was determined. Then, using the radial basis function neural network method, the initial DFRBFNN model for RCS fault diagnosis was constructed, and a random initialization method was applied to initialize the connection weights from the defuzzification layer to the output layer of the initial DFRBFNN model. Finally, the error reduction rate method was used to adjust the structure and parameters of the initial DFRBFNN model, resulting in the final DFRBFNN model for RCS fault diagnosis. The established model was applied to diagnose loss of coolant, flow loss, and steam generator tube rupture accidents, and its performance was compared with traditional fault diagnosis models to verify its effectiveness. The research shows that the constructed DFRBFNN model can accurately diagnose RCS faults under uncertain conditions.