Dafuling(DFL) deposit is a part of the Mingyuefeng ore field in eastern Hunan Province, China, is a typical perigranitic uranium deposit discovered recently. The characteristics of the ore minerals and the features of rare earth elements in this deposit have not been previously documented. In order to further elucidate the characteristics of ore minerals and rare earth elements (REE), as well as explore their indicative significance for uranium metallogenesis. Herein, uraninite, the primary ore mineral in the deposit, was investigated via scanning electron microscopy and electron probe microanalysis. Additionally, laser ablation-inductively coupled plasma-mass spectrometry(LA-ICP-MS) was used for the first time to determine the in situ the REE characteristics of uraninite. Uraninite exhibits a distinct fractionation between light rare earth elements(LREE) and heavy rare earth elements(HREE), while displaying a negative Eu anomaly. The (La/Yb)_N ratio exceeds 1, indicating a significant enrichment of LREE. Both major elements and REE in uraninite suggest its formation within a hydrothermal environment at temperatures ranging from moderate to low, below 350 °C. Consequently, DFL deposit can be classified as a typical hydrothermal vein-type uranium deposit. The REE serve as indicators of the transition of ore-bearing hydrothermal fluids from high salinity to low salinity, and the hydrothermal environment gradually shifts from a reducing state to a weakly oxidizing state. These observations suggest that the ore-bearing hydrothermal fluid responsible for metallogenesis originating from the deep crust or lithospheric mantle, ascended along regional deep faults, and subsequently underwent a series of physical and chemical transformations, eventually accumulated mineralization in suitable locations within DFL deposit.

In response to the problem of low accuracy in epilepsy detection and recognition using single-view networks, a multi-view convolutional network model with fused attention mechanism (FAM-MCNN) was proposed. Multiple view features were extracted from time domain, frequency domain, time-frequency domain and nonlinear domain to characterize electroencephalogram(EEG) signals comprehensively. Multi-scale convolution was used to capture different levels of detail information. In order to improve the ability to distinguish different types of EEG signals in epileptic patients, the attention mechanism was introduced to combine the features from view dimension and single feature vector dimension respectively. The results of the comparison experiments performed on the CHB-MIT epilepsy dataset show that the average accuracy, sensitivity, and specificity of the FAM-MCNN model are improved by 14.29%, 16.13%, and 12.54%, respectively, when compared to a single-view network. In addition, experiments under a small number of training samples (25%) show that its detection performance reaches the level of the comparison model with a large number of training samples (80%~90%).

In the condition of engineering surcharge near the existing pipeline, the pipeline produce subsidence deformation and further threaten the normal operation of the existing pipeline. Most of the research in this area stays in the finite element and indoor tests, and few theoretical solutions are used to analyze the stress and deformation response of existing pipelines under adjacent engineering surcharge. Based on this, the pipeline-soil interaction under this working condition was investigated by using theoretical analysis. Firstly, the Boussinesq solution was used to analyze the additional stress at the axis of the existing pipeline. Then, the pipeline was simplified as an infinite beam rest on the Pasternak model to further obtain the total energy of the system during the deformation of the pipeline. Finally, the stress and deformation response of the pipeline can be obtained according to the energy variation theory. By comparing with the existing experimental data, the correctness of the proposed method was verified. Compared with the degradation analysis of the proposed method, the proposed method is closer to the measurements. The parameter study shows that the stress deformation of the pipeline wills decrease nonlinearly with the increase of the buried depth of the pipeline. Increasing the diameter of the pipeline would increase the deformation response of the pipeline, the deformation of the pipeline is not sensitive to the angle between the pipeline and the loading area. Increasing the horizontal distance between the pipeline and the loading area can effectively reduce the stress and deformation response of the pipeline, and the deceleration increases first and then decreases. A series of analysis results can be used to analyze the influence of engineering surcharge on the stress and deformation of existing pipelines in practical engineering.

In order to investigate the potential causative factors and mechanisms of accidents in the flight transit security system, and to further ensure the safety of civil aviation operation, based on the system theory and the gray correlation theory, and combining with the actual situation of the flight transit security operation process, the safety problems in the system were transformed into the control and feedback problems, and the safety control and feedback structure was mapped out. Using complex network theory to transform accident causation and its logical relationship, a directed weighted accident causation network model was constructed, the overall characteristics of the network and the connection of each node from different perspectives were quantitatively analyzed, such as the node degree, the network diameter and the average path length, etc., and then 16 important accident causation factors affecting the flight transit security system were selected. Through grey correlation analysis, the influence degree of each cause factor on the accident was judged, and the key cause factors that need to be prevented and controlled were finally determined. The results show that the personnel factor dominates the accidents in the flight crossing security system, and its sub-factors, such as speeding, insufficient number of personnel, error of towing personnel and illegal entry of personnel into the control area, are the key causal factors leading to the accidents.

Under the dual carbon background, a multi-domain communication architecture was constructed to address issues such as a single communication mode and poor adaptability between business and communication technology in the new power load management system. The key supporting technologies of this architecture were comprehensively analyzed. Additionally, an adaptability evaluation system for business and communication technology was developed based on varying business types and their specific communication requirements. A communication technology adaptation method was proposed, employing the fuzzy analytic hierarchy process (FAHP), the CRITIC method, and grey relational analysis-technique for order preference by similarity to an ideal solution (GRA-TOPSIS). The proposed method facilitates the analysis of adaptability between differentiated business requirements and multi-domain communication technologies. The analysis of case studies indicates that the proposed architecture and the adaptation method offer an effective theoretical basis and solution for selecting multi-domain communication technology for the new power load management system business.

Weld defects present within pipelines constitute a considerable threat for leakage and rupture accidents. To elevate the detection precision of these defects, X-ray inspection was employed as a means to identify and locate them with greater accuracy. However, the diverse types, small sizes, and complex backgrounds of weld defects posed challenges for accurate detection. To address the limitations of current deep learning-based models, such as inadequate adaptability to complex backgrounds and lighting variations, as well as poor performance in detecting small targets, an improved faster region convolutional neural networks(Faster R-CNN) network model was investigated. This model incorporated a channel attention mechanism into the backbone network, modified the residual block structure, and employed ROI Align to replace the traditional ROI Pooling. The results show that compared to the original algorithm, the improved Faster R-CNN model achieves significant improvements in mean average precision (mAP) and F₁, with respective increases of 15.82% and 16.44%. It is concluded that this improved model can meet the high-precision requirements for weld defect detection and holds significant theoretical importance as well as promising prospects for engineering applications.

Accurately predicting bike-sharing flow is essential for optimizing the supply-demand balance of shared bikes and enhancing urban residents’ travel convenience. To address the issues of low prediction accuracy and insufficient capture of spatiotemporal characteristics in bike-sharing flow prediction, a hybrid convolutional-recurrent neural network (Conv3D-GRU) model was proposed. Using Chicago’s 2022 full-year bike-sharing data, experiments were conducted, and the results were compared with those of the 3D convolutional neural network (3D-CNN) model and the convolutional long short-term memory (ConvLSTM) model. The model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Experimental results show that compared with the 3D-CNN and ConvLSTM models, Conv3D-GRU is improved by 3.25%, 4.90%, 1.14% and 11.94%, 13.70% and 2.46% on RMSE, MAE and R², respectively. This demonstrates that the Conv3D-GRU model has lower prediction errors and higher prediction accuracy, making it an effective and reliable approach for forecasting bike-sharing inflow and outflow.

The Permian Wutonggou Formation in Dixi area of Junggar Basin has huge potential for oil and gas exploration. Based on the latest seismic data, combined with thin section data, drilling and logging data, rock physical properties and physical parameters, using seismic forward modeling, wave impedance attributes and waveform clustering attributes, the basic characteristics of the reservoir were characterized, the seismic waveform identification method of the upper and lower sand groups in the first member of Wutonggou Formation was clarified, the thickness distribution law of the upper and lower sand groups was described, and the sedimentary facies development characteristics of the upper and lower sand groups were clarified. The results display that the sandstone of Wutonggou Formation is mainly lithic sandstone and feldspar lithic sandstone, which belongs to low porosity and low permeability reservoir. The seismic waveform characteristics of sand bodies are significantly affected by seismic resolution, sand body thickness, mudstone interlayer thickness, sand body superposition relationship and underlying lithology. The sand body distribution regular pattern based on the interpretation of the sand body waveform characteristics of the forward model is highly consistent with the average wave impedance attribute distribution regular pattern. The study area develops delta front underwater distributary channel microfacies, estuary dam microfacies, underwater tributary bay microfacies and sheet sand microfacies. There are great differences in the development characteristics of sedimentary facies between the upper and lower sand groups.

In the absence of accurate transit demand information, a demand responsive transit(DRT) route planning method based on taxi trajectory data was proposed to predict the “potential demand” of demand responsive transit and provide a feasible plan for route planning before transit operation. Firstly, taxi trajectory data in the study area was obtained through data mining, representing the “potential demand” for passenger travel in the area, and candidate station were determined using the K-means clustering algorithm. Secondly, a benchmark station network was established using these candidate station, with edge benchmark stations designated as the starting and ending points of routes. Utilizing the K-shortest pathes(KSP) algorithm constrained by route length, benchmark chains were generated. Finally, after determining the sub-chain set of the benchmark chains, demand response stations within each sub-chain were searched based on circumferential critical value constraints. Using this algorithm, alternative routes were generated repeatedly within specific time periods, and an initial optimal route was selected based on comprehensive evaluation indices for each alternative route.

With its advantages in cooling efficiency and cost, the cold channel closed system is more and more used in the construction of new data centers. However, the closed channel system will turn the originally open channels between data center cabinets into narrow spaces with restricted ventilation. When the main combustibles in the data center catch fire, the accumulated hot smoke and gas in the closed channel cannot be timely discharged, seriously threatening the safety of data center equipment and personnel. At present, there is a paucity of experimental data and theoretical basis for the fire hazard of cables commonly used in data centres. Pyrosim software was used to establish a full-size physical model of the data center room in the cold closed channel, and fire dynamics simulator(FDS) software was used to establish a full-size fire model to simulate different fire source locations, so as to analyze the changes of fire parameters such as smoke spread rate, visibility and temperature distribution. The results show that when a fire occurs under the floor (maximum heat release rate reaches 2 000 kW), the smoke would fill the whole machine room more quickly due to the influence of the special air conditioning airflow and floor than the cabinet fire. The visibility at the safety exit measuring point reached 0 m, 60 s earlier than the fire at the inside of the cabinet. At the same time, affected by the air conditioning airflow and perforated tiles, the temperature at the inside of the cabinet quickly reaches the critical value where the fire hazard is much greater. The results could provide important theoretical support for the fire protection system design of the cold channel of closed data center.