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.

As an innovative new method of natural gas hydrate extraction, the recovery of hydrate particles determines the efficiency of this method and is one of the key technical links. However, due to the limited hydraulic suction, the recovery rate of hydrate particles is low. Therefore, in order to improve the recovery performance of hydrate particles, a double jet recovery scheme was innovatively proposed. In order to explore the efficiency of solid particle recovery under different working conditions, experimental research was carried out, and the recovery flow field and particle recovery rate were studied by numerical simulation. The results show that with the increase in the distance between the front-end jetting and the recovery hole (l₁) and the distance between the back-end jetting and the recovery hole (l₂), the flow field effect between the double jets weakens, resulting in the particle deposition phenomenon becoming more obvious and the number of sand piles changing from 2 to 3. When the distance between the back-end jetting and the recovery hole (l₂) is 300 mm, the particle recovery increases first and then decreases with the increase in the distance between the front-end jetting and the recovery hole (l₁). When l₁ = 300 mm, the recovery increases first and then decreases with the increase of l₂. The results further enrich the mechanism of hydrate mining and help optimize the design of mining tools.

In rock image recognition, achieving rapid and accurate identification of rocks is crucial for the digitalization of rocks. Among the challenges faced in intelligent rock recognition is the issue of image blurring caused by environmental factors such as lighting and humidity. In light of this, a novel deep learning approach (MobileNetV3-small-RegNetX) was proposed for rock image recognition, which is suitable for scenarios with limited resources such as mobile devices. Building upon the RegNet network, transfer learning methods, combining the advantages of the MobileNetV3 residual structure with squeeze-and-excitation (SE)modules was employed to effectively optimize feature extraction and network structure, leading to a significant improvement in detection speed. To validate the accuracy of this approach, comparative experiments were conducted between the new model and current mainstream lightweight models (DenseNet and ShuffleNet). The results demonstrate that the new model proposed exhibits high precision (82.15%) and fast processing (0.06 GFLOPs). Additionally, the model demonstrates good adaptability to environmental factors such as lighting and humidity-induced image blurring.

In order to solve the problems of excessively long droplet break time and excessively large droplet length to diameter ratio in the production of high-temperature and high-viscosity temporary plugging agent, the minimum velocity of droplet forming of temporary plugging agent was obtained by theoretical calculation, and the formula for calculating the optimal disturbance period was derived. The numerical simulation method was used to analyze the droplet forming process of temporary plugging agent, and the changes of the flow field of the droplet forming under the action of no disturbance and external square wave disturbance were explored. The simulation results show that without disturbance, the jet is difficult to break into droplets within 300 mm, the jet temperature is basically unchanged within 0.5 s, and the jet velocity increases to 2.13 times of the initial velocity. When square wave disturbance is added to the outside, too short disturbance period is not conducive to uniform droplet forming, and too long disturbance period will lead to excessive elongation of liquid column before fracture. When the disturbance period is about 0.11 s, the droplet forming efficiency is the highest, the fracture frequency is stable at 0.11 s, and the final droplet length-diameter ratio is stable at about 2. The research results provide a basis for the selection of process parameters for droplet forming of high viscosity temporary plugging agent.

Landslide geological hazard susceptibility assessment is an important means of hazard prevention and reduction. The selection and optimization of susceptibility assessment model is very important. Sinan County was selected as the study area, and 16 assessment factors such as elevation, slope, curvature, lithology, land use, and average annual precipitation were selected. Frequency ratio (FR) model was coupled with support vector machine (SVM) model and random forest (RF) model. Grid search method was introduced to obtain the optimal parameter combination of SVM model, RF model and their coupling model for model training. Finally, SVM, RF, FR-SVM and FR-RF models were constructed to predict landslide susceptibility in the whole study area, and receiver operating characteristics (ROC) curve was performed verification. The results show that compared with the single machine learning model, the coupled machine learning model has more landslide hazard samples fall in the high zone and the very high zone, and has higher accuracy. In the single model, more landslide hazard samples in the RF model fall in the high zone and the extremely high zone. In the coupled model, more landslide hazard samples in the FR-RF model fall in the high zone and the very high zone, and no hazard samples points in the FR model and the FR-RF model fall in the very low zone, indicating that no matter the single model or the coupled model, The performance of RF model is better than that of SVM model. The AUC values of ROC prediction curves of the four models are 0.831 6, 0.843 9, 0.864 4 and 0.910 4, indicating that the coupling model combined with FR model and RF model has a higher accuracy, and this model is more suitable for the assessment of landslide susceptibility in Sinan County. The assessment results can provide some reference for hazard prevention and reduction of local landslide geological hazards.

The experimental research on the dynamic response of aero-engine rotor under sudden impact load was carried out by using high-speed motor drive on the vibration table. Rotor dynamic response tests under different characteristic speeds, load sizes, impact directions, and pulse widths were completed, revealing the general rules of rotor dynamic response under sudden impact loads. The results show that the dynamic response of the rotor increases instantly and then returns to a stable state when under instantaneous impact, and it increases with the increase of the sudden impact load. Besides, the vertical dynamic response of the rotor is greater than the horizontal dynamic response, and the vertical response of the same measurement section is 4% to 46.15% greater than the horizontal response under various operating conditions. In addition, the dynamic response of the rotor under axial foundation impact is greater than that under vertical foundation impact. Within a certain range, as the pulse width of the impact load increases from 6 ms to 11 ms, the dynamic response of the rotor decreases by 2.5%~10%. The study provides a reference for the vibration response analysis of aero-engine rotors under sudden impact loads and the structural safety design of aero-engine, which has important engineering application value.

Aiming at the problem of white noise amplification of differential microphone array, a design method of parametric differential beamformer was proposed. Through theoretical derivation, it is proved that the delay summing beamformers can maximize the white noise gain and the superdirected beamformers can maximize the directional gain. The orthogonal eigenvector was obtained by using the unitary diagonalization method to deal with the pseudo-correlation matrix between the steering vector and the white noise gain, and the parameters of the beamformer machine were designed based on it. Through simulation experiments, the performance of parametric differential beamformers under different parameter settings was analyzed. Experimental results show that the proposed method can flexibly balance and adjust the white noise gain and directional gain by adjusting the parameters.

With the development of global trade, buoyancy-lifting hybrid airship is an important choice for global long-distance and large-load transportation, it has gradually become a research hotspot at home and abroad. In order to improve the transportation efficiency of buoyancy-lifting hybrid airship, its layout and parameter sensitivity were studied. Discussions were conducted on the hull layout and tail wing layout, and several types of hull and tail wing layouts with high lift to drag ratios were proposed. At the same time, a sensitivity analysis was conducted on the impact of design parameters on aerodynamic characteristics. The results indicate that the width of the hull has the most significant impact on the lift coefficient, drag coefficient, and maximum lift to drag ratio of airships. Its relative sensitivity coefficient is nearly ten times that of the longitudinal position and inclination angle of the tail wing. The inclination angle of the tail wing has the strongest impact on the torque coefficient of the airship, and its relative sensitivity coefficient is more than twice the width of the hull.

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.

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%).