In the ASIST system, data on 86 917 abnormal events from 2017 to 2023 are collected as research objects, and an indicator system for abnormal events was established. To ensure the safety of aviation operations, accurate and reliable risk assessment models were developed to analyze abnormal events in depth, thereby achieving effective risk management. Firstly, the principle of catastrophe theory was introduced into the fuzzy inference system, which enables it to better handle complex issues and enhance the accuracy of evaluations. Then, a risk assessment model based on catastrophe theory and fuzzy inference system was developed to assess the risks of abnormal aviation events. Additionally, 56 cases with detailed background information records were selected for instance analysis, and compared with the cloud model, to verify the feasibility and accuracy of the model. Finally, relevant indicators were controlled using fuzzy methods, providing guidance for the safety management work of aviation operations.

In order to advance the analysis and mining of TCM(traditional Chinese medicine) text data and achieve intelligent extraction and processing of knowledge, the BIO(begin, inside, outside) sequence labeling method, the BiLSTM-CRF model, and manually defined rules were adopted to complete the knowledge extraction task. Utilizing the Py2neo library in Python 3.6 and the Neo4j database, a spleen and stomach disease knowledge graph was constructed based on Neo4j, and a TCM spleen and stomach disease named entity recognition system was developed using the Flask framework. The results show that the BiLSTM-CRF model achieves high performance and good generalization ability on the test set, with accuracy, precision, recall, and F₁ scores of 96.19%, 86.64%, 88.82%, and 87.71%, respectively. The constructed knowledge graph includes eight types of node labels, such as prescriptions or patent medicines, Chinese medicines, and clinical manifestations, as well as ten types of relationships. It supports the querying and discovery of nodes and relationships among Western medical diagnosis, TCM syndromes, and TCM treatment principles for spleen and stomach diseases. It is concluded that the BiLSTM-CRF model demonstrates excellent generalizability in named entity recognition of TCM spleen and stomach disease. It exhibits outstanding performance in handling complex text structures and domain-specific terminology, providing strong support for the research on knowledge extraction and knowledge graph construction in Traditional Chinese Medicine for spleen and stomach diseases.

In order to improve the prediction accuracy of lithology affected by imbalanced geological data, an ECA-MSCB ResNet model was proposed. The model integrates ECA (efficient channel attention) and MSCB (multi-scale convolutional block) into the traditional ResNet architecture to achieve efficient extraction and representation of lithological data features. For the issue of imbalanced lithology categories, prior probability-balanced logit bias was introduced during model training, and the focal loss function was modified to enhance the recognition of minority lithology classes. Experimental results show that the model based on ECA-MSCB ResNet performs well on the imbalanced geological lithology dataset, achieving an average prediction accuracy improvement of approximately 7.45% compared to the original ResNet model and 27.33% compared to the random forest method. Notably, the recognition of minority lithology classes improves by an average of 17.9%. Furthermore, the model demonstrates strong lithology classification ability on public datasets, achieving an F₁-score of 75.77%. In addition, the recognition accuracy of the proposed model outperformed both traditional and mainstream methods. The ECA-MSCB ResNet method holds significant application value in the field of imbalanced geological lithology recognition.

The external human-machine interface is used to enhance communication between autonomous vehicles and road users like pedestrians and cyclists, thus traffic safety and user experience are improved. The recognizability of the external interface is considered the foundation for ensuring effective and understandable signal functions, and it is explored to ensure pedestrians crossing safety. The form of information expression, interface location, and the speed of autonomous vehicles were considered as independent variables, and eye-tracking technology was used to collect eye movement and behavioral data. The identifiability of the interface was evaluated through repeated measures analysis of variance and logistic regression. The results show that the form of interface information, location, and vehicle speed significantly affect identifiability. The light band has the best identifiability; higher recognition efficiency is observed when the vehicle is traveling at a low speed; and the highest recognition efficiency is found when the interface is at P3, while the lowest is noted at P1 and P4.From the perspective of enhancing pedestrian traffic safety, this study provides a reference for the design of the external interface of autonomous vehicle while driving, and helps to improve pedestrian attention and recognition accuracy of the external interface.

As the operational carrier of civil aviation transportation network, the air route network undertakes the important task of ensuring the safe and efficient operation of aircraft. When important waypoints fail due to thunderstorm disturbances, it is easy to chain reaction to adjacent nodes, ultimately leading to a significant decrease in network performance. Aiming at the problem that existing complex network node importance evaluation models do not effectively consider thunderstorm disturbances, the characteristics of thunderstorm disturbances were incorporated into the waypoint importance evaluation system for thunderstorm weather scenarios. The evaluation indicators were weighted using game theory methods, and the TOPSIS(technique for order preference by similarity to an ideal solution) comprehensive evaluation method was improved based on gravity model theory. A node importance evaluation model based on game theory improved TOPSIS method was established, and the K-medoids algorithm was then used to achieve waypoint clustering and grading. Taking flight operations in the Beijing-Tianjin-Hebei region as an example, the importance of air route network nodes in thunderstorm weather scenarios was evaluated. The results show that within the Beijing-Tianjin-Hebei route network, route points in the southern region are more susceptible to thunderstorm weather and are more densely distributed. The route network contains 9 important route points. When important route points in the route network fail due to thunderstorm impact, it will have a significant negative impact on the performance of the route network. The proposed node importance evaluation model based on game theory-improved TOPSIS method can effectively identify important waypoints in the route network during thunderstorm seasons or areas with high thunderstorm incidence, providing effective basis for optimizing the route network structure and resource allocation in thunderstorm scenarios.

To investigate the mechanism of lncRNA (long chain non coding RNA) Rpph1 activating cytopyrosis through AMP-AMPK(activated protein kinase)/Nrf2(nuclear factor E2 related factor 2) signaling pathway, then to promote podocyte injury in DN (diabetes nephropathy). HGPC(Human glomerular podocytes) were cultured in vitro and randomly divided into control group, model group, lncRNA Rpph1 over-expression group, low-expression group, and empty vector group. HGPC were incubated with 5 mmol/L D-glucose as control group, while the other three groups were incubated with 30 mmol/L D-glucose to establish DN model. Liposome transfection method was used to co-incubate stable plasmids carrying Rpph1 over-expression, low-expression, and empty vector with HGPC. qRT-PCR was used to detect lncRNA Rpph1 expression, Western blot was used to detect p-AMPK/AMPK and Nrf2 proteins, as well as the expression levels of cytopyrosis related proteins including NLRP3(Nod like receptor thermal domain associated protein 3), caspase-1, and GSDMD-N. MTT assay was used to detect cell survival rate. Flow cytometry was used to detecte apoptosis rate. Compared with control group, the expression level of lncRNA Rpph1 in model group significantly increased (P<0.05). The expression levels of p-AMPK/AMPK, Nrf2, NLRP3, caspase-1, and GSDMD-N proteins significantly increased in model group (P<0.05). The survival rate of model group cells significantly reduced, while apoptosis rate increased in model group (P<0.05). Compared with model group and empty vector group, lncRNA Rpph1, p-AMPK/AMPK, Nrf2,NLRP3, caspase-1, and GSDMD-N proteins in lncRNA Rpph1 over-expression group significantly increased, and cell survival rate significantly reduced, apoptosis rate increased (P<0.05). The expression levels of lncRNA Rpph1, p-AMPK/AMPK, Nrf2, NLRP3, caspase-1, and GSDMD-N proteins significantly decreased in lncRNA Rpph1 low-expression group, and cell survival rate significantly increased, apoptosis rate reduced (P<0.05). In all, High expression of lncRNA Rpph1 in DN may activate cytopyrosis and promote podocyte injury by AMPK/Nrf2 signaling pathway.

Microwave assisted cutter breaking has a strong application prospect, in order to deeply analyse the auxiliary effect of microwave irradiation on cutter breaking, the TBM scale cutter breaking test after microwave irradiation was carried out. Firstly, the rock was irradiated by microwave using different parameters, and the influence law of different microwave parameters on the surface temperature of the rock was studied. After the rock is back to room temperature, the damaged rock was taken as the basis to carry out the TBM scale cutter rotary rock breaking experiment, to study the influence of microwave irradiation time and power on the cutter thrust, cutter torque, rock ballast weight, cutter wear and the specific energy of rock breaking. The results show that: with the increase of microwave irradiation time and irradiation power, the rock surface temperature increases, the rock heating rate increases, the highest temperature of the rock surface is 172.6 ℃. The cutter thrust is fluctuating in the breaking rock, the rock below the cutter is crushed to powder, the cutter side produces block ballast. With the increase of microwave irradiation time and microwave power, the cutter thrust decreases, the disc torque decreases, the weight of the rock ballast increases, the amount of cutter abrasion decreases, and the specific energy of rock breaking decreases. The best microwave irradiation time should be more than 40 s and the microwave power should be more than 7 kW for the test of Chifeng basalt, which provides a certain experimental basis for the microwave-assisted cutter rock-breaking.

Based on the actual needs of large drilling spacing, and frequent long-distance relocation of drilling rigs in coal mine underground construction, as well as the need for remote control function, a research idea of compact layout and modular design of each unit of rubber wheel directional drilling rig was adopted to solve key technical development problems, such as independent walking rubber wheel chassis, multi power output units, hydraulic system, and electrical control system. After whole machine was processed and assembled, load testing was simulated on the drilling performance test bench of the National Safety Production Inspection Center. The experiment shows that all functional parameters of the drilling rig meet the design requirements. The ZDY3500JDK rubber tyre directional drilling rig developed for coal mine meets demand for long-distance autonomous relocation underground, greatly improving the production and transportation efficiency of mine, and providing reliable equipment support for drilling construction operations in large and medium-sized mines with trackless rubber tyre transportation.

The growing sophistication of deepfake speech poses significant security threats to ASV(automatic speaker verification) systems. Current anti-spoofing models based on CNNs(convolutional neural networks) are constrained by inadequate global feature extraction and limited generalization capability against unseen spoofing attacks. To address these challenges, a novel network architecture integrating CT-DSCNet(channel-temporal attention mechanisms with depthwise separable convolutions) was proposed. Building upon the RawNet2 framework, the developed model incorporates dual-domain attention modules to enhance discriminative feature representation while suppressing irrelevant acoustic artifacts. Furthermore, depthwise separable convolutional residual blocks were strategically implemented to optimize computational efficiency and real-time processing capabilities. Comprehensive evaluations were conducted across three benchmark datasets: ASVspoof2019 LA, ASVspoof2021 DF, and FMFCC-A. Experimental results demonstrate state-of-the-art performance with EER(equal error rate) of 1.53% on ASVspoof2019 LA, representing a 70.58% relative improvement over baseline systems. Notably, the proposed architecture exhibits superior cross-dataset generalization, achieving a 25.35% lower EER on the FMFCC-A evaluation set compared with conventional approaches. These findings validate the effectiveness of the hybrid attention-convolution design in advancing spoofing detection robustness and domain adaptability.

The flow pattern and its characteristics in the flood discharge stilling basin with step-down floors play dominant roles in effectively dissipating the discharged flooding water energy, guaranteeing the safety of the hydraulic structure and its downstream river bank stability as well as navigable flow conditions. A three-dimensional numerical model for water-air two-phase flows with strong nonlinearity, involving large free surface deformation and complicated solid-wall boundary conditions was established, and was applied to analyze flow characteristics in Xiangjiaba flood discharge stilling Basin with step-down floors. RANS(Reynolds-averaged Navier-Stokes) equations, Realizable k-ε turbulence model and VOF free surface tracking method were used in the developed numerical model. The model was firstly validated through comparisons of the simulated results and measured data for the flow patterns as well as the time-averaged pressure at the dam surface induced by the flood discharge jet. It is then applied to simulate the 3-D flow structure and fluctuation characteristics in the stilling basin with step-down floors induced by flood discharge from dam under the same discharge amount but different flood discharge scenarios. The results show that, for the flood discharge stilling basin with high and low step-down floors,under the same flood discharge condition, different scenarios of dam discharge gate open mode significantly affects the three-dimensional structure and characteristics of discharged flow in the dissipative pool induced by flood jets. The combined discharge from dam surface and middle holes results in the strong turbulent mixing of submerged multi-layer and multi-jet flows, effectively dissipating the energy of the discharged water and reducing the water surface fluctuations. The established numerical model can better reproduce the high-speed flooding jets and its turbulent motion in the dissipative pool associated with flood water discharge.