Fire flooding is a primary method used to enhance heavy oil recovery, often replacing steam stimulation. However, it encounters challenges such as low sweep efficiency and delayed effective times in heavy oil development. A new fire flooding PGI (pulse gas injection) technology was used to solve these problems. The combustion sweep effect of fire flooding can be improved by adjusting the working system of the gas injector. Based on the geological characteristics of the Hongqian 1 heavy oil reservoir in Xinjiang, the feasibility and mechanism of PGI were elucidated through numerical simulations. The influence of geological and engineering factors was studied on the development effect of PGI, and the consequences are applied to the fire flooding field. The combustion front can be controlled by the peak-valley value stage of PGI, accelerating the uniform movement of the combustion front and improving the oil displacement efficiency. Specifically, PGI can reduce the effective time by approximately 600 days and increase combustion sweep by more than 30%. This technique is particularly suitable for medium heterogeneous reservoirs with a vertical permeability contrast of less than 15 and crude oil viscosities ranging from 2 000 to 10 000 mPa·s. The optimal pulse amplitude ranges between 1.5 and 2, with a recommended step length of 30 days. When applied to the Hongqian 1 fire flooding industrial area in Xinjiang, daily oil production increased by 0.5 to 2.8 t for the well group, and the air-oil ratio decreased by 34%. PGI can achieve a better production increase effect for field production.

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.

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.

The existing CFRP(carbon fiber reinforced plastic) plate clip-type anchorage exhibits arching deformation during the tensioning process due to compressive forces on the inside wall of the anchor cup. This causes “voids” at the contact interface between the anchor cup and the clip, leading to an uneven distribution of lateral forces within the CFRP plate anchorage section. The sides of the CFRP plate are prone to cracking failure due to stress concentration. An optimized design for arching clip-type CFRP plate anchors was proposed to address this issue. Finite element numerical simulations and static loading tension tests were conducted on clip-type anchors with varying arch heights. The findings show that the primary failure mode of conventional CFRP plate clip-type anchors is initial cracking followed by fragmentation, with an anchoring efficiency of only 68.75%. When the arch height is low, the voids in the anchor cup are not adequately filled, resulting in lower compressive stress and an anchoring efficiency of 56.67%. When the arch height is too high(0.5 mm), stress concentration occurs in the middle section of the CFRP plate anchorage, which increases the anchoring efficiency to 81.25%, but this is still suboptimal. Notably, when the arch height is set to 0.25 mm, anchoring efficiency increases to 90.83%, and the failure mode shifts to explosive failure, indicating that the CFRP material has been fully utilized. The rational adjustment of the clip’s arch height effectively prevents cracking failures in the CFRP plate anchorage due to voids, demonstrating the significant engineering application value of this research.

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.

In the exploration and exploitation of oil and gas, artificial intelligence models are extensively employed in the prediction of formation pore pressure. Among them, single models tend to encounter problems such as overfitting or unstable prediction outcomes, leaving room for improvement in aspects like prediction accuracy and generalization ability. To enhance the prediction accuracy of formation pore pressure, a CNN-Attn neural network-based formation pore pressure prediction model was established by virtue of deep learning technology. In this research, five types of logging and while-drilling data were optimally selected, and the linear correlation between the data and formation pore pressure was verified using the Pearson correlation coefficient method. Through the optimization of the structure of the one-dimensional CNN, the model can effectively capture the local characteristics of the data and, when combined with the self-attention mechanism, strengthen the model’s ability to capture global dependencies, thereby elevating the model’s expressiveness and comprehension. To validate the prediction accuracy of this model, two wells in the Bayan block were subjected to prediction. The average absolute errors of the prediction results were both less than 1 MPa, the root mean square errors were both less than 1 MPa, the average relative errors were both less than 1.3%, and the determination coefficients were both greater than 0.9, with higher accuracy compared to the BP, CNN, and LSTM models. This model has improved the prediction accuracy of formation pore pressure and provided data support for drilling safety.

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.

Oil transfer station plays a crucial role in the oil and gas gathering and transportation system of an oilfield, ensuring stable production and continuous supply of oil and gas. However, given the complexity of its process system and the ambiguous uncertainty surrounding fault modes and relationships, a systematic reliability assessment method integrating T-S fuzzy fault trees with BNs(Bayesian networks) was proposed. Firstly, a T-S fuzzy fault tree was established based on T-S gates and their descriptive rules, which is subsequently converted into a Bayesian network model. Secondly, leveraging limited fault samples and general data sources, Bayesian updating estimation was employed to determine the failure rates of basic events, addressing the uncertainty inherent in fault sample data. Lastly, the T-S fault tree and BN model were synergistically utilized for forward reasoning to predict the reliability of the process system and the contribution of basic events, while reverse diagnosis is conducted to pinpoint the key factors causing different fault states of the system. Research conducted on typical oil transfer station process systems has demonstrated that the proposed method can effectively predict system failure rates and diagnose weak links even under conditions of uncertainty in basic data and event relationships. This provides crucial decision support for the optimal design and reliability maintenance of complex oil and gas process systems.

Located in the south of the Central Asian orogenic belt, the southern Beishan belt in Gansu Province is a key area for studying the tectonic evolution of the Central Asian orogenic belt. Its late Paleozoic tectonic setting has been controversial for a long time. In order to further explore the late Paleozoic tectonic evolution of the southern Beishan belt,the geochronology and geochemical characteristics of the Changshan monzogranite body were analyzed. The analysis results show that the LA-ICP-MS zircon U-Ph weighted average age of the Changshan monzogranite is (291.1±1.5) Ma, and the emplacement of the plutons occurred in the early Permian. Geochemical datas show that the plutons are high potassium calc-alkaline and peraluminous series rocks. The results show that SiO₂ ranges from 72.07% to 72.94%, K₂O ranges from 4.93% to 5.10%, and the contents of K₂O>Na₂O, A1₂O₃ ranges from 13.52% to 13.97%. The curves of chondrite-normalized REE are obviously right inclined, and the LREE are relatively enriched (LREE/HREE are 10.96~14.98), δEu are 0.78~0.92, with weak negative Eu anomaly. Trace elements are relatively enriched in LILE (large ion lithophile elements), depleted HFSE (high field strength elements), and significantly depleted in high field strength Elements Nb, Sm, Y. According to the regional tectonic setting, petrological and geochemical characteristics, the Changshan adamellite plutons are considered to be the product by post-collisional magmatic activity, reflecting the completion of the collision collage on the southern margin of the Central Asian orogenic belt in the early Permian.

The voltage fluctuations in the high-voltage DC bus of the train traction converter have a significant impact on the output power quality of the traction system. Therefore, it is necessary to improve the response speed of the intermediate stage isolated DC/DC converter to reduce the power coupling between the high-voltage stage and the low-voltage stage. Taking the isolated DC/DC converter as the research object, an unbiased model predictive control and sampling noise suppression strategy was proposed to address its inherent problems of high sensitivity to circuit parameters and susceptibility to sampling noise. Firstly, the operation principle of the dual-bridge series resonant converter and the causes of the steady-state errors were analyzed, and a feedback correction method based on recursive least square algorithm was designed to eliminate the steady-state error. Then, the introduction of noise suppression coefficient reduces the sensitivity of the control variable to the control target through a simple and effective method. Furthermore, the virtual current was utilized in predictive model instead of the actual current sampling value, and it further reduces the system costs. Finally, an experimental platform was built to verify the improvement of the proposed strategy in both steady-state and dynamic performance.