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.

As a key equipment and a major source of energy consumption in a building, chiller plant, if it fails, it will not only affect the normal operation of the system, but also cause serious energy waste. In order to improve the reliability of chiller system operation. A multi-strategy IDBO(improved dung beetle optimization algorithm) combined with a HKELM(hybrid kernel extreme learning machine) fusion fault diagnosis model was constructed to achieve accurate diagnosis of early faults in chiller systems. The model firstly employs hybrid kernel functions to improve the learning ability and generalization of KELM(kernel-extreme learning machine). Secondly, Bernoulli mapping, adaptive inertia factor, and Levy flight fusion dynamic weight coefficients strategies were used to improve the DBO(dung beetle optimization) algorithm in order to balance the global exploration performance of the DBO algorithm. Finally, the effectiveness of the IDBO algorithm was verified by benchmark functions, and the HKELM hyperparameters are optimized using the IDBO algorithm to construct a data-driven model for early fault diagnosis of chiller units. Through relevant training simulations and experimental validation, the accuracy of the proposed IDBO-HKELM model for early fault diagnosis of chillers is improved to 99.71%, which is an obvious advantage over other algorithms.

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.

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 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.

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.

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.

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.

The two important characteristics of microseismic are: tiny and shear rupture. The resulting monitoring characteristics are significantly different from those used to monitor natural earthquakes and artificial seismic exploration sources. Microseismic and its monitoring characteristics are the cornerstone of the development, application, and judgment of microseismic monitoring methods. First, different monitoring methods were investigated, suggestions were puts forward for their development and scope of application, and the reasons why some methods have not improved much were exploved. Among them, the most important ones are: when the number of microseismic, positive and negative initial motion, and signal-to-noise ratio are not easy known, it is necessary to conduct large-scale trial calculations and statistically investigate the combination of focal mechanisms with a high probability, so as to complete reasonable migration stacking. Mathematical statistics in the denoising should be used throughout all steps of detection, and so on. From the perspective of probability and mathematical statistics, following the characteristics of microseismic and its monitoring, the results show that microseismic monitoring has to be based on the fact of low signal-to-noise ratio, summarizes and improves the principle and denoising of VS(vector scanning). In the process of VS processing and interpretation automation, a large number of mathematical statistics are implemented to confirm the noise coherence parameters and analyze the microseismic activity. It makes up also for the defect that the vertical accuracy of ground monitoring is poor and cannot confirm the vertical height of the stimulation rock volume. VS has formed a relatively complete ground monitoring system after more than 20 years of research and development. Probability and mathematical statistics are important concepts and tools to ensure the success of the development and application of microseismic monitoring methods.

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.