For the group decision-making based on generalized grey target with mixed attributes, a group information aggregation method based on an improved power-weighted averaging operator was proposed. This method takes into account the interrelationships among decision-makers and among attributes, reducing the distortion of uncertain information and simplifying the computational process. First, the mixed attribute data are uniformly measured and transformed. Then, the comprehensive weighted G-S(Gini-Simpson) index was calculated by evaluating each expert’s values relative to the target centers of all experts, to determine the objective weight of experts. Next, the differences among experts are further calculated by the comprehensive weighted G-S index. Finally, a novel information aggregation method was constructed based on the proposed improved power-weighted averaging operator to aggregate group decision-making information with mixed attributes. The effectiveness and feasibility of the proposed method are verified by a case analysis.

Low level wind shear is an important factor affecting aircraft flight safety. Based on the observations from a three-dimensional scanning wind lidar at Baiyun Airport, Guangzhou during March 2023, the measurements from the wind lidar were preprocessed firstly. Then, TSSI (two-step identification method for wind shear) was proposed, which combined TDSI (two-dimensional synthetic wind shear identification) method with an adaptive window and the temporal wind shear identification method. The wind shear results recognized by the TSSI and TDSI methods were compared, and the evolutions of wind shear were analyzed. The main conclusions are as follows. Data preprocessing effectively removes isolated points and radial fluctuations observed by wind lidar, and fills in the missing data. The TSSI method is conducive to early warning of wind shear. During the observation period at Baiyun Airport, Guangzhou in March 2023, a total of 25 wind shear processes are identified by the TSSI. Among them, 21 cases are warned ahead of the TDSI, with an average warning time of 3~5 minutes, and TSSI also has a good alarm recognition function for both time and space dimension wind shear. Most of the identified wind shear processes occur around noon (e.g. 11:00-15:00) and last for about 15 minutes. The wind shear position is greatly influenced by the background wind field. The TSSI method proposed in this study can identify low-level wind shear earlier and more comprehensively, which is helpful to improve the accuracy of wind shear warning and provide guarantees for aircraft flight safety.

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 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 accuracy of subtle fault identification, an artificial intelligence subtle fault prediction method based on the seismic data was developed. By making sample labels based on subtle fault interpretation results, a sample label library based on interpretation results was built. The subtle fault modeling and forward methods for subtle faults were developed, and a label library based on model forward was built. The special neural network for identifying subtle faults was developed, which can directly generate attribute data for subtle faults. In addition, the seismic preprocessing approaches such as removing strong seismic events and structure oriented smoothing filtering were added to improve the original seismic data. Multi-attribute fusion based on principal component was used to reflect multi-scale faults. Finally, the prediction results were verified through three steps, forming the subtle fault prediction workflow with artificial intelligence. This study demonstrates good application in the Daniudi gas field in the Ordos Basin. Compared with conventional attributes, the number of subtle fault identification has increased by 30%, and the resolution and continuity of the subtle faults have significantly improved. The prediction results are consistent with seismic data, well data, and conventional seismic attributes. Based on the subtle faults identification, the new understandings of regional structure are revealed: the dominant orientation of subtle faults is northwest, the most subtle faults are relatively concentrated in the western and northeastern parts of the survey, and the fractures between faults are also very dense. Some subtle faults are distributed along the boundary of the high-quality reservoir, perhaps related to the development of reservoirs. The prediction results contribute to evaluating high-quality reservoirs in the Lower Paleozoic and well deployment, and have application prospects for similar areas.

The deep-sea is rich in polymetallic nodules resources that are promising for mining, but the process of mining poses a number of environmental problems, in particular the migratory diffusion of plumes formed by sediments disturbance. To evaluate the environmental impact of sediment setting behavior during polymetallic nodule mining, seafloor surface sediments and seawater from the Beijing Pioneer polymetallic nodule mining area in the northwestern Pacific Ocean were employed. Sedimentation experiments were conducted to investigate the deposition characteristics of the suspended sediments. The results indicate that the deep-sea sediments are primarily composed of viscous particles, with median particles sizes ranging from 2.57 to 4.47 μm, and maximum particle size of 66.9 μm. The relationship between settling velocity and particle size was analyzed, showing that the settling velocity of viscous particles increased due to flocculation effects. After 5 hours of sedimentation, the median particle size of suspended sediments decreased to below 0.1 μm, and the maximum optical density has observed at 14 minutes after the onset of sedimentation. A positive correlation is observed between the mass concentration and optical density of the suspension. By fitting the temporal variation of sediment mass concentration, the settling pattern is determined. The mass concentration reached its peak 14 minutes after the onset of settling, and decreased to 2.46% of the initial value after 5 hours, indicating that 97.54% of the total mass has settled. These findings provide supporting data for environmental impact assessments related to plume behavior during deep-sea polymetallic nodule mining.

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.

The effects of different diversion schemes on air flow and heat transfer in a countercurrent drying tower were studied by numerical simulation of maize drying process based on porous medium model. The influence of the angle box arrangement on the temperature field and velocity field in the tower was studied by numerical simulation and experiment. The results show that the cross arrangement of corner boxes can improve heat and mass transfer efficiency, reduce heat loss and solve the problem of uneven drying. At the same time, the increase of the inlet speed can also improve the uneven temperature distribution and improve the drying effect. It can be seen that the two key factors, the arrangement of the corner box and the inlet speed, should be fully considered in the design of the counter-current drying tower for corn drying. The optimization of drying tower structure can improve the overall drying efficiency and corn quality. The conclusion of this paper provides a useful reference for corn drying industry to reduce the cost and improve the quality of corn.

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.

Underground coal mining faces hazards like gas explosions, coal dust explosions, and fires, underscoring the need for safety robotics based on 3D reconstruction. While essential for precise navigation and detection in complex environments, traditional methods fall short in data quality, accuracy, and cost. To address this, a 3D reconstruction method called 2DGS-DbTrans for underground coal mine tunnels was proposed, which is based on pure vision-based 3D reconstruction technology. To improve image resolution, a Transformer module was designed to enhance the input images, consisting of two core components: the multi-head depth convolutional axis attention mechanism and the deep convolutional gated network. In the processing workflow, sparse point clouds were first generated using Colmap, and the underground mine tunnel environment was represented by 2D Gaussian surfaces, where each Gaussian surface contains the coordinates, color, size, and orientation information of the objects. In addition, two loss functions were defined: the color loss function and the road smoothness loss function. Experimental results show that the 2DGS-DbTrans method outperforms other methods in 3D reconstruction of underground coal mine tunnels.