Since 2019, the Jiubaoyan landslide has exhibited continuous and gradual deformation. On September 17, 2021, during the rainy season, the landslide was obviously deformed and slipped due to the continuous heavy rainfall. On the basis of traditional engineering geological exploration methods such as on-site investigation, drilling and displacement monitoring, the finite element simulation method Midas GTS was utilized to simulate and calculate the seepage and displacement field of the slope under different working conditions, the landslide formation mechanism was comprehensively analyzed. Furthermore, the Fast GPU Matrix computing of discrete element method (MatDEM) was introduced to forecast the trend of the landslide sliding evolution under rainstorm working conditions. The results indicate these as follows. ① The finite element numerical simulation results are consistent with the drilling results, revealing that the sliding zone of Jiubaoyan landslide is located at the interface between the quaternary landslide accumulation layer gravel soil (${Q}_{4}^{del}$) and the mudstone of the Jurassic Suining Formation (${J}_{2}^{sn}$); ② Both finite element numerical simulation and on-site investigation suggest that Jiubaoyan landslide is a multi-level shallow soil landslide mainly driven by push forces and secondarily propelled by traction forces. The sliding mass is divided into upper and lower parts, which are closely interconnected. The loose soil structure in the landslide area as well as the abundant water storage ahead and behind the slope serves as material source for the landslide formation and annual heavy rainfall during the rainy season acts as critical external triggers for landslides.③ The finite element numerical simulation results reveal that Jiubaoyan landslide remains relatively stable under the natural conditions but transitions to an unstable state under the rainfall conditions. It is possible to lead to large displacement landslide under the persistent extreme rainstorms. ④ The discrete element simulation results suggest that the slope is in an destabilized state under extreme rainfall conditions, with two parts of sliding mass are penetrated through two sliding zones. This further landslide instability may culminate in significant displacement landslide, resulting in considerable economic and human losses. ⑤ The research method combining finite element analysis with discrete element analysis not only corroborates on-site investigation, drilling, and monitoring, but also enables a quantitatively analysis of the formation mechanism and prediction of potential working conditions in the future. It is hoped that such a study can offer some useful references for studying similar multi-level landslide disasters in mountainous regions.

Recent studies indicate that for early-stage non-small cell lung cancer(NSCLC) classified as T1N0M0, sublobar resection offers long-term outcomes comparable to lobectomy. However, these early-stage patients may still experience pleural invasion, which is associated with poor prognosis. It is necessary to compare the long-term efficacy of sublobar resection versus lobectomy in patients with T≤3 cm N0M0 NSCLC accompanied by pleural invasion. Research data were sourced from the SEER(Surveillance, Epidemiology, and End Results) database. Patients diagnosed were from between 2010 and 2020 with T≤3 cm N0M0 NSCLC and pleural invasion. Patients were divided into sublobar resection and lobectomy groups, and their cancer-specific survival(CSS) and overall survival(OS) were compared. Univariate analysis post-matching reveale no significant differences in CSS and OS between the sublobar resection and lobectomy groups. Multivariate analysis also indicate that the surgical approach is not an independent prognostic factor for CSS(HR=1.185, 95% CI: 0.745~1.885, P=0.472) and OS (HR=1.171, 95% CI: 0.869~1.577, P=0.299)in patients with T≤3 cm N0M0 NSCLC and pleural invasion. Subgroup analyses show no significant differences in CSS and OS between the two groups across various subgroups. Competing risk model multivariate analysis also demonstrate no significant difference in lung cancer-specific mortality between sublobar resection and lobectomy. In conclusion, for patients with T≤3 cm N0M0 NSCLC accompanied by pleural invasion, sublobar resection offers long-term survival outcomes comparable to lobectomy and can be considered a viable surgical option for this patient population.

A new amidoxime small molecule compound was synthesized by oximation addition reaction and the uranium decorporation was evaluated. The changes of endogenous metabolites caused by Uranium decorporation in animals were investigated by metabonomics method, and the related differential metabolites were searched for and their metabolic pathways and mechanisms were explored.The mice were divided into blank group (NG), model group (MG), 0.42 mmol/kg ZnNa₃-DTPA group(YG), 0.21 mmol/kg amidoxime group (CN) and 0.42 mmol/kg amidoxime group (EN), and were injected with positive drug (ZnNa₃-DTPA) and amidoxime compounds in tail vein immediately after the tail vein injection of uranyl acetate and amidoxme compound,the uranium content in the kidney and femur of mice was determined by Inductively-coupled plasma mass spectrometer(ICP-MS) 24 h later. The metabolites in the serum of each group were identified by GC-MS(gas chromatograaphy-mss spectrometer), and screened as potentially differentiated metabolites by orthogonal partial least squares discriminant analysis (OPLS-DA) with variable importance in the projection (VIP) > 1, mass spectrometry database and MetaboAnalyst platform were used to analyze the differential metabolites and their associated pathways. The results show that compared with model group, 0.42 mmol/kg amidoxime compound group decreased uranium content in kidney and femur by 61.70% and 54.74%, and the positive group at the same dose reduced the uranium content in kidney and femur by 60.70% and 40%, respectively.The results indicated that the small molecule compound of aminoxime had significant uranium decorporation. Metabolomic analysis showed that the metabolic profile of the amidoxime group was significantly different from that of the model group, which was closer to that of the normal group than that of the positive group. A total of 14 different metabolites were found after screening, and the enrichment analysis of metabolic pathways showed that the metabolic pathways related to them were mainly tyrosine metabolism. Biosynthesis of phenylalanine, tyrosine and tryptophan, metabolism of glycine, serine and threonine, etc. The small molecule amidoxime compound has a remarkable uranium decorporation effect, which is better than ZnNa₃-DTPA, and has a protective effect on kidney injury caused by uranium.

A hybrid algorithm (IWOA-BP) combining the improved whale optimization algorithm (IWOA) and backpropagation neural network (BP) was proposed to offer theoretical support for the formulation of grain strategies in the agriculture sector and its related industries. By introducing an improved convergence factor, nonlinear inertia weight, and optimal neighborhood disturbance strategy into the modified whale optimization algorithm, the optimal solution of the algorithm was obtained. This solution was then utilized as the initial weights and thresholds of the BP neural network, thereby enhancing the convergence speed and accuracy of the IWOA-BP hybrid algorithm. Subsequently, a grain yield prediction model based on the improved whale optimization algorithm was established using data from China’s grain yield over 45 years and seven influencing factors including effective irrigation area, chemical fertilizer application, rural electricity consumption, total power of agricultural machinery, sowing area of grain crops, disaster-affected area, and per capita consumption expenditure in rural areas. Through extensive experiments on a test set, it was found that the IWOA-BP model consistently outperformed other prediction models such as long short-term memory (LSTM), extreme learning machine (ELM), BP neural network with whale optimization algorithm (WOA-BP), and BP neural network with particle swarm optimization (PSO-BP). Compared to the ELM model, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the IWOA-BP model were reduced by 77.12% and 88.18% respectively. When compared to the LSTM model, the RMSE and MAPE of the IWOA-BP model were reduced by 69.11% and 47.36% respectively. Furthermore, in comparison to the WOA-BP model, the mean absolute error (MAE), RMSE, and MAPE of the IWOA-BP model were reduced by 43.78%, 43.22% and 45.96% respectively. Additionally, when compared to the PSO-BP model, the MAE, RMSE, and MAPE of the IWOA-BP model were reduced by 89.67%, 90.61% and 90.82% respectively. Therefore, the proposed IWOA-BP prediction model can be effectively used to predict grain yield due to its higher coefficient of determination, smaller prediction error, and faster convergence speed. It has important technical reference value for agricultural departments and relevant policymakers.

In order to effectively utilize LNG(liquefied natural gas) cold energy and liquefy CO₂ in gas turbine exhaust gas, a new process of LNG cold energy to liquefy CO₂ and CO₂ power cycle was proposed. In this process, Reheat cycle and regenerative cycle were added on the basis of conventional Rankine cycle, and multi-flow strand heat exchanger was set up. Chemical process simulation software was used to simulate the process flow and sensitivity analysis of reflux temperature, interstage cooling temperature and maximum circulating temperature and pressure was carried out to obtain the best operating parameters. Exergy efficiency, specific work and CO₂ liquefaction rate of the system were analyzed and calculated by exergy analysis. Exergy efficiency of exergy was 54.16%, specific work was 335.9 kJ/kg LNG, and CO₂ liquefaction rate was 0.621 7 kg/kg LNG in a new process. The evaluation indexes of the new process were better than those of the existing process. As for the exergic efficiency, exergic efficiency of exergic was as the highest and the temperature of CO₂ after liquefied was as the constraint condition, exergic efficiency of exergic was 54.28% and specific power was 337.5 kJ /kg LNG, so the system performance was further improved.

With the development of the energy industry and the continuous growth of global energy demand, the exploration and development of geothermal resources have become increasingly difficult. Deep and ultra-deep geothermal resources have been identified as a key direction for the development of the new energy industry. As the drilling depth for geothermal wells continues to increase, the thermal and physical properties of the drilling fluid are found to have a more significant impact on the calculation of wellbore temperature and pressure amid changes in temperature and pressure. In A coupled numerical model of transient temperature and pressure in the wellbore during the drilling of geothermal wells was established, and the influence of the density and viscosity of drilling fluid on the calculation of wellbore temperature and pressure with changes in temperature and pressure during the drilling of geothermal wells was studied. It was shown by the calculation results that the viscosity and density of the drilling fluid significantly affected the calculation of wellbore temperature. When the changes in viscosity with temperature are considered, the calculation results of wellbore temperature are found to be 3.1% higher, and when changes in viscosity and density with both temperature and pressure are considered simultaneously, the results are 4.99% higher, compared with the case where changes in viscosity with temperature are not taken into account; To improve the accuracy of calculations, the thermal and physical properties of drilling fluid should be fully considered in calculating the temperature and pressure of geothermal wells.

Zizhou gas field is a multi-layer superposed tight sandstone gas field. The reservoir covers strata from Benxi Formation of Carboniferous system to He8 Formation of lower Shihezi Formation of Permian system. At present, the pore structure of the reservoir and its influence on the reserve utilization are not well understood. It seriously restricts the gas field interlayer digging process. The pore structure of tight sandstone reservoirs in Benxi Formation-He8 Member was comprehensively investigated through the utilization of cast thin sections, reservoir physical property, production data, as well as statistical and correlation analysis methods. The suggestions for further interlayer potential exploitation were also given. Draw four conclusions. The reservoir has a typical 1+1 type pore structure. Among them, the lower formations (Benxi Formation, Taiyuan Formation, Shan2 member) have developed primary intergranular pores, with large primary pore proportion, face rate and pore size, and good pore structure, which is the dominant pore structure of primary pores. In the upper formations (Shan1 member and He8 member), the intragranular dissolve pore- intercrystalline pore- microfissure are developed, the primary pore ratio, face ratio and pore size are smaller, and the pore structure is poor, which is the dominant pore structure of the secondary pores. The difference of rock composition, especially the content of quartz and cuttings, is the main reason for the formation of 1+1 pore structure. Among them, the lower formations are dominated by quartz sandstone with high quartz content and low cuttings content, which is conducive to the preservation of primary pores. The upper formations are dominated by lithic sandstone with high lithic content and low quartz content, which are not conducive to the preservation of primary pores but to the formation of secondary pores. The pore structure of 1+1 type has a significant effect on the exploitation of reserves. Among them, the lower formations have good pore structure, relatively high permeability (0.52 mD), large discharge area (0.70 km²), and high reserve utilization degree (67.9%). The upper formations have poor pore structure, relatively low permeability (0.33mD), small discharge area (0.34 km²), and low reserve utilization degree (26.9%). The analysis shows that the reserves of the upper formations have not been effectively utilized under the current well pattern conditions. In order to reduce the waste of reserves in the upper formation, realize the balanced exploitation of gas fields and improve the overall reserve utilization degree of Zizhou gas field, it is recommended to exploit the upper formations separately with a small well spacing.

In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and address the limitation of acquiring limited information from a single sensor in grinding monitoring, this study a method for online recognition of the grinding direction of single crystal diamond tools based on multi-information fusion and particle swarm optimization (PSO) algorithm for optimizing the BP(back propagation) neural network was proposed. Vibration signals and acoustic emission (AE) signals were collected during the grinding process. The wavelet packet decomposition method was applied to analyze the vibration signals of the tool and identify the characteristic frequency bands strongly correlated with the grinding direction. The parameter analysis method was used to analyze the AE signals and extract the characteristic parameters. The energy values of the characteristic frequency bands in the vibration signals and the characteristic parameters of the AE signals were taken as the feature parameters for identifying the grinding direction of the tool. These feature parameters were then used as inputs to the BP neural network model for fusion and online recognition of the grinding direction. To overcome the disadvantage of the BP neural network easily getting stuck in local minima, the PSO algorithm was utilized to optimize the weights and thresholds of the neural network, effectively solving the problem of local minima. The experimental results show that the accuracy of online identification of the grinding direction of single crystal diamond tools is effectively improved by PSO-BP and multi-information fusion, reaching an accuracy of 85%, providing a new method for online identification of the grinding direction of single crystal diamond tools.

Signal processing and deep learning are often combined to achieve better diagnostic results in the field of fault diagnosis. Based on this, the symplectic geometric mode decomposition was improved and the ResNeXt neural network was optimized, and then a gearbox fault diagnosis model was proposed based on the combination of optimized symplectic geometric mode decomposition and ResNeXt neural network was improved. Firstly, the collected vibration signals were filtered and reconstructed by optimized symplectic geometric mode decomposition to obtain the effective components. Then it was sent to the improved ResNeXt neural network for fault recognition and classification. The rolling bearing variable condition data from the University of Ottawa was used to verify the feasibility of the model. The gearbox data from drivetrain dynamics simula (DDS) was used for contrast experiment and anti-noise experiment, which verified the effectiveness of changes and the generalization of the model.

Wall-mounted furnace heating is one of the main ways of winter heating in northern rural areas. Due to the pressure fluctuation of the gas pipeline, the wall-mounted furnace is prone to combustion instability, and even CO poisoning accidents. In order to reveal the variation law of temperature field and combustion products in gas-fired wall-mounted furnace under the change of pipeline pressure, taking the fire row burner as the research object, the distribution of gas composition at the outlet of the ejector device, the temperature in the combustion chamber, and the concentration of CO and NO in the flue gas at the outlet of the combustion chamber under different pipeline pressures were studied by numerical simulation and experiment. The results show that. ① With the decrease of the gas inlet pressure, the methane concentration at the outlet of the fire increases, and the non-uniformity of methane concentration increases. ② As the gas inlet pressure decreases, the CO mass concentration at the outlet of the combustion chamber gradually increases. When the gas inlet pressure is 500 Pa, the CO mass concentration reaches a peak of 25.2 mg/m³, which is higher than the human body CO poisoning accident limit of 23 mg/m³. The mass concentration of NO at the outlet of the combustion chamber increases first and then decreases, reaching a peak of 18.99 mg/m³ at 1 500 Pa. ③ As the gas inlet pressure decreases, the maximum temperature in the combustion chamber increases first and then decreases. The minimum temperature is 1 840 K at 500 Pa. The combustion is not sufficient and the heat generated by combustion is less. It can be seen that the decrease of the pipeline pressure increases the instability of the wall-mounted furnace combustion and the CO concentration also increases significantly. The results can provide some theoretical support for the manufacturers of gas wall-mounted furnaces in enhancing the safety of equipment.