The geological conditions of fault-controlled carbonate volatile oil reservoirs in Shunbei oilfield and the relationship between production wells are complex, and conventional methods have poor applicability in calculating dynamic reserves. Considering that the reservoir has the characteristics of fracture and cavity development, multi-phase seepage and inter-well interference, a volatile oil reservoir pseudo-pressure function was proposed and the functional relationship between saturation and pressure was given. The multi-phase flow material balance well group dynamics of the volatile oil reservoir material balance theory were established. The proposed method utilizes bottomhole flow pressure to calculate dynamic reserves, while there is no need for static pressure testing in the well group. The results show that the multiphase flow material balance equation describes the linear relationship between oil production rate and cumulative production. A partial correlation analysis was conducted on the main controlling factors of the well group’s dynamic reserves, and it was concluded that the main factors affecting the production dynamic characteristics are average oil production, production decline rate and formation energy. Quantitatively evaluate the impact of errors in important parameters of the formation and fluid on the calculation results of dynamic reserves. It is believed that the compression coefficient and porosity of the formation have a great influence on the accuracy of dynamic reserve calculations. The well group dynamic reserve calculation method was applied to a typical well group in the Shunbei oilfield, and the dynamic reserve decrease in the calculation results of a single well was compared to quantify the decrease in dynamic reserves in the Shunbei oilfield due to inter-well interference. The method can accurately calculate the dynamic reserves of well groups in the Shunbei fault-controlled volatile oil reservoirs.

In order to meet the large number of future unmanned aerial vehicle (UAV) operation requirements, the safe takeoff interval for UAVs was formulated on the basis of conforming to the safety target level and aiming at the highest efficiency. According to the operating speed error characteristics of UAVs, taking into account the operating characteristics of the climb phase and cruise phase, the takeoff safety problems in three scenarios of same route operation, cross route operation and route network operation were analyzed, a collision risk assessment model was established respectively, and a calibration method for the takeoff interval was proposed in combination with Monte Carlo simulation. Finally, taking the actual operation of logistics UAVs as an example, the 10^-7 maximum collision probability was taken as the target safety level for verification, and the minimum safe takeoff interval in the three operation scenarios was analyzed and determined. The results show that the safe takeoff interval of the same route T is 122 s, the safe takeoff interval T of the cross route is related to the difference D between the distance of two takeoff points from the intersection point and satisfies T = (D±1 199.97)/14(T≥0), and the safe takeoff intervals between the four takeoff points of the airway network system are 158, 86, 0, and 0 s, respectively. The method can provide a reference for the UAV operation enterprises to carry out takeoff interval management.

Hydrogen, which produces only water during usage, is an excellent secondary energy source. However, its environmental impact should consider the primary energy sources used for hydrogen production, as well as transportation. The use of the grid can not absorb the abandoned photoelectrolysis water to produce green hydrogen and incorporate it into natural gas, and the use of natural gas pipeline network transportation can ensure the environmental protection and clean hydrogen energy. An optimal operation model considering the start-stop characteristics of proton exchange membrane (PEM) electrolytic cell was established. The model can obtain the optimal production plan when dealing with intermittent energy, hydrogen demand fluctuation and time-varying electricity price, and achieve the balance of time-varying electricity price, hydrogen production, photovoltaic output and operating cost. The production plan shows the load of electrolyzer in different periods, which verifies the correctness of the model. By changing the minimum load in the constraint condition, the results show that the proportion of standby and idle state decreases with the decrease of the minimum load, and the running cost also decreases slightly. When the critical value reaches 6.1%, the running cost no longer changes.

The Tanshuling molybdenum deposit is located in the Jiangnan Uplift zone along south part of the Jiangnan Fault. Its main lithology type is granodiorite. With the aim to constrain their magma and ore-forming ages and deposit genesis, combined zircon U-Pb and molybdenite Re-Os geochronology together with whole-rock major and trace element geochemistry have been carried out. The result suggest that the content of SiO₂ is 64.5%~66.8%, Al₂O₃ is 14.4%~16.0%, K₂O is 3.92%~4.86%, Na₂O is 2.90%~3.91%, CaO is 1.56%~2.8%, MgO is 1.24%~1.53%, A/CNK value of 1.02~1.10, and A/NK value of 1.37~1.59. The characteristics of major elements show that the granites belong to metaaluminium to weak peraluminous high potassium calc-alkaline pot-assium series with I-type granite nature. The chondrite-normalized REE patterns are evidently right-declined, with relatively LREE enrichment and slight Eu negative anomalies. The molybdenite Re-Os age of the Tanshuling molybdenite is (133.09±0.86) Ma, and the U-Pb dating of the Maolin granodiorite is (140.4±0.62) Ma, (139.9±0.66) Ma, (139.6±0.63) Ma, all belong to the Early Cretaceous. Integrated chronological and geochemical characteristics show that the main magmatic activity of the Tanshuling molybdenum deposit belong to the Pacific tectonic system, and the alternation of extrusion and extensional has led to large-scale magmatic and mineralization in this area.

For gearboxes, variations in fault characteristics under different operating conditions, susceptibility to noise interference in fault diagnosis, lead to poor generalization and low recognition accuracy of fault diagnosis models. An end-to-end convolutional block attention module-sparse temporal convolutional network with soft thresholding(CBAM-STCN) was proposed for gearbox fault diagnosis. Firstly, the Hilbert transform was employed to convert the gear fault vibration signal into an envelope spectrum signal. Then, this signal was input into the CBAM-STCN fault diagnosis model. The model integrates a hybrid attention mechanism module, the convolutional block attention module (CBAM), which adaptively learns the weights of channel and spatial attention to extract information sensitive to fault features. The embedded soft thresholding function minimizes the discrepancy between the model’s output and the original input. Finally, the proposed method was utilized to identify and classify various types of gear faults under two different conditions. The results indicate that the CBAM-STCN model achieves an average accuracy of 98.95% in intelligent gear fault diagnosis, demonstrating its potential value for gearbox fault diagnosis.

To address the challenges of extracting and identifying fault features from roadheader cutting vibration signal, a new fault diagnosis method of roadheader cutting head based on the refine composite multi-scale fuzzy dispersion entropy(RCMFDE) and hippo optimized random forest(HORF) was proposed. Firstly, RCMFDE was used to comprehensively characterize the fault feature information of the roadheader cutting head, and the fault feature data set was constructed. Secondly, the fault type was trained and tested by the HORF to realize the fault pattern recognition of the cutting head of the roadheader. Finally, the proposed method was applied to the experimental data analysis of the cutting head of the roadheader, and compared with the existing multi-scale fuzzy entropy and fine-complex multi-scale spread entropy fault feature extraction methods. The results of the trial indicate that RCMFDE performs better than the other two entropy approaches in discovering defect features, and hippo random forest outperforms extreme learning machine and support vector machine in error recognition. The fault diagnosis method can more correctly recognize the error type of the cutting head of the roadheader, and the rate of accuracy of the recognition obtained 100%.

To reveal the complex relationship between the built environment and walking activity among older adults, a gradient boosting regression tree (GBRT) model was adopted, combined with multi-source data such as mobile signaling data, remote sensing image data, and point of interest (POI), to deeply explore the non-linear impact of the built environment on elderly walking activities and its threshold characteristics. The findings indicate that the built environment has a significant nonlinear impact on older adults’ walking activity, with land use factors being the most influential. Specifically, land use mix, the proportion of commercial service facilities, and the proportion of residential land are identified as key factors affecting older adults' walking activity. Additionally, the proximity of facilities also plays an important role. Finally, suggestions have been put forward for the adaptive transformation of land use and facilities to improve the level of elderly walking activities and promote healthy aging.

The format and content of items such as product names and specifications in the detailed section of VAT invoices are highly flexible and complex, lacking complete gridlines to separate information fields. Existing methods for all-element structural recognition of VAT invoices face issues like low element recognition rates and high computational complexity. A structured recognition method for full face information based on computer morphology was proposed, which uses morphological operations to detect invoice table lines, cuts and recognizes text in different areas of the invoice. Then the implicit rules of the layout of the value-added tax invoice product details area was reused, combined with the text connected areas obtained through computer morphology operations, to construct a complete table structure. Finally, text detection and recognition were achieved using text detection neural network with differentiable binarization (DBNet) and convolutional recurrent neural networks (CRNN). The proposed method was tested on a dataset of 49 value-added tax invoices in three different formats, and the results show that the element recognition rates reached 99.9%, 97.4%, and 98.8%, respectively. The average running time per invoice is 0.90, 0.47, and 0.82 s, respectively. The structural recognition performance of the entire invoice exceeded multiple comparison table recognition models and literature methods.

To investigate the correlation between aerobic exercise and cardiac function, lipid metabolism, and inflammation in patients with cardiovascular disease, by searching PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI) databases for relevant studies on the effects of aerobic exercise on cardiac function, lipid metabolism, and inflammatory factors in patients with cardiovascular disease, Meta-analysis and correlation analysis were conducted using RevMan5.4 and R software. The results show that aerobic exercise significantly reduces B-type natriuretic peptide (BNP) [SMD=-0.84, 95% CI (-1.34, - 0.34), P=0.001], systolic blood pressure (SBP) [SMD=- 0.55, 95% CI (-0.86, - 0.25), P=0.000 4], and diastolic blood pressure (DBP) [SMD=- 0.99, 95% CI (-1.67, - 0.32), P=0.004], LDL [SMD=- 0.53, 95% CI (- 0.89, - 0.18), P=0.003], and C-reactive protein (CRP) [SMD=-0.53, 95% CI (-0.90, -0.16), P=0.005]. CRP is positively correlated with HDL, LDL, and DBP, with correlation coefficients of 0.35, 0.26, and 0.28, respectively. CRP is negatively correlated with SBP, with correlation coefficients of -0.31. From this, it can be seen that aerobic exercise can improve heart function, lipid metabolism, and levels of inflammatory factors to a certain extent in patients with cardiovascular diseases, and there is a correlation between heart function, lipid metabolism, and inflammation.

In order to effectively predict the fuel consumption of vehicles, improve fuel economy and promote energy saving and emission reduction, a Hyperband-CNN-BiLSTM-based motor vehicle fuel consumption prediction method was proposed. Firstly, based on the vehicle operating status data and fuel consumption data collected from the actual road test, the salient factors affecting the fuel consumption of vehicles were analyzed. Secondly, combining the powerful feature extraction capability of convolutional neural network(CNN) and the advantages of bidirectional long and short-term memory network (BiLSTM) in dealing with the time-series data, a combined model of vehicle fuel consumption prediction based on CNN-BiLSTM was constructed. Then, in order to improve the model prediction accuracy, the combined model was optimized by Hyperband optimization algorithm, and the vehicle fuel consumption influencing factors were taken as the model input features to train the model to realize the modeling and prediction of vehicle fuel consumption. Finally, CNN, LSTM, BiLSTM, CNN-LSTM and CNN-BILSTM were selected as comparison models to evaluate the effect of Hyperband-CNN-BiLSTM prediction model. The results show that compared with other models, the Hyperband-CNN-BiLSTM model has the smallest mean absolute error (MAE) and root mean squared error (RMSE). They are 0.057 69 and 0.119 25, respectively. R² is the largest (0.991 76), and the model has the best prediction effect.