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 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%.

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.

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.

With the promotion of green development in civil aviation, aircraft noise has become an issue that cannot be ignored. An improved dynamic window approach (DWA) combining aircraft performance was proposed, which introduces the constraint of continuous climb operations (CCO) and constructs performance constraints for aircraft. To address the problem of rough solution set caused by traversal in traditional DWA algorithm, genetic algorithm(GA) was used for optimization. Secondly, speed was used to represent the time indirectly in order to optimize the track evaluation function. The effect of population distribution was added to make the model more reasonable. Finally, taking the departure direction of BOKIR-8T at Chengdu Shuangliu Airport as an example, the improved algorithm (DWA-GA) was compared with the traditional DWA algorithm, and the flight path under the influence of population distribution was compared, and the aircraft performance parameters and noise influence range were analyzed. The simulation results show that the improved algorithm is more accurate than the traditional DWA algorithm at low resolution, and the population distribution has obvious influence on the track.

Considering the high-temperature thermosetting chemical issues in the manufacturing process of composite tensile armor layers, the curing kinetics of T700/epoxy prepregs were explored. Through differential scanning calorimetry (DSC) analysis and the Starink method, the autocatalytic reaction curing kinetic parameters were accurately calculated. Then a curing kinetic model was established. It has been shown by experimental results that the reaction rate of the prepreg is significantly increased at higher heating rates. After the peak is reached, the reaction rate is decreased more rapidly, resulting in a lower average final reaction heat. The apparent activation energy of the curing reaction for this prepreg is 77.04 kJ/mol, and high consistency with the experimental data is exhibited by the constructed curing kinetic model.

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.

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.

China’s tight oil reservoirs have distinctive characteristics, including thin interbedded layers with alternate distribution in the longitudinal direction and strong reservoir heterogeneity. In order to maximize productivity and economic benefits, a development approach was commonly employed, involving a well network with layered fracturing for the simultaneous development of multiple layers. However, existing productivity models for fractured directional wells are only applicable to single-layer development and do not consider inter-layer interference, making them unsuitable for predicting well productivity of multi-layer development. In order to improve the accuracy of productivity prediction, the flow field nearby the fractured directional well is divided into the main fracture region, the stimulated reservoir volume region, and the un-stimulated reservoir volume region. Considering the effects of flow patterns in different regions and stress sensitivity, and introducing a disturbance coefficient, a non-steady-state productivity prediction model for multi-layer fractured directional well in tight oil reservoirs was established. After validating the model accuracy, the influence of fracture half-length, fracture conductivity, threshold pressure gradient, stress sensitivity and reservoir heterogeneity on the productivity of fractured directional well was further investigated. The results indicate that the threshold pressure gradient, stress sensitivity and longitudinal heterogeneity significantly affect the productivity of fractured directional well. The larger the threshold pressure gradient, and the more significant the stress sensitivity and longitudinal heterogeneity, the lower the productivity of fractured directional wells. With the gradual increase in fracture half-length, fracture conductivity, and matrix permeability, the productivity of fractured directional wells increases, but each factor has its optimal range. The ranking of factors affecting productivity is as follows: matrix permeability, fracture conductivity, fracture half-length, threshold pressure gradient, longitudinal heterogeneity, stress sensitivity.