The path planning problem of electric vertical takeoff and landing (eVTOL) aircraft in urban scenarios was studied. Firstly, a three-dimensional urban space model was constructed using the hazard grid method. Considering the selected model of eVTOL, with range, operational risk, and altitude variation as objective functions, a path planning model for manned eVTOL with multiple constraints was developed, taking into account the characteristics of the aircraft and environmental limitations. Subsequently, an improved artificial electric field algorithm (IAEFA) was proposed, which enhances the traditional artificial electric field algorithm (AEFA) by introducing an adaptive Coulomb parameter and incorporating a decreasing coefficient in the Coulomb constant calculation for simulation-based solution. Experimental results demonstrate that the constructed model achieves the expected outcomes. The solution effectiveness of path planning using the improved algorithm surpasses that of traditional particle swarm optimization and artificial electric field methods, resulting in shorter range, minimal altitude variation, and enhanced safety during operations. Finally, based on comparative experiments, the value of the decreasing coefficient was determined. The optimal solution effectiveness of the improved algorithm is achieved when the decreasing coefficient is set to 1.5.

The displacement response of a bridge is identified as a fundamental condition for the structural health monitoring and safety assessment of bridge structures. In order to fully leverage the advantages of machine vision for measuring structural displacements and to enhance its applicability, a structural displacement monitoring method based on the pinhole camera model was proposed. Sparse optical flow was employed to track structural feature points, achieving sub-pixel level image displacements. By employing the optical geometric relationships of camera imaging, an analytical solution was established between real-world displacements and image displacements. The image displacements were then substituted into algebraic relationships to obtain the true structural displacements. In the indoor experiments on dynamic load displacement identification of a simply supported bridge model, compared to the measurements obtained using a linear variable differential transformer displacement meter, the proposed method achieves a maximum error within 6% for pitch angles up to 30° and yaw angles up to 35°, thereby meeting the application requirements for monitoring the displacement deformations of bridge structures.

The quantitative monitoring and evaluation of the terrestrial ecological environment status helps to understand the changes in ecosystems and their driving factors, and is of great significance for the government to guide regional ecological environment management, achieve ecological protection and socio-economic coordinated development. Based on long-term multi-source satellite remote sensing data, a land ecological environment status index (LESI) evaluation model was constructed by coupling five indicators including greenness, heat, humidity, dryness, and air pollution using covariance principal component analysis. Four strategies, including Augmented Dickey Fuller (ADF) test, Biplot biplot, correlation analysis, and cross validation, were used to demonstrate the good stationarity, rationality, comprehensive representativeness, and regional adaptability of the model. On this basis, the spatio-temporal characteristics and evolution patterns of the land eco-environment status (LES) in the the Taihu Lake Basin from 2001 to 2021 were assessed, the driving factors of LES changes were discussed, and the contributions of climate change and human activities were quantified. The results show that the eco-environment quality of the the Taihu Lake Basin is declining first and then stable. The average annual LESI decreases significantly from 0.639 in 2001 to 0.523 in 2009 (-18.2%), and then tends to be stable. The spatial-temporal variation of LESI in Taihu Lake Basin is obviously different. The area where the ecological environment quality remains stable or improved (68.8%) is significantly larger than the area where the ecological environment quality is declining (31.2%), of which Hangzhou and Huzhou have the best eco-environment quality and remain stable. Shanghai and Suzhou are relatively poor and have significant fluctuations. The contributions of temperature, precipitation and night light to LESI are 0.03, 0.19 and 0.78, respectively, indicating that the eco-environment changes in the the Taihu Lake basin in recent 21 years are mainly dominated by human activities, while only some forest mountain areas and wetland areas are affected by climate change. The LESI model established in the study can effectively monitor and quantitatively evaluate changes in the eco-environment, providing scientific support for the government to formulate ecological environment protection policies and promote high-quality development.

A method for optimizing the control parameters of the sample point distribution state within the framework of the unscented transform (UT) for the unscented Kalman filter (UKF) was introduced. The issue of abnormal filtering performance arising from the state of sample point distributions was addressed by this method. A multi-strategy improved sparrow search algorithm(ISSA) was employed to finely tune the control parameters. The goal is to enhance the distribution of Sigma points, thereby improving the effectiveness of nonlinear approximations and ultimately enhancing the accuracy of filtering estimations. To address the shortcomings of traditional sparrow search algorithms, several refinements were implemented. Initially, a Cubic chaotic mapping was applied to diversify the initial population. Furthermore, during the exploration phase, a nonlinear adaptive convergence factor was introduced to balance the algorithm’s capacity for global exploration and local exploitation. Additionally, a wavelet mutation strategy was integrated into the follower phase to prevent blind adherence to specific paths and mitigate the risk of becoming trapped in local optima. Lastly, an adaptive t-distribution perturbation capability was introduced to strengthen the algorithm’s ability to perform wide-ranging global searches. The efficacy of the proposed ISSA was demonstrated through simulation experiments conducted on various test functions. The results consistently show that ISSA outperforms other methods in terms of convergence and solution accuracy. Furthermore, the benefits of ISSA are extended to the optimization of parameters within the UKF algorithm. Experimental outcomes indicate that the ISSA-UKF algorithm reduces the root mean square error (RMSE) of position by 52.2% and the RMSE of velocity by 21.9%, thus affirming the viability and effectiveness of the proposed enhancements.

The Loess Plateau, as a natural ecological barrier in the western region of China, has made positive contributions to the sustainable development of the nation. The governance and restoration of the ecological environment on the Loess Plateau (Gansu region) plays a critical role in the implementation of China’s ecological civilization construction strategy. To monitor the changes in forest resources on the Loess Plateau (Gansu region) from 2008 to 2018, based on cloud platform, Landsat, PALSAR, and terrain data were integrated to explore the advantages of spectral index, backscatter, texture, and terrain features in obtaining forest resource information. The random forest feature selection algorithm was utilized to obtain the spatiotemporal distribution of forest cover in the study area for 10 years, and factor detection was conducted using geographic detectors. The results indicate that the random forest feature selection algorithm can effectively screen important feature information, with an overall accuracy of 91.88% and a Kappa coefficient of 0.91. The experimental scheme that integrates Landsat, PALSAR, and terrain data presents significantly higher accuracy compared to the forest classification results using a single data source. The overall accuracy of the four classification results is 86.65%, 88.23%, 90.15%, and 89.86% respectively. Over the past 10 years, the net increase in forest area in the study area is 0.60×10⁴ km². The areas with increased forests are primarily distributed in the central and eastern parts of Qingyang City, Pingliang City, Tianshui City, and the western region of Linxia Hui Autonomous Prefecture, while forest degradation primarily occurs in the southwestern part of Dingxi City and the central and eastern areas of Linxia Hui Autonomous Prefecture. In single-factor detection, land use type is the dominant factor in forest cover change, and the spatial distribution of suitable soil type and the auxiliary effect of rainfall provide favorable natural conditions for the survival rate of afforestation and the healthy growth of forests.

Primary cracks and new cracks develop within the engineering rock mass, leading to the formation of macroscopic cracks. The hollow cylindrical discrete element simulation test enables the emulation of complex stress paths. In order to solve the problems existing in the simulation test of hollow cylindrical discrete element, such as numerous influencing factors and lengthy meso-parameter calibration, a method of mesoscale parameter calibration of hollow cylindrical sandstone discrete element based on machine learning algorithm was proposed. Through variations in input variables within the discrete element model, 210 sets of simulation data were obtained. A mesoscopic parameter calibration model based on random forest algorithm and extreme gradient boosting(XGBoost) algorithm was established, the prediction accuracy of the model was compared, the parameter sensitivity was analyzed, and the contribution of input parameters to the overall mechanical properties of the rock was quantified. Combined with the indoor triaxial test of hollow cylinder, the calibration results show that the XGBoost algorithm has the advantages of computing speed, and can quickly locate the range of discrete element mesoscopic parameters, which provides a new idea for the calibration of discrete element mesoscopic parameters of hollow cylinder, and has the value of engineering application.

In order to improve the recognition accuracy of high-altitude nuts and reduce the false detection rate of bolts and nuts, a high-altitude nut recognition model based on improved YOLOv5 was proposed. Firstly, a new attention mechanism efficient multi-scale attention(EMA) was added to the backbone network to integrate more information. Secondly, in order to enhance the network’s feature extraction capability, bidirectional feature pyramid network(BiFPN) was used to replace the PANet of the neck network. Finally, structured intersection over union(SIoU) was used to replace the original loss function complete intersection-over-union(CIoU) to accelerate the convergence of the model and improve its classification accuracy. The results show that the improved model has better performance than the original YOLOv5 model. The accuracy of the improved model increases by 0.92%. The recall increases by 0.16%. The average precision 1 (mAP_0.5:0.5) increases by 0.53%. And the average precision 2 (mAP:0.95) increases by 2.26%. An actual recognition comparison experiment between the improved model and the original YOLOv5 model was carried out. The experimental results show that the improved model has better recognition performance, which reduces the missed detection rate and the false detection rate, and improves the actual recognition rate. The improved model can well meet the recognition and image data acquisition of high-altitude nuts. And it also provide a data foundation for subsequent nut maintenance.

In order to study the solution properties of polyacrylamide at high temperatures at the molecular-atomic scale, the molecular models of partially hydrolyzed polyacrylamide(HPAM) and AM/AANa/AMPSNa copolymer [P(AM/AANa/AMPSNa)] have been established through a combination of experimental methods and molecular dynamics simulations. The solution properties of the two polymers at elevated temperatures were systematically investigated in terms of polymer chain rigidity and flexibility, hydrogen bonding, hydration layer, interaction energy and the effect of salt cation, and the micro-mechanism of temperature resistance of P(AM/AANa/AMPSNa) was explained. The results show that the introduction of side chains containing methyl and sulfonated groups into the molecular chain of P(AM/AANa/AMPSNa) could increase the rigidity of the molecular chain, more hydrogen bonds are formed between sulfonated groups and water and has longer lifetime. At the same time, the strong polar sulfonic acid group makes the hydration layer denser, which results in the weaker static shielding effect of the cations on the P(AM/AANa/AMPSNa). Under different temperature conditions, P(AM/AANa/AMPSNa) has stronger intermolecular non-bonding interactions, stronger water retention effect of the molecular chain at the microscopic level, and higher viscosity at the macroscopic level.

1g shaking table test refers to the shaking table test carried out under conventional gravity conditions, which is the mainstream type of shaking table test at present. A distinction was made between strict similarity models and strain distortion models, primarily utilizing the control equation method with supplementary dimensional analysis. The similarity relationships between these models and the prototype during different deformation stages in rock slope 1g shaking table tests were theoretically derived, and the key parameters required to achieve similarity between the model and the prototype were established. Numerical tests were conducted to verify and evaluate the established similarity relationships for different deformation stages, considering the inherent vibration characteristics and dynamic response characteristics of rock slopes. The results indicate that both strict similarity models and strain distortion models can accurately calculate the natural vibration characteristics of the prototype slope and the dynamic response characteristics during the elastic deformation stage under seismic loads. However, for large deformation issues, it is recommended that the strain similarity ratio be close to or equal to 1. These research findings provide valuable references for the design of similarity relationships and the selection of similarity models in 1g shaking table tests for rock slopes.

The pore structure of deep tight sandstone reservoir is complex and heterogeneous, and it is difficult to determine the influencing factors of pore microscopic parameters on the characteristics of gas-water phase permeability. Based on the fractal geometry theory, combined with the core mercury intrusion porosimetry (MIP) method, nuclear magnetic resonance (NMR) T₂ spectroscopy test and micron CT scanning results, the micro-pore throat parameters and various scale fractal dimensions of the reservoir were obtained. Through the mobile gas porosity and the maximum atmospheric phase relative permeability, the control mechanism of the fractal dimension and micro-pore throat structure parameters on the gas-water phase permeability characteristics was discussed. The results show that mercury injection and NMR fractal curves have obvious “three-stage” characteristics, and the total shape dimension of the reservoir describes the distribution of seepage and movable fluid more accurately when gas and water coexist. The maximum mercury saturation, average pore throat radius, total reservoir shape dimension and displacement pressure have significant effects on the mobile gas porosity during gas seepage. The average pore throat radius has a significant influence on the maximum effective gas phase relative permeability in gas seepage. The control mechanism of the micro-pore structure on the gas-water phase permeability can provide a powerful guide for the efficient development of water-producing gas reservoirs.