In the context of unmanned multi-vehicle formation guided by manned vehicles, a system for vehicle recognition and trajectory tracking control of unmanned vehicles during formation driving was devised and executed. An algorithm for multi-sensor fusion moving target detection was proposed, leveraging data from lidar, camera, and mmWave radar sensors. The algorithm utilizes Euclidean clustering, deep learning, and kinematic reasoning techniques for target detection. Additionally, a fusion methodology was introduced to integrate detection outcomes from various sources for precise identification of vehicles in the vicinity. Paths were anticipated based on the trajectories of preceding vehicles, and a Kalman filter was developed to smooth and filter these paths. A vehicle dynamic model, vehicle road error model, and the robust H∞ controller was established for vehicle trajectory tracking control simulation. Outcomes from simulation and real vehicle validation show as follows. The average recognition accuracy of preceding vehicles in test scenarios exceeds 95%. The mean squared error and average trajectory deviation rate of real-time anticipated paths decrease by 17.3% and 48.6% respectively pre and post filtering. Lateral control position error and yaw angle error decrease by 29% and 41% correspondingly compared to PID control. Vehicle formations attain stable working at speeds of up to 54 km/h.

In order to study the relationship between VCI (vascular cognitive impairment) and intracranial and extracranial large artery stenosis, cerebral white matter lesions and brain atrophy. By consecutively enrolling 105 patients with VCI, divided into mild group (n=77) and severe group (n=28), and at the same time selecting patients with normal cognition as the control group (n=71). comparing the differences in cerebrovascular disease risk factors, cerebral white matter lesions, ischemic cerebral infarction, and cerebral atrophy among the 3 groups, and analysing the correlation between the degree of stenosis of the intracranial and extracranial large arteries and VCI. The results show that the differences in the history of ischaemic stroke and the proportion of ≥2 lacunae were statistically significant among the 3 groups (P<0.001).The differences in cerebral white matter high signal, paraventricular white matter, deep white matter Fazekas score, and whole-brain cortical atrophy GCA grading were statistically significant among the 3 groups (P<0.001). In the multivariate ordered logistic regression analysis model, it was found that internal carotid artery segment C1, internal carotid segment C2~C7, and the degree of middle cerebral artery stenosis are the main influencing factors for the severity of VCI. The degree of stenosis of internal carotid artery C1 segment and internal carotid C2~C7 segment is positively correlated with the severity of VCI patients to a low degree, whereas the degree of stenosis of the middle cerebral artery, cerebral white matter lesions, and cerebral atrophy grading are positively correlated with the severity of VCI patients to a moderate degree. It is evident that with increasing cardiovascular risk factors, history of ischaemic stroke and degree of stenosis of the internal carotid and middle cerebral arteries, the risk of VCI in the subjects increased significantly. It suggests that the condition of intracranial and extracranial large arterial lesions can be used as one of the indicators for the detection of VCI, and that there are certain feasible therapeutic directions.

In order to accurately select the Copula function to simulate the mutual correlation between inclination and dip of jointed rock mass structural plane, the Copula function method to simulate the occurrence of jointed rock mass structural plane under different fitting indexes was proposed. The optimal Copula function was determined by using the least square Euclidian, AIC information criterion and BIC information criterion, and the optimal edge distribution type of the observed occurrence data of the structural plane was determined by Matlab software. At the same time, Monte Carlo sampling method was used to automatically generate simulation data, and the data was imported into Dips software for visualization processing, and the erP projection map of occurrence was obtained. The difference between the measured dip and inclination data and the simulated data determined by Copula function under different fitting indexes was compared. Finally, the validity of the method is tested based on engineering cases. The results show that different fitting indicators will produce different Copula functions, and there will be great differences in the effectiveness of simulation occurrence. Improper fitting indicators may lead to the selection of inaccurate Copula functions, so that the model can not accurately capture the relevant structure and features of the data. Inappropriate fitting indexes may lead to large errors between the fitting model and the real data, which will decrease the predictive ability and interpretation ability of the model. In this case, it is shown that the Gaussian Copula function selected under the fitting index of least square Euclidene values has the best fitting effect on the measured data. This research will help to select the appropriate fitting index when using Coupla function.

Unique test requirements in civil large aircraft flight testing, characterized by short task durations, wide measurement point distribution, and numerous measurement locations, are addressed. Challenges in the existing wired Ethernet-based onboard data acquisition systems, including difficult measurement equipment installation, complex test cable layout, and prolonged retrofitting periods, are identified. A flexible, miniaturized, and compact space-compatible measurement system for critical wing structural state parameter measurements during civil aircraft flight testing was proposed. An integrated microsystem, including a flexible antenna module and a multi-sensor parameter collection module, was developed and integrated into the civil aircraft wing. The system's reliability and stable signal transmission were demonstrated. The design and application of a wireless flexible measurement system for wing state monitoring on civil aircraft were detailed. The system design approach, data transmission strategy, and integration with third-party loggers were described. Ground and flight tests were conducted to collect data.The onboard flexible system's capability to measure temperature, three-axis vibration, and pressure is verified.

In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review.It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.

Aiming at the poor performance of existing algorithms in solving large-scale ship path planning problems and the lack of consideration of marine environmental factors such as eddies, a ship path planning method based on punishment pheromone ant colony optimization was proposed. Firstly, three evaluation functions were designed for the planned path: length, risk and heading. Secondly, ACO(ant colony optimization) algorithm inspired by reinforcement learning was designed to search the optimal path, which adds punishment pheromone to the traditional guidance pheromone, which can prevent ants from conducting ineffective searches. Finally, the simulation experiments of the improved algorithm under static environments demonstrate that the proposed algorithm is superior to traditional ACO, jump point search algorithm, and bi-directional search improved ACO in terms of path length, risk value and turn accumulation angle. Compared to the best metrics among these three algorithms, proposed algorithm still achieves a significant improvement in path length reduction of 6.1%, risk value reduction of 5.6%, heading accumulation angle reduction of 78.6%, and iteration number reduction of 53.3%. Especially when the mesoscale eddies and water flow are introduced, the proposed algorithm can still plan a more suitable path for ship navigation, which has positive application significance.

An improved version of the EfficientNetV2 network is presented for garbage image classification to address the limitations of mainstream algorithms, such as poor dataset universality, limited recognition types, and algorithmic constraints in specific environments. The proposed algorithm emphasized both classification speed and accuracy. The EfficientNetV2 network was utilized as the baseline model, and classification speed was enhanced through the incorporation of the SK (selective kernel) attention mechanism. Transfer learning strategies were employed to improve classification accuracy. By leveraging deep learning model frameworks for garbage image processing, the need for manual feature extraction from dataset images was eliminated, and the scope of garbage recognition was expanded. Experimental results demonstrate that the proposed algorithm achieves an accuracy of 99.71% on a self-built dataset, which is an improvement of at least 4.77% compared to other algorithms, such as GoogleNet. Furthermore, in terms of time efficiency, the proposed algorithm outperforms algorithms like VggNet19 by at least 50%. Through the enhancement of the EfficientNetV2 network, accurate and faster garbage classification is enabled, providing a scientific and efficient solution to the growing challenges posed by garbage issues.

It is a new problem for the prevention and control of non-point source pollution to adsorb pollutants by colloids and assist them to quickly migrate from soil to water during the rainfall-runoff process. Southwest Guizhou Province is one of the important ecological barrier areas in the Pearl River Basin, but some areas sow corn in the middle and late April and enter the rainy season in May. The destruction of soil structure, the significant increase of precipitation and the typical karst landforms in the region lead to high water environmental risks in sloping farmland areas. Undisturbed soil samples are collected from newly ploughed yellow soil slope farmland in karst area. These samples undergo simulated rainfall infiltration experiments. The purpose is to investigate the dynamic release rule of colloids under different rainfall intensities, including the change characteristics of colloid concentration, particle size distribution and its content level with the increase of accumulated rainfall. The results show as follows. The colloid concentration increased with the increase of rainfall intensity and cumulative infiltration, and the study further revealed that the difference of released soil colloid concentration under three rainfall intensities showed three different stages: the colloid concentration is not significantly different when the rainfall intensity is 25 mm/h and 40 mm/h in the 0~100 mm stage, but is significantly higher than 10 mm/h. The colloid concentration in the 100~250 mm stage is quite different under three rainfall intensities. When the accumulated rainfall is more than 250 mm, the difference between them is very small. When the rainfall intensity is 40 mm/h, the characteristic statistics of colloidal particle size show obvious two-stage and sudden drop characteristics. At first, the average particle size of effluent increases with the increase of rainfall intensity, and then decreases slightly with the further increase of rainfall. When the rainfall intensity is 25 mm/h and 10 mm/h, it presents a gradual change characteristic. The change of colloid content with different particle sizes shows a trend of stability, increase and decrease respectively. The research innovatively reveals the stage characteristics of the outflow concentration difference of soil colloids at different flow rates, and quantifies the change trend of colloids with different particle sizes with the cumulative infiltration from the meso-scale, which will provide a reference for further evaluating the regional water environmental risk and driving mechanism in this period.

In order to apply remote sensing technology to uranium exploration in the Mouding area of Yunnan Province, based on ASTER (advanced spaceborne thermal emission and reflection radiometer) remote sensing data, the interference removal and PCA (principal component analysis) method was used to extract the Al-OH, Mg-OH, {\mathrm{CO}}_3^{2-} and iron-stained alteration information in the study area. The lineaments in the study area were automatically extracted by PCA and LINE model in PCI Geomatica software, and the density map of lineaments was created. Finally, combined with geological data, the relationship between uranium mineralization and alteration and linear structures in the study area was analyzed, and a favorable mineralization area was delineated. This study can provide some ideas for subsequent exploration in the area, and also provide some reference for remote sensing technology in mineral exploration in vegetation-covered areas.

In the manufacturing process of wind turbine blades, gluing is an important part of ensuring the structural strength and tightness of the blades. In view of the low efficiency, uneven gluing, low quality of glue line and glue overflow on the surface of parts caused by the manual sealing and gluing operations commonly used in the gluing process of wind turbine blades, a kind of intelligent rubber shoe for wind turbine blade gluing with servo motor and reducer as driving components was developed, and the gluing process was introduced in detail. ANSYS analysis was carried out on the key components, and after simulation analysis, it was found that the structure of the intelligent rubber shoe could meet expectations. After experiments, it is verified that the shape of the rubber road is full, the two sides are smooth and flat, and the width and thickness of the rubber road meet expectations. The use of rubber shoes can not only reduce the work intensity of workers, but also reduce the use of 10% of the glue amount when gluing, and improve the efficiency and quality of gluing.