To design and prepare high-quality one-dimensional hexagonal quasicrystal nano-composites, the interface and interface phase models were applied to study the infinite one-dimensional hexagonal quasicrystal anti-plane fracture problem with cylindrical inclusions containing nano coatings by using the complex function method and Gurtin-Murdoch's surface/interface elasticity theory. Under two different models, the series form expressions of phonon and phason field stress fields in matrix, coating and inclusion were obtained, respectively. Numerical examples were used to analyze the effects of interface elastic constants and size effects on the stress field around inclusions. The results showed that the positive or negative values of interface elastic constants would affect the stress field around nano-inclusions. As the size of nano-inclusions increased, the stress field exhibited significant size dependence, and surface effects had significant differences in their effects on the stress fields of dimensionless phonon and phason fields. The relevant results provide a certain theoretical reference for studying the mechanical behavior of quasicrystalline nano-inclusions.

During the manufacturing and application of fiber-reinforced composites (FRP), issues such as impact damage and fatigue accumulation cause irreversible subtle damage to the internal structure. Acoustic emission (AE) technology, with its high precision and real-time property, has become an important means to monitor the damage evolution and failure mechanisms of FRP. The applications of acoustic emission technology in the damage characterization of FRP in recent years was reviewed. By conducting research on AE technical means such as parameter analysis, waveform analysis, pattern analysis, and deep-learning analysis, the results showed that parameter analysis and waveform analysis could complement each other in terms of signal characteristics during the detection process, achieving a qualitative description of damage behaviors such as the deformation and fracture of composite structures. Methods such as deep-learning analysis provided important theoretical support for the health monitoring and life prediction of fiber-reinforced composites. Overall, acoustic emission technology can monitor and evaluate the composite structures in operation in real-time. It has great development potential for maintaining the health of FRP materials and preventing sudden failures. In the future, it can be further combined with artificial intelligence technology to improve the accuracy and efficiency of damage identification.

Geothermal tail water reinjection is the main bottleneck that restricts the development and utilization of geothermal resources in Lanzhou Basin. In order to break through the technological gap of geothermal tail water reinjection in Lanzhou Basin sandstone-type thermal storage, relying on the geothermal heating demonstration project in Pengjiaping, Lanzhou City, for the first time, the natural reinjection experiment with graded flow rates of 15, 20, 25, 28 m³/h and graded pressure pressurised reinjection experiment of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 MPa were designed and carried out, the maximum stable natural reinjection volume of 27.96 m³/h and the stable reinjection volume of 51.68 m³/h for 0.667 MPa pressure were firstly obtained from the sandstone-type thermal storage of Pengjiaping, Lanzhou Basin, and the impact of geothermal tail water reinjection on the water level, water temperature, and temperature field of the extraction wells was also investigated. After a heating season of productive reinjection experiment verification, a set of suitable and feasible sandstone-type thermal storage geothermal tail water reinjection technology process has been successfully explored in Lanzhou Basin, which is of great reference and significance for the large-scale and high-quality development and utilization of geothermal resources in Lanzhou Basin.

Corn is one of the important grain reserve crops in China, and its yield directly impacts national food security. The chlorophyll content of corn is closely related to its photosynthetic capacity and significantly affects the photosynthetic rate of the leaves and vegetation productivity. It is an important crop parameter for monitoring crop growth, pest and disease surveillance, and maturity prediction. Real-time and accurate monitoring is of great significance for corn parameters and yield prediction. This study was conducted in the typical black soil area of Lishu County, Siping City, Jilin Province. To solve the problem of missing effective images that may occur during the revisit period of Sentinel-2 satellites, a method for retrieving corn leaf chlorophyll based on the fusion data of Sentinel-2 and MODIS images was proposed. Using fused imagery, three machine learning algorithms were employed: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) to construct a model for estimating corn leaf chlorophyll content, and the accuracy of the model was verified. The conclusions obtained were as follows. The data simulated using the ESTARFM data fusion algorithm maintained a high correlation with the real imagery. Among the leaf chlorophyll inversion models for missing image dates, where input variables included fused image band reflectance and vegetation index, the XGBOOST model showed good fitting accuracy The research demonstrates that accurate estimation of leaf chlorophyll content can be achieved even on days with missing imagery, when fusion image feature bands are integrated with machine learning algorithms. This notably improves the temporal precision of corn chlorophyll content measurement, presenting a novel method for daily or large-scale inversion studies of leaf chlorophyll content, particularly in scenarios involving image gaps. Furthermore, it illuminates the potential for refined monitoring of physiological and biochemical parameters across a wider range of crops, with shortened time intervals.

In order to ensure the safety and reliability of the structural connection of assembled bridges, the strength test design of different types of interface agents was carried out based on bridge engineering and structural mechanics, and the mechanical properties of different cement grades, water-cement ratio and ash-sand ratio were analyzed. The change law of the strength of cement mortar in the early stage is faster than that in the later stage. The change law of the compressive strength of different types of interface agents with different ages was obtained, the prediction model of the relationship between different ages and strength of cement mortar was constructed, and the optimal mechanical properties of SS-III were proposed from the perspective of the bending-compression ratio. The relationship between different interface agents and the tensile strength of adhesion splitting was tested and analyzed by developing the test device of adhesion splitting tensile strength. The bonding performance of SS-III was determined to be the best interface bonding agent for assembled bridges from the perspective of the bending ratio and bonding properties. cement mortar as the interface agent for assembled bridges is more reasonable, which provides a new research idea for the safety and reliability analysis of the interface connection of assembled bridges.

The injection head is the key equipment to drive the coiled tubing in the coiled tubing operation machine. A fault occurred during the operation of a LG450 injection head, and it was found that the chain drive system was seriously damaged after disassembly. The macro and micro morphology analysis of the chain drive system shows that there are arc-shaped scratches on the outer side of the gear teeth and inclined scratches on the tooth surface. The chemical composition test, hardness test and metallographic test were carried out on the sprocket teeth, and the results showed that the material met the process requirements. The finite element simulation of the meshing process of the sprocket and the chain is carried out. The results show that the contact pressure distribution is consistent with the friction marks on the tooth surface and side of the failed sprocket. It is revealed that the failure reason of the chain drive system of the injection head is the spatial intersection of the chain roller and the sprocket axis, and the abnormal meshing of the roller and the gear teeth. This study shows that the motion state of the sprocket chain will have an important impact on the safety of the injection head, which is of great significance to guide the design and processing of the injection head.

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.

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.

In order to solve the problem that the tunnel crosses the water-rich fault fracture zone, the construction risk is large, and the problems of surrounding rock instability and water inrush are very likely to occur. Based on the engineering background of the Xiaocaoba tunnel of the Chongqing-Kunming high-speed railway crossing the water-rich fault fracture zone, the fluid-structure interaction numerical model was established by using FLAC^3D to study whether to consider the influence of groundwater action, different grouting forms, grouting ring thickness and the force of the supporting structure under the influence of groundwater. The results show that the stability of the surrounding rock is poor under the consideration of groundwater, and after the tunnel excavation, the groundwater is distributed in a “funnel-shaped” manner around the tunnel after the seepage reaches a steady state, and the stability of the surrounding rock is enhanced after the advanced grouting reinforcement, and the grouting reinforcement form of the whole perimeter is better than that of the grouting around the arch wall. On the basis of selecting the grouting form, the parameters of the grouting ring were continuously optimized, and it was concluded that with the increase of the ratio of grouting ring thickness and permeability coefficient, it can effectively reduce the displacement of surrounding rock, limit the development of plastic zone, and reduce the pore water pressure of the primary branch, and the increase of the thickness of the grouting ring can significantly change the distribution range of the pore water pressure of the primary branch. After the construction of the appropriate grouting scheme on site, the feasibility of the grouting scheme and the rationality of the selection of grouting parameters were verified by comparing the monitoring values around the tunnel with the simulated values, and the stability of the surrounding rock was effectively controlled. The research results can provide reference value for the design and construction of similar tunnel projects in the future.