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.

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.

When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM (convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy.Compared to canonical correlation analysis (CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.

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.

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.

With the rapid development of industry and agriculture, the situation of Cd pollution in farmland soil is severe. Cd pollution in farmland will directly or indirectly have adverse effects on soil ecological security, crop growth and human health development. Cd has received extensive attention due to its strong biological toxicity and easy migration and accumulation, and effective management and control of Cd pollution in farmland is urgently needed. At present, the remediation technology of Cd pollution in farmland soil has been gradually developed and enriched, but the summary analysis of the remediation technology is relatively lacking. The characteristics and hazards of farmland soil Cd contamination were analyzed, focusing on the current status and sources of farmland Cd pollution. Based on this analysis, the principles, characteristics, and applicable scopes of remediation technologies for Cd-contaminated farmland soil were summarized. Case studies were used to compare the practical application effects of these remediation technologies. Additionally, the advantages, disadvantages, and limitations of various remediation approaches were examined, providing theoretical references for the prevention and remediation of farmland soil Cd pollution and promoting high-quality development of agriculture.

The construction decision of special geological area is very important and complicated. The uncertainty of geological conditions will directly affect the selection and implementation of construction scheme. In order to solve the non-equilibrium problem among the “five control” objectives (time limit, cost, quality, safety and environmental protection) of the bridge construction scheme in the complex special geological area of Southwest China, the network planning technology and BIM(building information modeling) were combined. BIM visualization technology, fuzzy set theory and GRA(grey correlation analysis) were integrated into the optimization of bridge construction schemes in complex special geological areas. HFMD(hybrid fuzzy multi-attribute decision-making model) based on duration-cost-quality-safety-environmental protection was established, and a visualization system of construction process was constructed by using Python and BIM technology. Assisted managers to make decisions, and the result met the “five control” index comprehensive optimization scheme, and the construction period was advanced by 10 days, and the cost was reduced by 3.1%, which proved the practicability and effectiveness of this model A and method. It provides a reference for the decision of bridge construction scheme in complex special geological area.

To solve the problem of bearing fault and complex sound field environment, taking H7009C full ceramic angular contact ball bearing as the research object, the dynamic analysis model of full ceramic angular contact ball bearing was established, and the error of theoretical calculation and simulation of rolling body was compared to verify the validity of the model. Based on the transient dynamic analysis of the influence of inner ring fault on the bearing dynamic characteristics, the surface SPL (sound pressure level) of bearings caused by different faults was calculated, and the SPL characteristics of high-speed bearings were compared under the action of variable speed and variable load. The results show that the sound pressure level of the faulty inner ring bearing increases with the increase of speed, and increases first and then decreases with the increase of load. When the ratio of the lowest sound pressure level frequency to the vibration frequency is close to 0.75 or the ratio of the highest sound pressure level frequency to the vibration frequency is close to 4, it can be identified as an inner ring fault.

The flooding cap is the key equipment for the pre-commissioning of subsea pipeline. Its deep sea installation operation has high risks and strict requirements, which puts forward higher requirements for the safety performance of structural strength. In addition to bearing loads during installation, the flooding cap also needs to block huge internal pressure in the pipeline during pressure test. Its structural strength and bearing capacity directly affect the safety and reliability of the whole subsea production system pre-commissioning. The flooding cap used in a 1 500 m deep gas field in the South China Sea Lingshui area was taken as the research object. Based on the relevant standards of DNV and NORSOK, elastoplastic finite element modeling of the flooding cap and key pressure components was carried out to analyze the safety strength requirements under different working conditions. The results show that the maximum Von Mises stress of each component of the flooding cap is less than the allowable stress under lifting conditions, and the high stress is mainly concentrated at the bolt connection. Under the impact condition, the flooding cap simulation can meet the installation speed requirement of 0.5 m/s, and the overall structural strength can meet the relevant standards. Under pressure testing conditions, the pressure capacity of the flooding cap is calculated to be 823 bar, and the measured pressure during the pre-commissioning operation of the Linsgshui gas field in the South China Sea is 268 bar, which is far less than its pressure capacity, satisfying the finite element calculation results. The relevant research results can provide theoretical basis and technical reference for the design and field application of flooding cap.