The slewing and luffing coupling motion of tower cranes can easily induce structural vibrations in the crane mast and swing angles of the payload, potentially leading to operational faults. To investigate these vibration patterns, an elastic crane model was developed under combined slewing and luffing dynamic motions, incorporating Lagrange dynamics, air resistance, and beam deflection. The model was analyzed across phases from acceleration to constant speed and then to deceleration. The effectiveness of this nonlinear coupled motion model was validated using a designed experimental platform. The study examined the effects of varying accelerations and initial swing angles. The results indicate that luffing acceleration influences structural vibration, while slewing acceleration has a significant impact on it. Additionally, initial angles greater than or equal to 0.2 rad greatly affect structural vibration. When the slewing acceleration exceeds 0.04 m/s², the frequency of mast vibration increases. Understanding the structural vibration law during coupled motion is crucial for enhancing the design of dynamic systems.

The velocity measurement of trunk canals and rivers is regarded as an important basis for water resources management and flood prediction. The techniques and methods for trunk canals velocity measurement based on machine vision were analyzed and synthesized. Particular focus was placed on reviewing the principles, technologies, and recent developments of particle image velocimetry, particle tracking velocimetry, space-time image velocimetry, optical flow methods, and deep learning-based flow measurement methods in recent years. Finally, the existing challenges and issues were addressed, and potential future development directions were proposed.

Schizophrenia is a persistent mental disorder manifested by significant abnormalities in perception, emotion, and behavior. Nevertheless, the neural mechanisms underlying this disorder are still not fully understood. In order to explore the differences in whole-brain causal connectivity between patients with schizophrenia and healthy controls in the resting state, a hierarchical degree (HD) index was proposed based on eigenmode method to overcome the inadequacy of node degree measured at a single level in traditional graph theory. It was found that the node degree of the whole-brain causal network of schizophrenia patients reduced. In addition, the most significant changes in in-degree were found in the motor system, whereas the most significant changes in out-degree were found in the default mode system. Higher-order node degree was further extracted and found to be superior to traditional graph theory degree in distinguishing schizophrenia patients from healthy controls based on a machine learning approach, and more accurately predicted positive and negative symptoms of schizophrenia, suggesting that higher-order network features can be used as biological indicators of schizophrenia. The findings of this paper reveal abnormal higher-order network features of schizophrenia and contribute to the advancement of objective diagnostic technologies for schizophrenia.

Aiming at the nonlinearity and multi-disturbance problems of the electric regulating valve control system in the actual production process, a control method based on the improved ant colony algorithm to optimize the single neuron PID (proportional integral derivative) was proposed and applied to the valve opening control. The self-learning and self-adaptive ability of the single-neuron network was used to achieve the online tuning of PID control parameters. The improved ant colony optimization algorithm was adopted to optimize the learning rate and neuron ratio coefficients in the single-neuron PID, which effectively overcomed the shortcomings of the single-neuron PID where the learning rate and neuron ratio coefficients could not achieve the expected control effect due to the empirical setting. The simulation comparison results show that, compared with the traditional PID, single neuron PID, and single neuron PID based on ant colony optimization algorithm optimization of the three control methods, the control method proposed overshoots the amount of reduction of 10.2%, 6.1%, and 1.8%, respectively. At the same time, the regulation time is correspondingly shortened by 0.22s, 0.07s, and 0.03s. It shows a stronger adaptive and anti-interference ability, which can make the valve opening control more stable and reliable.

Complex equipment such as wind turbine blades faces both performance degradation and random shocks. There are two interdependent relationships between these two failure modes: the interdependence between internal factors in the degradation process and the interdependence between degradation and shock processes. These characteristics pose challenges to reliability analysis. To solve this problem, a new mutually dependent competing failure processes (MDCFPs) model was proposed based on two MDCFPs models. This new model integrated two interdependencies. Taking the wind turbine blade stiffness degradation model based on Gamma process and the extreme shock model based on homogeneous Poisson process as examples, the accuracy and differences of three models were analyzed using the control variate method, and the influence of key parameters was studied. The results show that, under the same conditions, the new model's reliability is closest to the observed empirical reliability, with an absolute error of no more than 0.12. At the same time, the new model's reliability is lower than that of the two base models, with maximum absolute errors of 0.26 and 0.40, respectively. After adjusting the parameters of the new model, the absolute errors in reliability compared to the base models are limited to 0.03 and 0.02. These findings suggest that the new model effectively accounts for interdependencies among factors, prevents overestimation of reliability, and can replace base models, demonstrating broader applicability.

Bearing local fault occurs in the turboprop engine reduction gearbox, which affects engine running safely. Due to the complex structure and high speed of the reducer gearbox, the vibration signal used to monitor the operation state of the mechanical components is complicated. In order to detect the bearing local fault signal in time and extract fault feature accurately from the vibration signal, a method which was based on vibration signal combining FFT, fast kurtogram and envelope spectrum was proposed. Firstly, during the engine operation, the FFT(fast Fourier transform) spectrum was used to detect whether there was bearing fault component in the vibration signal; and then the fast kurtogram was applied to determine the frequency band distribution of the fault component; finally, the bearing fault characteristic frequency was acquired through envelope spectrum analysis. In the course of certain type of turboprop engine ground bench test, this method was used to accurately detect and diagnose the local spalling fault of the inner raceway. Therefore, this method can provide a basis for the engineering application on local fault detection and diagnosis of bearings in aero-engine reduction gearbox.

Network intrusion detection systems (NIDS) are critical for maintaining cybersecurity. However, due to the complexity of network traffic data and the issue of class imbalance, existing detection models often exhibit high false alarm rates and insufficient detection accuracy for different types of attacks. To address these challenges, an imbalanced learning method for network intrusion detection, based on topological data analysis (TDA) and named TopoSMOTE, was proposed. This method aims to balance the training dataset by generating new minority class samples. The core of TopoSMOTE lied in constructing topological graphs to synthesize new samples. Firstly, the method used TDA to map the spatial relationships and connection patterns in network traffic data, forming a topological graph. Then, based on the topological graph, a minority class sample selection strategy was designed, which synthesized new data by selecting the nearest neighbor samples with topological relationships in a low-dimensional mapped space. Experiments were conducted on two imbalanced datasets. The experimental results show that the TopoSMOTE method achieves higher detection accuracy and lower false alarm rates compared to advanced oversampling methods and intrusion detection models.

The accurate prediction of soil compaction parameters has practical significance for improving soil bearing capacity and reducing compressibility in geotechnical engineering. The existing models have certain limitations in prediction progress and engineering applicability, and ignore the quantification of model prediction uncertainty. Genetic programming (GP) was used to model and predict two important soil compaction parameters (optimal water content and maximum dry density) for 226 groups of soil compaction test data with extensive and representativeness. The optimal display models of optimal water content and maximum dry density were obtained respectively, and the prediction results were compared with the results of existing prediction models. The GP model was quantified by combining quantile regression method and uncertainty statistics. The results show that the compaction parameters are most affected by fine grain content and plastic limit, while the gravel content and liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content and plastic limit, while the gravel content (CG) and the liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content (CF) and the plastic limit in the soil. In addition, the quantile regression (QR) method provides 90 % confidence and the mean prediction interval (MPI) is less than 0.3.At the same time, most of the data fall within the range of uncertain bands, indicating that the GP algorithm has strong prediction ability and high prediction accuracy. This interpretable display model is more convenient for engineering applications.

In order to prevent and control the snow drifting disaster along the Karamay-Tacheng Railway in northwest of Xinjiang, numerical simulation method was adopted to study the influence of different height and angle parameters of wind deflector on the snow flow field around a given railway embankment in 50-year recurrence period in this paper. The results show that increasing the angle between the wind deflector and the prevailing wind direction will weaken its effect on snow dispersion in the 50-year recurrence period. When the angle is set to 60°, the snow dispersion effect is the best. Increasing the height of the wind deflector will increase the coverage of the acceleration zone, which is more conducive to disperse snow flow and reduce the probability of snow particle accumulation. When the height is set to 2 m, it's a cost-effective solution.

In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.