The extremely short-term prediction of seaplane motion can provide the rocking motion posture over the next few seconds, which is considered essential for ensuring safety during take-off and landing phases under adverse wind and wave conditions. Although some research has been conducted on extremely short-term prediction methods for seaplane motion, limited attention has been given to analyzing differences in the applicability of various methods. In this context, the NACA TN 2929 aircraft was taken as an example, and the three degree of freedom motion simulation data under typical working conditions were calculated based on potential flow theory. To compare the forecasting performance under different forecasting conditions, three typical extremely short-term prediction of seaplane motion models, namely AR (auto-regressive), LSTM (long short term memory), and TCN (temporal convolutional network), were constructed. The results show that compared to the AR model, the LSTM and TCN neural network models exhibit superior forecasting accuracy for longer prediction durations, effectively enabling accurate predictions of the heave, roll, and pitch motions of the seaplane at the ten-second level, providing a valuable theoretical reference for the selection of seaplane motion prediction algorithms.

Crack detection is crucial to maintaining the structural safety of buildings. In recent years, convolutional neural networks based on deep learning have provided new solutions for crack detection. However, this comes at the cost of huge computing resources, so there are problems of poor real-time performance and low detection efficiency in practical applications. To address this problem, a lightweight MSFC (multi-scale dynamic fusion convolution module) based on the U-Net architecture was proposed to improve the efficiency of crack segmentation. To verify the effectiveness of the proposed method, a dataset Crack2045 containing 2 045 crack images was constructed and experiments were conducted on this dataset. The experimental results show that compared with the original U-Net model, the model using the MSFC module reduces 78.51% of the parameters and 63.75% of the computational complexity while maintaining the same accuracy. At the same time, the MSFC module has a certain degree of generalization and can be seamlessly integrated into different semantic segmentation models. This study not only provides an efficient deep learning method for crack detection, but also provides new possibilities for model deployment in resource-constrained environments.

To address the reduction in support bearing capacity caused by concrete shrinkage and to improve the damping performance of supports under mining-induced seismic conditions, this study focuses on the energy-absorbing and shock-resisting performance of single arch sand-filled steel tubular frames and the damping performance of multi-arch combined support systems. Finite element software was employed to establish models of surrounding rock and sand-filled steel tubular frames, as well as multi-arch support systems connected with flexible cables and dampers. The performance of sand-filled steel tubular frames under static and dynamic loading, as well as their seismic resistance under mining-induced tremors, was investigated. The results indicate that the deformation of the tunnel under static loading remains stable, while the support effectiveness is satisfactory under impact loading except for relatively large deformations at the crown. Under static and dynamic loading, the equivalent plastic strain at the crown of the sand-filled steel tubular frames shows a significant increase, while changes in other areas remain minimal, demonstrating good load-bearing capacity. In the three-arch support system, the third arch experiences reduced vibration amplitude due to the dual energy dissipation effects of flexible cables and dampers. Calculations of the safety factor at the maximum shear stress of the tunnel reveal a significant improvement in the seismic performance of adjacent supports, providing insights for further studies on support damping mechanisms.

The hybrid DC transmission system has problems such as inconsistent boundary components, inconsistent fault response characteristics, difficult resolution of high resistance fault effects, and low accuracy in identifying near end faults, which reduces the reliability of protection schemes. Therefore, the phase characteristics of the regional refractive index of the hybrid DC transmission system were analyzed for the first time, and a single ended protection scheme suitable for hybrid boundaries was proposed based on this. Firstly, establish a hybrid DC transmission system model and analyze the traveling wave transmission characteristics of different fault types. Subsequently, the fault areas of the hybrid DC transmission system were divided, and the refractive index expressions and phase frequency characteristics of the areas were derived separately. Finally, a single ended protection scheme based on a specific frequency refractive index is proposed and its performance is tested. The test results show that the proposed protection scheme not only has the speed of traditional protection schemes, but also has better resistance to high impedance faults, noise interference, and other abilities.

In recent years, the scale of wind turbine grid connection has been increasing, for the deep learning of wind speed prediction requires a large amount of data, as well as stochastic differential equations for wind power system modeling fail to portray the impact of wind speed correlation on the output power and grid-connection point voltage, a Markov switching stochastic differential equation modeling method considering stochastic factors and wind speed correlation was proposed for power systems containing wind power. The Nataf and LSTM were introduced to construct the wind speed spatio-temporal correlation model, the Markov switching stochastic differential equation was used to segment and linearize the wind power system into various linear segments. Then the effects of wind speed correlation and stochastic excitation strength on the voltage at the grid-connection point were studied, and the critical stable excitation strength of the wind power system was analyzed. Finally, the stochastic simulation of the constructed system model was carried out by numerical analysis methods, and the results show that the system state variable fluctuates in the stable region within the critical value of the random excitation intensity, and the comparison with the stable waveform of voltage in the Simulink simulation circuit verifies the validity of the modeling method in this paper, and provides a theoretical basis for the stability analysis of the new wind farm access to the power system.

In traditional blind deconvolution algorithms, recalculating the gradient or redesigning the optimization approach for filter coefficients becomes necessary when altering the characterization index. This requirement can render the development process of new blind deconvolution algorithms inflexible. To address these issues, a blind deconvolution algorithm that employs NRO(Newton-Raphson optimizer) to seek out the optimal filter coefficients was proposed. Initially, generalized spherical coordinate transformation was used to define the search range for the filter coefficients. Subsequently, the generalized l_p/l_q norm of the envelope spectrum was adopted as the characterization index. The proposed blind deconvolution algorithm is then utilized for the early detection of minor faults in rolling bearings. Both simulation and experimental results confirm the efficacy of the proposed algorithm, demonstrating its faster convergence rate compared to classical PSO(particle swarm optimization).

Further analysis is needed to comprehend how the trend of land subsidence in the Beijing plain area evolves following the implementation of a series of prevention and control measures. Based on the Sentinel-1A image data from 2017 to 2022, the PS-InSAR technique was employed to assess the current situation of land subsidence in the plain area of Beijing, and the geographical detector was utilized to analyze the main influencing factors of land subsidence and their interaction effects. The findings reveal the following: The main conclusions were as follows. The distribution of land subsidence in Beijing plain is uneven, and the maximum subsidence rate reaches 90 mm/a. The subsidence rate of non-funnel area shows a certain degree of slowing trend from 2020 to 2021, while the slowing trend of subsidence rate in funnel area is not obvious. Groundwater as the primary influencing factor of land subsidence, with the thickness of the compressible layer closely following. The interaction among all influencing factors demonstrates a factor enhancement relationship, with the interaction between groundwater and subway infrastructure exerting the most significant impact on land subsidence. This highlights that groundwater and urban construction jointly propel land subsidence in the Beijing plain area. These research findings provide a scientific foundation for the comprehensive assessment, precise prediction, and integrated prevention and control of land subsidence in the Beijing plain.

To determine the presence of sulfhydryl groups on natural aquatic biofilms and their adsorption characteristics for typical heavy metals, a method for sulfhydryl masking in the biofilms based on a specific masking agent was established in this study. Based on this method, surface concentration of sulfhydryl group in natural biofilms and their adsorption characteristics for typical heavy metals, including Cu, Pb, and Cd, at different pH were investigated. The results indicate that the established masking method can effectively mask sulfhydryl groups and has little effect on microorganisms in biofilms. There are relatively low concentrations of sulfhydryl groups on the surface of natural biofilms, with a concentration of (5.8 ± 0.6) μmol/g, accounting for 5.7% of the total site concentration on the biofilms. Despite the low concentration of sulfhydryl groups, their stronger metal binding capacity makes them significantly contribute to metal adsorption when the metal concentration is low (the theoretical loading of heavy metals by the biofilm is less than 1.0 μmol/g). This pattern is essentially unaffected by the pH of the adsorption system and the type of heavy metal. This proves that sulfhydryl groups in biofilms also have an important impact on the behavior and risk of heavy metals in natural aquatic environments with low metal content, further highlighting the environmental significance of natural biofilms.

With the rapid development of the energy industry and technological innovation, a large number of professional terms and expressions are constantly updated, and new words continue to emerge. However, traditional neologism discovery methods often rely on dictionaries or rules, and it is difficult to efficiently process and update a large number of specialized terms, especially in the rapidly changing energy field. Therefore, combined with the characteristics of text data in the energy field, a new word discovery method in ENFM(energy field combining N-Gram and multiple attention mechanism) was proposed. Firstly, the N-Gram model was used to process the text data in the field of energy, and the candidate list of new words was generated by statistics and analysis of word frequency. Subsequently, the ERNIE-BiLSTM-CRF model integrating multiple attention mechanism was introduced to further improve the accuracy and efficiency of neologism discovery. Compared with the traditional neologism discovery technology, the accurate identification and overall efficiency of neologism have been significantly improved. The accuracy rate, recall rate and F₁ value of neologism in the data set of policy text in the energy field are 95.71%, 95.56% and 95.63%, respectively. The experimental results show that this method can accurately identify new words in a large number of text data in the field of energy, effectively identify the specific words and expressions in the field of energy, and significantly improve the recognition ability of professional terms in the field of energy in Chinese word segmentation tasks.

The response of an aircraft engine to bird strikes has the fan blade as its primary component, and the flight safety of the aircraft is directly impacted by the dynamic damage caused by stress changes. A three-dimensional model of a near-real bird body was established in this paper based on the structural features of the “bar-headed goose”. The dynamic damage of the blade was studied in consideration of the take-off-climb and approach-landing stages where bird strike accidents are most likely to occur for aircraft, with the effects of different impact speeds, fan blade speeds, and bird impact attitudes being taken into account. It is indicated by the results that the axial damage and deformation of fan blades tend to be increased monotonically with the increasing of aero-engine speed and relative velocity of bird strike blades. Additionally, as the fan blade speed is increased, the stress peak value after a bird strike shows a V-shaped trend, with the smallest stress peak value being occurred at 2 005 r/min. Furthermore, as the contact area between the bird body and fan blade at the initial collision moment is increased, both the stress and damage degree of the blade are gradually increased across different postures. When impacted at a 90° posture, the axial damage deformation of the blade is reached to 60.887 mm. Valuable reference for anti-bird strike design considerations for aero-engine fan blades is provided by these research findings.