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To solve the problem of low recognition accuracy of radar active jamming in strong noise environment,an algorithm for ER-C-L(Extended ResNet-CNN-LSTM) network model based on one-dimensional composite features is proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features. Then, the complex features are injammed into the ER-C-L network to identify six new active jamming models. Simulation experiments show that the recognition accuracy of jamming is 98.5% in strong noise environment within the JNR of -10 dB. Compared with other deep learning algorithms such as CNN, ResNet-CNN, extended ResNet-CNN and LSTM, it has higher interference recognition accuracy.

As one of the most critical and failure-prone parts of the mechanical systems of industrial equipment, bearings are subjected to high loads for long periods of time. When they fail or wear irreversibly, they may cause accidents or even huge economic losses. Therefore, effective health monitoring and fault diagnosis are of great significance to ensure safe and stable operation of industrial equipment. In order to further promote the development of bearing health monitoring and fault diagnosis technology, the current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from the distribution of vibration signal data used, firstly, the relevant methods under uniform data distribution are sorted out, the classification, analysis and summary of the current research status are carried out mainly according to signal-based analysis and data-driven-based, and the shortcomings and defects of the fault detection methods in this case are outlined. Secondly, considering the problem of uneven data acquisition under actual working conditions, the detection methods for dealing with such cases are summarized, and different processing techniques for this problem in existing research are classified into data processing methods, feature extraction methods, and model improvement methods according to their different focuses, and the existing problems are analyzed and summarized. Finally, the challenges and future development directions of bearing fault detection in existing industrial equipment are summarized and prospected.

BackgroundNatural circulation systems are widely used in the nuclear industry for decay heat removal, post-accident containment cooling, and the cooling of radioactive waste storage facilities. Because a large number of spent fuel assemblies are densely arranged in the spent fuel pool, the spent fuel assemblies in the central area of the spent fuel pool can be approximated as having adiabatic boundary conditions. In the event of a coolant loss accident, the heat generated by the spent fuel assemblies can be exported only by the natural circulation of air.PurposeThis study aims to develop analytical models for fuel assembly natural circulation characteristics independent of the results of computational fluid dynamics (CFD) to investigate the natural circulation characteristics of spent fuel pools following a coolant loss accident.MethodsBased on the experimental results of a fuel assembly pressure drop of a full-scale pressurized water reactor (PWR), the Darcy–Forchheimer model was firstly revised to predict the pressure drop of humid air flowing through the fuel assembly. Models for the fuel assembly natural circulation flow rate and peak cladding temperature were then established, and the effects of the relative humidity of air on the models were considered. Finally, these models were applied to investigating effects of total heating power, ambient temperature, and relative humidity on the natural circulation flow rate and peak cladding temperature of the fuel assembly.ResultsThe results show that the models accurately predict the natural circulation flow rate and peak cladding temperature of the fuel assembly under different heating powers, and the errors are less than 25% and 20%, respectively, as compared with the experimental measurement values.ConclusionsThe developed models in this study can be used to study the natural circulation characteristics of full-scale PWR fuel assemblies.

This study explores the innovation and entrepreneurship capabilities of college students who return to their hometowns, particularly within China’s rural revitalization strategy. By constructing a comprehensive framework, this paper examines four key dimensions that influence these abilities, employing logistic regression analysis to assess the impact of various factors. Notably, including policy support as a variable in Model 2 significantly enhances the Model’s explanatory power by 22.4%, a statistically significant finding (p < 0.001). This underscores the critical role of policy support in bolstering students’ innovation and entrepreneurial skills. Further, through applying structural equation modeling, this research delves deeper into the nexus between policy, institutional environments, and the cultivation of core entrepreneurial competencies, offering insights into the external factors that facilitate competence development. The Model’s hypotheses are rigorously tested for fit, confirming the effectiveness of the proposed framework in understanding and supporting the entrepreneurial endeavors of returning college students.

BackgroundPassive residual heat-removal system (PRHRS) based on natural circulation has been widely used in small reactors connected on secondary side loop.PurposeThis study aims to develop and analyze a passive residual heat-removal system on the secondary side of a small reactor to improve reactor safety.MethodsBased on the completed heat-transfer experiment of the intermediate circuit of a small integrated nuclear power plant, Reactor Excursion and Leak Analysis Program (RELAP) was employed to determine and analyze the natural circulation characteristics of the intermediate loop were determined and analyzed.ResultsThe heat transfer rate results of the program are in good agreement with the experimental data, and the natural cycle characteristics of PRHRS can be characterized. The pressure of the system loop is determined by the average temperature of the primary side of the steam generator (SG), and the difference in the inlet temperature of the SG primary side, mass flow rate, and height of the cooling and heating sources significantly influence the heat-transfer performance of the SG system. The heat-transfer performance of the waste-heat-removal system is more sensitive to the resistance of the system loop when the temperature of the primary-side inlet of the SG is higher.ConclusionsThese results provide a valuable application for further investigations of passive systems in small reactors.