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Objective To establish a human tonsil organoid model and investigate the immunological effects of this tonsil organoid infected by influenza virus. Methods Human tonsil tissue removed by adenoid hypertrophy surgery were subjected to gradient centrifugation, and then the obtain cells were cultured in vitro with using a 2.5D-Transwell chamber in combination with IL-2 and B cell activating factor (Baff) to construct a human tonsil organoid. Single cell transcriptome sequencing (scRNA-seq) was used to analyze the distribution of influenza virus receptors in different cell subsets of the tonsil organoid. Immunofluorescence assay and flow cytometry were applied to detect the immunological effects of the tonsil organoid in 48 h after influenza virus infection. Results The tonsil organoids were established successfully using this 2.5D-Transwell culture technique. scRNA-seq analysis showed that the receptors for influenza virus were extensively distributed in nearly all of cell subsets of tonsil organoids. In 48 h after influenza virus infection, the tonsil organoids could generate a large amount of plasma cells and memory B cells to produce specific IgG antibodies. In addition, direct infection of the virus promoted the production of TNF-α and IL-2 from CD8+ T cells and the secretion of TNF-α, IFN-γ and IL-2 by CD4+ T cells. Conclusion The tonsil organoids can be established successfully and reach maturity at about the 6th day after culture. The receptors for influenza virus are widely distributed in human tonsil organoids. Influenza virus directly infects human T cells, leading to T cell activation and cytokine releasing. Moreover, influenza virus infection also promotes B cells differentiate into plasma cells and induce the secretion of IgG as well as the formation of memory B cells.

Power system planning, load forecasting, and energy utilization analysis are significantly impacted by power load anomalies, necessitating prompt detection and identification. Firstly, the abnormal classification, causes, and characteristics of power load data are analyzed. Secondly, it enhances the traditional Transformer encoder structure by replacing the mask multi-header attention layer with the multi-header attention layer and eliminating the feedforward network. These improvements aim to enhance the model's global attention to the load sequence. To capture trend characteristics more effectively and expedite convergence, an improved generative adversarial networks (GAN)-Transformer model is proposed based on the generator and discriminator game structure of traditional GAN. Additionally, a multi-stage mapping and training approach along with an integrated focus score scoring mechanism are introduced. These techniques facilitate phased load sequence reconstruction, enabling the model to better extract anomalous features of the load data. Finally, through an analysis of an arithmetic example, the results demonstrate that the GAN-Transformer model outperforms in terms of load data anomaly detection accuracy, recall, F1 value, and training time. These results validate the effectiveness and superiority of the proposed method. The findings of this research provide valuable insights for advancements in power load data anomaly classification and data repair based on deep learning.

The effective development of Civic Education work in colleges and universities can promote the cultivation of high-quality talents. Starting from Internet information technology, this paper constructs a digital platform for Civic Education work, in which Shannon’s theory and balanced power allocation are used to optimize channel transmission and enhance the loading speed of Civic Education resources. The XGBoost algorithm is used to effectively cluster students’ learning behaviors as a way to help Civic Education workers understand students’ learning behaviors in Civic Education. The results show that the average response time is 0.327s, and the maximum throughput is 11849.478 bytes/sec. The prediction accuracy of the XGBoost algorithm for student learning behavior is 4.65%, 9.79%, and 21.79% higher than that of LR, RF, GBM, and Stacking classification models, respectively. 9.79%, 21.41%, and 13.5%, respectively. This shows that the university’s thinking and political education work can be developed through the direction of the digital Internet platform, which in turn provides a new research path for the reform and innovation of thinking and political education work.

Replica symmetry breaking (RSB) has been widely recognized as a statistical analysis approach to understand the disorder and nonlinear interactions in complex systems ranging from atoms to the cosmic scale. However, it is challenging to analyze the nonlinear optical characteristics of random laser (RL) in disordered gain medium via RSB due to the lack of a general RSB-based statistical analysis framework. In this work, we report the tunable RSB in polymer fiber RL, where the effects of temperature and different structures on RSB are investigated experimentally and theoretically. It experimentally proves that RSB in RL is not robust, and disorder and temperature are responsible for tunable RSB in RL, which contributes to the improvement of the statistical analysis framework for investigating the optical principles of RL using RSB. And the finding of the tunable RSB allows to investigate the dynamical differences for various RL systems, which broadens the directions for the use of spin-glass theory to explore the physical mechanism of RL.

In recent years, with the popularization of intelligent auxiliary driving technology, wireless charging technology has garnered widespread attention due to its advantages in automation. However, there are still many issues to be resolved before the widespread adoption of wireless charging technology, with a key issue being the interoperability of coupling structures. To achieve adaptive interoperability, this paper proposes a wireless charging system based on dual generalized double-D (DD) coils. The system introduces a receiving structure composed of two generalized DD coils, which can naturally decouple the two coils. At the same time, the system topology employs a series connection of half-bridge rectifiers to enhance the system’s equivalent mutual inductance. Mathematical models and simulation verifications are provided. The proposed approach has been validated through simulations, showing that the system can automatically achieve interoperability with unipolar coils, DD coils, and quadrupolar coils.

In order to improve the rolling bearing fault diagnosis accuracy,this paper presents a fault diagnosis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Convolution Neural Networks（ CNN）. At first,using the EEMD decompose the signal. After that,choose appropriate IMFs according to the correlation coefficent and kurtosis calculating results to reconstruct the signal. After calculating a series of indexes of reconstructed signals,using CNN and various methods to diagnose faults. The results shows that the method used in this paper can effectively carry out fault diagnosis. The accuracy can reach 96. 7%. It has certain application significance to fault diagnosis.

The frost heave characteristics of artificial frozen soil are very important information for underground freezing engineering. It is found that both the frost heaving force and rate of the same soil layer increase with the decrease of freezing temperature. In addition, due to the comprehensive influence of freezing temperature, natural water content, dry density, and tidal flow peak value, the frost heave characteristics of different soil samples are evidently uncertain. With the aim of improving the deficiency of traditional BP neural network algorithms in solving fuzzy random engineering problems, random factor and mean square error between layers are used to modify the evaluation function of the network model. On this basis, taking tidal flow peak value, freezing temperature, natural water content, and dry density as inputs, the frost heaving force and rate of frozen soil as output values, and setting the number of hidden layer elements as 4, an improved fuzzy random BP network prediction model for frost heave characteristics was established. The new network prediction model has a smaller weight and bias, and the response tends to be smoother than the traditional one, which greatly reduces the overfitting phenomenon. The engineering example shows that the improved BP neural network prediction model can make the predicted value of frost heaving force and rate basically coincide with the measured value after effective training, and the error is controlled within 8%. Therefore, the prediction model can be used as an effective tool to predict frost heaving characteristics in Nantong metro freezing construction, and the corresponding model and method can also be extended to similar engineering cases.

To meet the requirement of practical teaching of electromechanics course, a model inverter for the school laboratory is designed, which converts the power supply into another arbitrary frequency electric energy to drive the three-phase asynchronous motor to rotate. The motor control system can control the soft start of the motor. Variable voltage and variable frequency speed regulation can be carried out through voltage-frequency conversion circuit and pulse width modulation(PWM) circuit; taking IR2104 as core, the motor drive system uses high-power field effect transistor to convert the weak electric signal which controls the motor into the high-voltage energy which drives motor; motor protection system can detect overcurrent, short circuit, phase gap and overheat, and cut off power supply when necessary to provide protection. The function test and application validation show that the system meets the expected requirements and can be applied to relevant teaching experiments.

Spreading dynamics of liquid droplets impacting onto powder bed are experimentally studied using high-speed photography. Dimensionless numbers—We, Re, the modified We* and Re∗ corrected by substrate deformation—are used to analyze the impact behaviors of droplets. The spreading time and the maximum spreading factor are further analyzed. The spreading time is accurately described by a universal scaling law that is obtained from the modified dimensionless time vs. the effective Weber number (We∗), and the maximum spreading factor is found to follow the modified classic scaling law βmax = f(We*, Re*).