Rumors mislead judgments of people, affect economic development, and the stability of social order. The research on the rule of spreading rumors is significant and meaningful. This paper improves the traditional Barabási–Albert scale‐free network and proposes a network topology model that conforms to the characteristics of sharing social networks based on the complex network theory and the actual characteristics of sharing social networks. In addition, the credulous spider rational taciturn rumor propagation model is proposed by improving the credulous spider rational model, which solves the overspread problem of the traditional rumor propagation model. This paper further studies the influence of anxiety on the spread of rumors, and finds that the anxiety of audience is increasing with the spread degree of rumors.

The AlGaN/GaN/AlGaN double heterostructure (DH) with high electron mobility of 1862 cm2/Vs at room temperature and 478 cm2/Vs at 573 K high temperature was obtained by a combination of optimization schemes considering scattering mechanisms. First, a composite buffer layer structure, including GaN and AlGaN layer, was used to improve the crystal quality of the AlGaN/GaN/AlGaN DH. Second, interface roughness scattering was reduced by increasing the channel thickness, thus the two-dimensional electron gas mobility was further improved. Moreover, an ultrathin AlN interlayer was inserted between the GaN channel layer and the AlGaN buffer layer to decrease the alloy disorder scattering. The Hall effect measurements showed that the DH had better transport characteristics at high temperatures, and an electron mobility of 478 cm2/Vs was achieved at 573 K, which is twice larger than that of the conventional single heterostructure (∼200 cm2/Vs at 573 K). Therefore, AlGaN/GaN/AlGaN DH is more suitable for the applications in high temperature electronic devices.

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The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.

When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over‐fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high‐rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high‐rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large‐span building type wind tunnel tests.

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Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single-source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system.To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed

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