propagation model
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2022 ◽  
Author(s):  
Yuhuai Zhang ◽  
Jianjun Zhu

Abstract In daily lives, when emergencies occur, rumors will spread widely on the Internet. However, it is quite difficult for the netizens to distinguish the truth of the information. The main reasons are the uncertainty of netizens' behavior and attitude, which make the transmission rates of these information among social network groups being not fixed. In this paper, we propose a stochastic rumor propagation model with general incidence function. The model can be described by a stochastic differential equation. Applying the Khasminskii method via a suitable construction of Lyapunov function, we first prove the existence of a unique solution for the stochastic model with probability one. Then we show the existence of a unique ergodic stationary distribution of the rumor model, which exhibits the ergodicity. We also provide some numerical simulations to support our theoretical results. The numerical results give us some possible methods to control rumor propagation that (1)increasing noise intensity can effectively reduce rumor propagation when $\widehat{\mathcal{R}}_{0}>1$. That is, after rumors spread widely on social network platforms, government intervention and authoritative media coverage will interfere with netizens' opinions, thus reducing the degree of rumor propagation; (2) Speed up the rumor refutation, intensify efforts to refute rumors, and improve the scientific quality of netizen(i.e. increase the value of $\beta$ and decrease the value of $\alpha$ and $\gamma$ ) can effectively curb rumor propagation.


2022 ◽  
Author(s):  
Daniel Breton

Modeling the propagation of radiofrequency signals over irregular terrain is both challenging and critically important in numerous Army applications. One application of particular importance is the performance and radio connectivity of sensors deployed in scenarios where the terrain and the environment significantly impact signal propagation. This report investigates both the performance of and the algorithms and assumptions underlying the Delta-Bullington irregular terrain radiofrequency propagation model discussed in International Telecommunications Union Recommendation P.526-15. The aim is to determine its suitability for use within sensor-planning decision support tools. After reviewing free-space, spherical earth diffraction, and terrain obstacle diffraction losses, the report dis-cusses several important tests of the model, including reciprocity and geographic continuity of propagation loss over large areas of rugged terrain. Overall, the Delta-Bullington model performed well, providing reasonably rapid and geographically continuous propagation loss estimates with computational demands appropriate for operational use.


Author(s):  
Hongwei Su ◽  
Zi-Wei Zhang ◽  
Guoxing Wen ◽  
Guan Yan

Over the past few decades, the study of epidemic propagation has caught widespread attention from many areas. The field of graphs contains a wide body of research, yet only a few studies explore epidemic propagation’s dynamics in “signed” networks. Motivated by this problem, in this paper we propose a new epidemic propagation model for signed networks, denoted as S-SIS. To explain our analysis, we utilized the mean field theory to demonstrate the theoretical results. When we compare epidemic propagation through negative links to those only having positive links, we find that a higher proportion of infected nodes actually spreads at a relatively small infection rate. It is also found that when the infection rate is higher than a certain value, the overall spreading in a signed network begins showing signs of suppression. Finally, in order to verify our findings, we apply the S-SIS model on Erdös–Rényi random network and scale-free network, and the simulation results is well consist with the theoretical analysis.


2022 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Jianjun Liang ◽  
Xiao-Ming Li ◽  
Kaiguo Fan

The distribution and source sites of nonlinear internal waves (NLIWs) northeast of Hainan Island were investigated using satellite observations and a wavefront propagation model. Satellite observations show two types of NLIWs (here referred to as type-S and type-D waves). The type-S waves are spaced at a semidiurnal tidal period and the type-D waves are spaced at a diurnal tidal period. The spatial distribution of the two types of NLIWs displays a sandwich structure in which the middle region is influenced by both types of NLIWs, and the northern and southern regions are governed by the type-S and type-D waves, respectively. Solving the wavefront model yields good agreement between simulated and observed wavefronts from the Luzon Strait to Hainan Island. We conclude that the NLIWs originate from the Luzon Strait.


2022 ◽  
Vol 355 ◽  
pp. 01016
Author(s):  
Juan Ren ◽  
Qingjun Liu ◽  
Ting Chen ◽  
Pingye Deng

There are a lot of principles for sound transmission in the pipeline for whether sound transmission structure or noise reduction structure. Even in ultrasonic testing, there is a large number of principles for using pipeline sound transmission. Based on the sound propagation model and the boundary conditions of pipe wall sound absorption, the sound propagation equation for pipe wall sound absorption is given by establishing mathematical model and solving mathematical equation in this paper. When the distribution of sound field along the cross-section of the pipe (outlet) is ignored, the transmission efficiency of sound with different frequencies can be calculated or the sound absorption efficiency can be calculated. The analytical solution of the sound transmission equation in the pipeline has great theoretical significance and practical value for guiding the structural design of sound transmission and noise reduction, improving the calculation efficiency and verifying the numerical analysis results.


2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>


2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>


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