A particle-resolved heat-particle-fluid coupling model by DEM-IMB-LBM

Multifield coupling is frequently encountered and also an active area of research in geotechnical engineering.In this work,a particle-resolved direct numerical simulation(PR-DNS)technique is extended to simulate particle-fluid interaction problems involving heat transfer at the grain level.In this extended technique,an immersed moving boundary(IMB)scheme is used to couple the discrete element method(DEM)and lattice Boltzmann method(LBM),while a recently proposed Dirichlet-type thermal boundary condition is also adapted to account for heat transfer between fluid phase and solid particles.The resulting DEM-IBM-LBM model is robust to simulate moving curved boundaries with constant temperature in thermal flows.To facilitate the understanding and implementation of this coupled model for non-isothermal problems,a complete list is given for the conversion of relevant physical variables to lattice units.Then,benchmark tests,including a single-particle sedimentation and a two-particle drafting-kissing-tumbling(DKT)simulation with heat transfer,are carried out to validate the accuracy of our coupled technique.To further investigate the role of heat transfer in particle-laden flows,two multiple-particle problems with heat transfer are performed.Numerical examples demonstrate that the proposed coupling model is a promising high-resolution approach for simulating the heat-particle-fluid coupling at the grain level.

Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

A NOVEL STOCHASTIC HEPATITIS B VIRUS EPIDEMIC MODEL WITH SECOND-ORDER MULTIPLICATIVE α-STABLE NOISE AND REAL DATA

This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and stochas-ticity.Based on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of vaccination.This deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant fluctuations.The long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable jumps.By developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is provided.The theoretical results of the current study are validated by numerical simulations and parameters estimation is also performed.Moreover,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling.

A Stochastic Model to Assess the Epidemiological Impact of Vaccine Booster Doses on COVID-19 and Viral Hepatitis B Co-Dynamics with Real Data

A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.

An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

A Hybrid SIR-Fuzzy Model for Epidemic Dynamics:A Numerical Study

This study focuses on the urgent requirement for improved accuracy in diseasemodeling by introducing a newcomputational framework called the Hybrid SIR-Fuzzy Model.By integrating the traditional Susceptible-Infectious-Recovered(SIR)modelwith fuzzy logic,ourmethod effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters.The main aim of this research is to provide a model for disease transmission using fuzzy theory,which can successfully address uncertainty in mathematical modeling.Our main emphasis is on the imprecise transmission rate parameter,utilizing a three-part description of its membership level.This enhances the representation of disease processes with greater complexity and tackles the difficulties related to quantifying uncertainty in mathematical models.We investigate equilibrium points for three separate scenarios and perform a comprehensive sensitivity analysis,providing insight into the complex correlation betweenmodel parameters and epidemic results.In order to facilitate a quantitative analysis of the fuzzy model,we propose the implementation of a resilient numerical scheme.The convergence study of the scheme demonstrates its trustworthiness,providing a conditionally positive solution,which represents a significant improvement compared to current forward Euler schemes.The numerical findings demonstrate themodel’s effectiveness in accurately representing the dynamics of disease transmission.Significantly,when the mortality coefficient rises,both the susceptible and infected populations decrease,highlighting the model’s sensitivity to important epidemiological factors.Moreover,there is a direct relationship between higher Holling type rate values and a decrease in the number of individuals who are infected,as well as an increase in the number of susceptible individuals.This correlation offers a significant understanding of how many elements affect the consequences of an epidemic.Our objective is to enhance decision-making in public health by providing a thorough quantitative analysis of the Hybrid SIR-Fuzzy Model.Our approach not only tackles the existing constraints in disease modeling,but also paves the way for additional investigation,providing a vital instrument for researchers and policymakers alike.

Numerical Analysis of Bacterial Meningitis Stochastic Delayed Epidemic Model through Computational Methods

Based on theWorld Health Organization(WHO),Meningitis is a severe infection of the meninges,the membranes covering the brain and spinal cord.It is a devastating disease and remains a significant public health challenge.This study investigates a bacterial meningitis model through deterministic and stochastic versions.Four-compartment population dynamics explain the concept,particularly the susceptible population,carrier,infected,and recovered.The model predicts the nonnegative equilibrium points and reproduction number,i.e.,the Meningitis-Free Equilibrium(MFE),and Meningitis-Existing Equilibrium(MEE).For the stochastic version of the existing deterministicmodel,the twomethodologies studied are transition probabilities and non-parametric perturbations.Also,positivity,boundedness,extinction,and disease persistence are studiedrigorouslywiththe helpofwell-known theorems.Standard and nonstandard techniques such as EulerMaruyama,stochastic Euler,stochastic Runge Kutta,and stochastic nonstandard finite difference in the sense of delay have been presented for computational analysis of the stochastic model.Unfortunately,standard methods fail to restore the biological properties of the model,so the stochastic nonstandard finite difference approximation is offered as an efficient,low-cost,and independent of time step size.In addition,the convergence,local,and global stability around the equilibria of the nonstandard computational method is studied by assuming the perturbation effect is zero.The simulations and comparison of the methods are presented to support the theoretical results and for the best visualization of results.

Cartographic Study and Modeling of the Bakwanga Kimberlite Massive 5 at Kasai Oriental in the Democratic Republic of Congo

Bakwanga kimberlite massive 5 in Kasai Oriental is part of a set of 13 kimberlite massives numbered according to the order in which they were discovered. They are located on an alignment with a more or less W-E direction making up the Northern group known as Bakwanga. The importance of the Bakwanga kimberlite massives on the country’s economy in the production of diamonds sufficiently proves the interest of geological research work in this area. The objective of this work is to determine a mathematical model of the shape of the massive as close as possible to reality and through cartography. The cartographic study and modeling of this kimberlite massive were carried out using data from core samples taken on longitudinal and transverse profiles of the 50 × 50 meter mesh drilling plan intersecting this kimberlite massive. We intend to deduce the structure and lithostratigraphy of the kim-berlitic facies and the direct environment of massive 5. As a result, we note that the majority of surveys on the extent of this massive have intersected: Red clayey sand - Polymorphic sandstone - Nodular sandstone, with kaolin blocks and nodules - Epiclastic Kimberlite - Xenokimberlite - Massive Kimberlite - Mesozoic sandstone - Dolomite (enclosing). The shape of the Massive 5 model is vaguely elliptical with a W-E longitudinal axis of 575 m and N-S axis of 275 meters. Surveys have shown that Massive 5 is in fact composed of two pipes, located in the W (western pipe) and E (eastern pipe) ends of the massif. The two chimneys of the two pipes have walls ranging from subvertical at the eastern pipe to very steep walls of around 70˚ to 80˚ for the western pipe and the average diameter of the two pipes is ±50 meters. At level 600, the massive has an area of ±10.5 hectares and it gradually decreases in depth and the modeling of the latter shows a concentric decrease in the volume of the massive from the surface to depth in the shape of a mushroom. 3 eruptive phases established this Kimber-litic massive, the first two phases (old) of which formed the crater of the western pipe and the third formed the crater of the eastern pipe in the dolomites. These dolomites constitute everywhere the surrounding area of the massive;the distinction of these 3 phases is made possible thanks to Epiclastic deposits, Xenokim-berlites and massive Kimberlites.

Stochastic Bifurcation of an SIS Epidemic Model with Treatment and Immigration

In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochastic system by computing the Lyapunov exponent of the linearized system. Further, the global stability of the stochastic model is analyzed based on the singular boundary theory. Moreover, we prove that the model undergoes a Hopf bifurcation and a pitchfork bifurcation. Finally, several numerical examples are provided to illustrate the theoretical results. .

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Stability of a Delayed Stochastic Epidemic COVID-19 Model with Vaccination and with Differential Susceptibility

In this paper, we treat the spread of COVID-19 using a delayed stochastic SVIRS (Susceptible, Infected, Recovered, Susceptible) epidemic model with a general incidence rate and differential susceptibility. We start with a deterministic model, then add random perturbations on the contact rate using white noise to obtain a stochastic model. We first show that the delayed stochastic differential equation that describes the model has a unique global positive solution for any positive initial value. Under the condition R0 ≤ 1, we prove the almost sure asymptotic stability of the disease-free equilibrium of the model.

Exploring International Media and Information Literacy Initiatives: Insights From DW Akademie’s MIL Model

Media and Information Literacy(MIL)is one of the most important topics in today’s mediatized world.Under the leadership of United Nations Educational,Scientific and Cultural Organization(UNESCO),many international organizations in the world,as foreign donors,annually announce many projects and grants for the promotion and development of the field of MIL in the countries of the world.One of the main actors of this movement is DW Akademie with different media and MIL projects several countries of the world.This research paper delves into the role of DW Akademie’s MIL model in shaping a media-savvy generation.The study explores the theoretical underpinnings and practical applications of Deutsche Welle(DW)Akademie’s MIL model,analysing its effectiveness in fostering media literacy skills.The research employs a multi-faceted approach,incorporating case studies to assess the model’s impact across diverse demographics.The paper also considers the model’s alignment with global educational policies and proposes recommendations for its integration into broader frameworks.By investigating DW Akademie’s MIL model,this research contributes to the ongoing discourse on media literacy education,providing valuable insights for educators,policymakers,and researchers.The findings offer a nuanced understanding of the model’s position in cultivating a media-savvy generation poised to navigate the complexities of the information age.

基于CFD-DEM的流-固耦合数值建模方法研究进展

随着土体渗流侵蚀研究的逐渐深入,对土颗粒流失和变形破坏机理的研究方法呈现出多尺度的特点。其中,计算流体力学-离散元耦合方法(CFD-DEM)为在细观尺度上研究流-固耦合相互作用对土体宏观力学特性的影响提供了一种行之有效的方法。针对CFD-DEM耦合方法在岩土工程领域应用现状,本文系统总结现有流-固耦合计算方法的优缺点,重点论述CFD-DEM耦合方法的建模策略,包括固相颗粒形状建模与粒间接触模型、流体相控制方程及参数计算方法,以及CFD-DEM耦合计算,并就相关问题进行深入探讨,最后提出了CFD-DEM耦合方法未来的发展方向。

基于倾斜三维模型与机载LiDAR点云的DEM快速更新

随着倾斜摄影技术的发展和实景三维建设的不断推进,地形级实景三维地理场景对DEM现势性要求日益提高。为了解决传统方法更新DEM成本高、工作量大的问题,本文提出了利用倾斜三维模型成果转换点云,融合两期DSM和DOM进行变化检测,替换已有机载LiDAR点云,快速更新DEM的方法,并通过项目验证了该方法的可行性,极大地节约了生产成本,为DEM更新提供了思路。

基于DEM的雷达阵地选址预处理方法研究

防空预警雷达阵地选址的优劣直接影响雷达本身及防空武器作战效能的发挥.针对基于数字高程模型(DEM)的常规实时计算方法存在耗时、耗力的问题,在明确阵地选址预处理内容的基础上,提出了预处理的具体流程和参数计算方法.通过数据预处理,以空间换时间的方式解决了阵地自动选址计算量大、耗时长的问题.实例仿真结果表明,该雷达阵地选址预处理方法大大缩短了自动选址时间,有效提高了选址效率.

多源参考数据下的边坡DEM提取与精度分析

参考DEM是InSAR DEM提取精度分析中不可或缺的重要数据。考虑到当前可供使用的DEM数据层出不穷,如何选择可靠的参考DEM以解决不同地形DEM提取精度的需求,是DEM提取中一个亟待解决的问题。本文分别选择12.5 m的ALOS DEM、30 m的STRM DEM、90 m的STRM DEM作为参考DEM,依据2020年两景Sentinel-1A影像对德兴市部分地区边坡进行DEM数据提取,利用InSAR技术提取实验区的S1ADEM数据,并以ASTGTM DEM数据为参考,使用检测点法随机选择约300个检测点,选择相应的评价指标,分别对不同参考DEM下提取得到的S1ADEM进行精度检验。研究结果显示,使用12.5 m分辨率的ALOS DEM作为外部DEM,提取得到的S1ADEM精度更高,其在实验区A的RMSE为9.412 m,使用90 m的STRM DEM,提取得到的S1ADEM精度更低,其在实验区A的RMSE为10.293m。该实验为不同地形提取DEM提供了指导,能够帮助学者们在进行InSAR实验时更好地选择参考DEM,研究成果可为边坡灾害及预警提供重要的技术支持。

矿区沉陷DEM多重滤波方法研究

针对传统地表移动监测方法周期较长、工作量大的问题,通过无人机LiDAR和点云滤波获取地面点云,并构建沉陷DEM,实现地表沉陷监测的方法具有快速、高效的优势;由于现有点云滤波和插值算法构建的沉陷DEM模型仍会包含噪声,限制了该技术在矿区的普及,因此,进一步研究了沉陷DEM噪声的去除方法,对比分析了多重滤波与经典滤波方法。实验分析结果表明:在几种去噪方法中,中值滤波组合维纳滤波的去噪效果最好,保留了下沉盆地的细节特征,能够满足矿区地表形变监测的基本要求。