Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Simulation of crowd evacuation under attack considering emotion spreading

In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.

An Overview on Opinion Spreading Model

Research on opinion spreading has received more and more attention in recent years. This paper focus on make a summary of opinion evolution researches, we first review some classical opinion models, and then introduce the existing result of improvement models from the aspect of opinion space, model parameter, social network and so on. The current study’s limitation and further research are also prospected at the end. By in-depth understand the opinion spreading mechanism so as to guide and control the public opinions, which is very useful and meaningful.

Demage Spreading in the Ising Model with a special Metropolis Dynamics Approach

The time evolution of the Hamming distance (damage spreading) for the and Ising models on the square lattice is performed with a special metropolis dynamics algorithm. Two distinct regimes are observed according to the temperature range for both models: a low-temperature one where the distance in the long-time limit is finite and seems not to depend on the initial distance and the system size; a high-temperature one where the distance vanishes in the long-time limit. Using the finite size scaling method, the dynamical phase transition (damage spreading transition) temperature is obtained as for the Ising model.

Physics-constrained deep-inverse point spread function model:toward non-line-of-sight imaging reconstruction

Non-line-of-sight(NLOS)imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections.This imaging method has garnered significant attention in diverse domains,including remote sensing,rescue operations,and intelligent driving,due to its wide-ranging potential applications.Nevertheless,accurately modeling the incident light direction,which carries energy and is captured by the detector amidst random diffuse reflection directions,poses a considerable challenge.This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging,which are crucial for achieving high-quality reconstructions.In this study,we propose a point spread function(PSF)model for the NLOS imaging system utilizing ray tracing with random angles.Furthermore,we introduce a reconstruction method,termed the physics-constrained inverse network(PCIN),which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network.The PCIN approach initializes the parameters randomly,guided by the constraints of the forward PSF model,thereby obviating the need for extensive training data sets,as required by traditional deep-learning methods.Through alternating iteration and gradient descent algorithms,we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters.The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups.Moreover,the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

Steady-State Analysis of SECIR Rumor Spreading Model in Complex Networks

In this paper, the SECIR rumor spreading model is formulated and analyzed, in which the social education level and the counterattack mechanism are taken into consideration. The results show that improving education level and increasing the ratio of counter are effective in reducing the risk of rumor propagation and enhancing the resistance to rumor propagation.

Spreading speed of a food-limited population model with delay

This paper is concerned with the spreading speed of a food-limited population model with delay.First,the existence of the solution of Cauchy problem is proved.Then,the spreading speed of solutions with compactly supported initial data is investigated by using the general Harnack inequality.Finally,we present some numerical simulations and investigate the dynamical behavior of the solution.

Temperature-Dependent Newtonian Rheology in Advection-Convection Geodynamical Model for Plate Spreading in Eastern Volcanic Zone, Iceland

Geodynamic process as advection-convection of the Mid-Atlantic Ocean Ridge (MAR), that is exposed on land in Iceland is investigated. Advection is considered for the plate spreading velocity. Geodetic GPS data during 2000-2010 is used to estimate plate spreading velocity along a profile in the Eastern Volcanic Zone (EVZ), Iceland striking N102。E, approximately parallel to the NUVEL-1A spreading direction between the Eurasian and North American plates. To predict subsurface mass flow patterns, temperature-dependent Newtonian rheology is considered in the finite-element models (FEM). All models are considered 2-D with steady-state, incompressible rheology whose viscosity depends on the subsurface temperature distribution. The thickness of lithosphere along the profile in the EVZ is identified by 700。C isotherm and 1022 Pa s iso-viscosity, those reach 50 ± 3 km depth at distance of 100 km from rift axis. Geodetic observation and model prediction results show the ~90% of spreading is accommodated within ~45 km of the rift axis in each direction. Model predicts ~8.5 mm.yr-1 subsidence at the surface of rift center when magmatic plumbing is inactive. The rift center (the highest magmatic influx is ~11 mm.yr-1) in model shifts ~10 - 20 km west to generate observed style surface deformation. The spreading velocity, isotherm and depth of isotherm are the driving forces resulting in the surface deformation. These three parameters have more or less equal weight. However, as the center of deformation in the EVZ shifts and most of the subsidence takes place in the volcanic system that is currently the active which is the located of plate axis.

Utilization of Oil Properties to Develop a Spreading Rate Regression Model for Nigerian Crude Oil

The target of this study is to develop a spreading rate regression model capable of predicting rate of spread of Nigerian crude oil spills on water. The major factors responsible for spreading rate of crude oil on water were considered, namely surface tension, viscosity, and specific gravity/American Petroleum Institute degree (0API), all at specified temperature values. The surface tension, viscosity and density parameters were interactively measured under controlled factorial analysis. The spreading rate of each crude oil was determined by artificially spilling them on laboratory calm/stagnant water in a rectangular tank and their averages were also computed. These averages were used to develop a regression model equation for spreading rate. The model developed indicated that an average spreading rate was 3.3528 cm/s at 37.5°C and the predictive regression model is evaluated with the interactions of specific gravity, viscosity and surface tension. It is convenient to state that the model will predict the spread rate of crude oils which possess imputed physicochemical properties having pour point averaged 15.5°C on calm seawater.

Numerical analyses of pile performance in laterally spreading frozen ground crust overlying liquefiable soils

Lateral spread of frozen ground crust over liquefied soil has caused extensive bridge foundation damage in the past winter earthquakes.A shake table experiment was conducted to investigate the performance of a model pile in this scenario and revealed unique pile failure mechanisms.The modelling results provided valuable data for validating numerical models.This paper presents analyses and results of this experiment using two numerical modeling approaches： solid-fluid coupled finite element（FE） modeling and the beam-on-nonlinear-Winkler-foundation（BNWF） method.A FE model was constructed based on the experiment configuration and subjected to earthquake loading.Soil and pile response results were presented and compared with experimental results to validate this model.The BNWF method was used to predict the pile response and failure mechanism.A p-y curve was presented for modelling the frozen ground crust with the free-field displacement from the experiment as loading.Pile responses were presented and compared with those of the experiment and FE model.It was concluded that the coupled FE model was effective in predicting formation of three plastic hinges at ground surface,ground crust-liquefiable soil interface and within the medium dense sand layer,while the BNWF method was only able to predict the latter two.

Virus spreading in wireless sensor networks with a medium access control mechanism

In this paper, an extended version of standard susceptible-infected （SI） model is proposed to consider the influence of a medium access control mechanism on virus spreading in wireless sensor networks. Theoretical analysis shows that the medium access control mechanism obviously reduces the density of infected nodes in the networks, which has been ignored in previous studies. It is also found that by increasing the network node density or node communication radius greatly increases the number of infected nodes. The theoretical results are confirmed by numerical simulations.

Dynamic properties of epidemic spreading on finite size complex networks

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS （susceptibleinfected-susceptible） model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Simulation modelling of potato virus Y spread in relation to initial inoculum and vector activity

Potato virus Y(PVY)is a non-persistent virus that is transmitted by many aphid species and causes significant damage to potato production.We constructed a spatially-explicit model simulating PVY spread in a potato field and used it to investigate possible effects of transmission efficiency,initial inoculum levels,vector behavior,vector abundance,and timing of peak vector activity on PVY incidence at the end of a simulated growing season.Lower PVY incidence in planted seed resulted in lower virus infection at the end of the season.However,when populations of efficient PVY vectors were high,significant PVY spread occurred even when initial virus inoculum was low.Non-colonizing aphids were more important for PVY spread compared to colonizing aphids,particularly at high densities.An early-season peak in the numbers of noncolonizing aphids resulted in the highest number of infected plants in the end of the season,while mid-and late-season peaks caused relatively little virus spread.Our results highlight the importance of integrating different techniques to prevent the number of PVY-infected plants from exceeding economically acceptable levels instead of trying to control aphids within potato fields.Such management plans should be implemented very early in a growing season.

Modelling of the spread of a potential invasive pest,the Siberian moth (Dendrolimus sibiricus) in Europe

Background： The Siberian moth （Dendrolimus sibiricus） （SM） defoliates several tree species from the genera Larix, Piceo and Abies in northern Asia, east of the Urals. The SM is a potential invasive forest pest in Europe because Europe has several suitable host species and climatic conditions of central and northern Europe are favourable for the SM. Methods： This study developed a grid-based spatio-temporal model for simulating the spread of the SM in case it enters Europe from Russia via border stations. The spread rate was modeled as a function of the spatial distribution of host species, climatic suitability of different locations for the SM, human population density, transportation of moth-carrying material, and flying of moths from tree to tree. Results and conclusions： The simulations showed that the SM is most likely to spread in the forests of northeast Belarus, the Baltic countries, and southern and central Finland. Climatic conditions affected the occurrence of the SM more than human population density and the coverage of suitable host species.

Epidemic spreading on networks with vaccination

In this paper, a new susceptible-infected-susceptible （SIS） model on complex networks with imperfect vaccination is proposed. Two types of epidemic spreading patterns （the recovered individuals have or have not immunity） on scale-free networks are discussed. Both theoretical and numerical analyses are presented. The epidemic thresholds related to the vaccination rate, the vaccination-invalid rate and the vaccination success rate on scale-free networks are demonstrated, showing different results from the reported observations. This reveals that whether or not the epidemic can spread over a network under vaccination control is determined not only by the network structure but also by the medicine＇s effective duration. Moreover, for a given infective rate, the proportion of individuals to vaccinate can be calculated theoretically for the case that the recovered nodes have immunity. Finally, simulated results are presented to show how to control the disease prevalence.

The Role of Fluids in Promoting Seismic Activity in Active Spreading Centers of the Salton Trough, California, USA

We interpret seismic activity in the active spreading centers of the Salton Trough to indicate 1) a magmatic intrusion in the lower crust beneath the active Brawly, Cerro Prieto, Imperial, Elsinore, and San Jacinto fault systems;and 2) fluids in the upper crust that have been released from that magmatic body. The absence of a magmatic body and fluids at the location of fossil spreading centers along the Sand Hill and Algodones faults ndicated by little or no seismic activity in those areas. We show several lines of evidence to point out that both melt and fluids related to the seismic activity. In particular, receiver function analysis, Vp/Vs ratios, and tomographic data reveal low velocity zones coincide with the location of the active spreading centers. High Vp/Vs ratios and low velocity zones in the lower crust and upper mantle attributed to melt inclusion, while low Vp/Vs ratios in the upper crust are attributed water inclusions. Frequency-mag- nitude distributions characterized by high b-values in southern California;high b-values have also been associated with crustal fluids. A crustal scale model developed from the receiver functions, gravity, and magnetic data supports the existence of a magmatic intrusion within about 20 km of the surface southwest of the Salton Sea, that intrusion extends for 70 km in a SW-NE direction.

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide.The pandemic has brought much uncertainty to the global economy and the situation in general.Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics,which have negative impact on public health.The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions.To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts,data on the spread of the COVID-19 virus in Lithuania is used,the forecasts of epidemic dynamics were examined,and the results were presented in the study.Nevertheless,the approach presented might be applied to any country and other pandemic situations.The COVID-19 outbreak started at different times in different countries,hence some countries have a longer history of the disease with more historical data than others.The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.

Simulation of Spread of Infectious Diseases and Population Mobility in a Deterministic Epidemic Patch Model

Computer simulation models are widely applied in various areas of the health care sector, including the spread of infectious diseases. Patch models involve explicit movements of people between distinct locations. The aim of the present work has been designed and explored a patch model with population mobility between different patches and between each patch and an external population. The authors considered a SIR （susceptible-infected-recovered） scheme. The model was explored by computer simulations. The results show how endemic levels are reached in all patches of the system. Furthermore, the performed explorations suggest that the people mobility between patches, the immigration from outside the system and the infection rate in each patch, are factors that may influence the dynamics of epidemics and should be considered in health policy planning.

Modelling the spread of sexually transmitted diseases on scale-free networks

In this paper a new model for the spread of sexually transmitted diseases （STDs） is presented. The dynamic behaviors of the model on a heterogenons scale-free （SF） network are considered, where the absence of a threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Three immunization strategies, uniform immunization, proportional immunization and targeted immunization, are applied in this model. Analytical and simulated results are given to show that the proportional immunization strategy in the model is effective on SF networks.

Epidemic spreading on random surfer networks with infected avoidance strategy

In this paper, we study epidemic spreading on random surfer networks with infected avoidance （IA） strategy. In particular, we consider that susceptible individuals＇ moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy＇s effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive.