Hopf bifurcation of nonlinear system with multisource stochastic factors

The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.

Stochastic period-doubling bifurcation analysis of stochastic Bonhoeffer-van der Pol system

In this paper, the Chebyshev polynomial approximation is applied to the problem of stochastic period-doubling bifurcation of a stochastic Bonhoeffer-van der Pol （BVP for short） system with a bounded random parameter. In the analysis, the stochastic BVP system is transformed by the Chebyshev polynomial approximation into an equivalent deterministic system, whose response can be readily obtained by conventional numerical methods. In this way we have explored plenty of stochastic period-doubling bifurcation phenomena of the stochastic BVP system. The numerical simulations show that the behaviour of the stochastic period-doubling bifurcation in the stochastic BVP system is by and large similar to that in the deterministic mean-parameter BVP system, but there are still some featured differences between them. For example, in the stochastic dynamic system the period-doubling bifurcation point diffuses into a critical interval and the location of the critical interval shifts with the variation of intensity of the random parameter. The obtained results show that Chebyshev polynomial approximation is an effective approach to dynamical problems in some typical nonlinear systems with a bounded random parameter of an arch-like probability density function.

Households’Willingness-to-Pay for Wastewater Treatment in Traditional Agro-Food Processing Villages,Nhue-Day River Basin,Vietnam:Case Study in Hanoi City

Despite the number of studies focusing on the financial analysis of production activities, conducting on technical solutions, and improving water quality, no study has been conducted on the application of economic instruments that apply to water quality management in craft villages, and several studies of WTP also. This study aimed to estimate the households’ willingness-to-pay for wastewater treatment in selected traditional agro-food processing villages in Nhue-Day River Basin, Vietnam. A pilot Choice Experiment (CE) technique in Choice Modelling (CM) approach was applied for this study with 267 selected agro-food processing households by using the conditional logit (CL) and random parameter logit (RPL) models. The results showed that total annual environmental fee for wastewater treatment from agro-food processing households is estimated as 1089 million VND (equal to US$47,868 per year) for the total of 902 agro-food processing households in three research sites in Nhue-Day River Basin. This estimated budget for wastewater treatment accounted for 55.85% of total annual operation and maintenance costs only. In addition, the technology is improved to enable 90% of treated wastewater. Overall, the results of this study suggest the new wastewater treatment plant construction and improved wastewater collection system by increasing the investment in order to improve the water quality in Nhue-Day River Basin that brings about the reducing environmental degradation, biodiversity loss and human health risks.

Stochastic period-doubling bifurcation analysis of a Rssler system with a bounded random parameter

This paper aims to study the stochastic period-doubling bifurcation of the three-dimensional Rossler system with an arch-like bounded random parameter. First, we transform the stochastic RSssler system into its equivalent deterministic one in the sense of minimal residual error by the Chebyshev polynomial approximation method. Then, we explore the dynamical behaviour of the stochastic RSssler system through its equivalent deterministic system by numerical simulations. The numerical results show that some stochastic period-doubling bifurcation, akin to the conventional one in the deterministic case, may also appear in the stochastic Rossler system. In addition, we also examine the influence of the random parameter intensity on bifurcation phenomena in the stochastic Rossler system.

Stochastic period-doubling bifurcation in biharmonic driven Duffing system with random parameter

Stochastic period-doubling bifurcation is explored in a forced Duffing system with a bounded random parameter as an additional weak harmonic perturbation added to the system. Firstly, the biharmonic driven Duffing system with a random parameter is reduced to its equivalent deterministic one, and then the responses of the stochastic system can be obtained by available effective numerical methods. Finally, numerical simulations show that the phase of the additional weak harmonic perturbation has great influence on the stochastic period-doubling bifurcation in the biharmonic driven Duffing system. It is emphasized that, different from the deterministic biharmonic driven Duffing system, the intensity of random parameter in the Duffing system can also be taken as a bifurcation parameter, which can lead to the stochastic period-doubling bifurcations.

QUANTITATIVE EVALUATION OF ARRANGEMENT OF MONOMERS IN LINEAR BINARY COPOLYMERS USING A MONTE CARLO SIMULATION METHOD

A Monte Carlo method was used to take thorough account of the influences of different reactivity ratios and initial feed compositions on copolymer microstructure.The model proves the lack of azeotropic behavior in systems in which r_A>1 and r_B<1 or vice versa;it is also able to calculate the drift in the copolymer properties:copolymer composition,and randomness parameter.Moreover,for each reactivity ratio pair given,there is a unique reaction conversion,at which macromolecules produced inherit their ...

Investigating safety and liability of autonomous vehicles:Bayesian random parameter ordered probit model analysis

Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Design/methodology/approach–The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations.The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs,respectively,as well as accommodating the heterogeneity issue simultaneously.Findings–The findings show that day,location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.Originality/value–The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Effect of Randomness of Interfacial Properties on Fracture Behavior of Concrete Under Uniaxial Tension

Interfacial transition zones （ITZs） between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the zero-thickness cohesive elements with different normal distribution parameters were used to model the ITZs with random mechanical properties. A number of uniaxial tension-induced fracture simulations were carried out, and the effects of the random parameters on the fracture behavior of concrete were statistically analyzed. The results show that, different from the dissipated fracture energy, the peak load of concrete does not always obey a normal distribution, when the elastic stiffness, tensile strength, or fracture energy of ITZs is normally distributed. The tensile strength of the ITZs has a significant effect on the fracture behavior of concrete, and its large standard deviation leads to obvious diversity of the fracture path in both location and shape.

Injury severities from heavy vehicle accidents:An exploratory empirical analysis

Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

With the increased frequency of extreme weather events and large-scale disasters, extensive societal and economic losses incur every year due to damage of infrastructure and private properties, business disruptions,fatalities, homelessness, and severe health-related issues. In this article, we analyze the economic and disaster data from1970 through 2010 to investigate the impact of disasters on country/region-level economic growth. We leveraged a random parameter modeling approach to develop the growth-econometrics model that identifies risk factors significantly influencing the country/region-level economic growth in the face of natural hazard-induced disasters,while controlling for country/region-and time-specific unobserved heterogeneities. We found that disaster intensity in terms of fatalities and homelessness, and economic characteristics such as openness to trade and a government's consumption share of purchasing power parity(PPP), are the significant risk factors that randomly vary for different countries/regions. Other significant factors found to be significant include population, real gross domestic product(GDP), and investment share of PPP converted GDP per capita. We also found that flood is the most devastating disaster to affect country/region-level economic growth. This growth-econometrics model will help in the policy and decision making of governmentsrelated to the investment needs for pre-and post-disaster risk mitigation and response planning strategies, to better protect nations and minimize disaster-induced economic impacts.

Exploring traffic safety climate with driving condition and driving behaviour:a random parameter structural equation model approach

This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.