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In this paper, a new threshold INAR(1) model based on modified negative binomial operator with random coefficient is proposed. Basic probabilistic and statistical properties of this process are established. Then the conditional least squares (CLS) and the conditional maximum likelihood (CML) methods are applied to estimate the model parameters when the threshold value is known or not. The asymptotic properties of the CLS-estimator and the CML-estimator are also been discussed. A method to test the constancy of the autoregressive parameters is provided. As an illustration, a simulation study is conducted to illustrate the performances of these estimators and present an empirical analysis of monthly counts of break and enter non-dwelling in Bellingen. [ABSTRACT FROM AUTHOR]

The application of seawater and sea sand concrete (SWSSC) can reduce the construction period and cost of island infrastructure, but it may also bring the risk of alkali–silica reaction (ASR) owing to the presence of alkali ions in seawater and sea sand. To compare the characteristics of ASR between SWSSC and seawater and desalinated sea sand (DSS) concrete, the properties and the ASR products of mortar bars with different DSS content were studied. When the DSS proportion increased from 0% to 100%, the sodium (Na+), potassium (K+) and calcium (Ca2+) ion concentration of the specimens decreased by 22.6, 2.0 and 45.1 mg L−1, respectively, the pH decreased by 0.05 and the expansion of mortar bars was reduced by 0.16%. Desalination of sea sand could not eliminate the risk of ASR of SWSSC completely. The 14 days expansion of mortar bars with 100% DSS was 0.13%, and the precursors of ASR-P1 were observed by SEM. The experimental results of micriscopic tests all showed that with the increase of DSS proportion, the content of Na-shlykovite and ASR-P1 were reduced. A small amount of magnesium (Mg) in ASR products was detected. This study can provide a basis for the application of SWSSC in island infrastructure. [ABSTRACT FROM AUTHOR]

The random coefficient autoregressive (RCAR) processes are very useful to model time series in applications. It is commonly observed that the random autoregressive coefficient is assumed to be an independent identically distributed (i.i.d.) random variable sequence. To make the RCAR model more practical, this paper considers a new RCAR(1) model driven by explanatory variable and observations. We use the conditional least squares, the quantile regression and the conditional maximum likelihood methods to estimate the model parameters. The consistency and asymptotic normality of the proposed estimates are established. Simulation studies are conducted for the evaluation of the developed approaches and two applications to real-data examples are provided. The results show that the proposed procedures perform well for the simulations and application. [ABSTRACT FROM AUTHOR]

In this article, a new mixture integer-valued autoregressive process model with a finite random range Nt based on Pegram operator and binomial thinning operator is proposed. The new model can be well used to describe finite-range { 0, 1, ⋯ , L } integer-valued data with a broad variety of dispersion patterns, zero-inflation and multimodality. We derive the basic numerical characteristics of the model and testify stationarity and ergodicity properties. Conditional least squares method and maximum quasi-likelihood method are employed to estimate the parameters of NMBAR(1) model. The asymptotic properties of the estimators are acquired. Multiple groups of stochastic simulation studies are carried out to assess and compare the performances of these estimators and a real example is analyzed to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]

Taking into account the benefits of the dual synthetic jet (DSJ) actuator's simple form, the absence of an air supply, and powerful jet momentum, as well as the low water adhesion force of the superhydrophobic surfaces, it is possible to make droplet shedding happen easily by combining these two methods. This paper studied the motion of water droplets under the action of the DSJ actuator with inclined outlets on three different kinds of surfaces with different wettability, namely, aluminum (hydrophilic), fluorinated silicon (hydrophobic), and superhydrophobic surfaces. Particle image velocimetry measurements were used to describe the flow field of the DSJ actuator. Then, high-speed photography was adopted to compare the critical air velocity for stable motion of the droplet on the three kinds of surfaces. The droplet mobility at three different surfaces when the jet velocity was the same was compared. The displacement and changes of the contact line of water droplets with different volumes on the superhydrophobic surface under the influence of jets were studied. Besides, choosing a typical case, the effect of the dual synthetic jet on the droplet was quantitatively examined, as well as the aerodynamic drag and lift forces. And some of the phenomenon observed in the experiment was explained using the simulation data. It is hoped that this research would lead to the development of a new method of facilitating droplet transport in applications such as anti-icing, drug delivery, self-cleaning surfaces, etc. [ABSTRACT FROM AUTHOR]

This paper proposes a new time-varying integer-valued autoregressive (TV-INAR) model with a state vector following a logistic regression structure. Since the autoregressive coefficient in the model is time-dependent, the Kalman-smoothed method is applicable. Some statistical properties of the model are established. To estimate the parameters of the model, a two-step estimation method is proposed. In the first step, the Kalman-smoothed estimation method, which is suitable for handling time-dependent systems and nonstationary stochastic processes, is utilized to estimate the time-varying parameters. In the second step, conditional least squares is used to estimate the parameter in the error term. This proposed method allows estimating the parameters in the nonlinear model and deriving the analytical solutions. The performance of the estimation method is evaluated through simulation studies. The model is then validated using actual time series data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]