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A covariate-driven random coefficient generalized conditional heteroscedasticity (GARCH) time series model with the form of the buffered autoregression (BRC-GARCH-X) for modeling financial time series data is considered. As an extension of the classical two-regime threshold process, the buffered autoregression enjoys a more flexible regime-switching mechanism. Furthermore, the main feature of this model is that the threshold variable for regime-switching is formulated as a weighted average of important auxiliary variables. The estimator for regression parameters is obtained by the quasi-maximum exponential likelihood (QMEL) estimator and the corresponding asymptotic properties are established. Moreover, a mixed portmanteau test is developed for diagnostic checking. And a reasonable method for selecting search ranges for thresholds is also proposed and simulation studies are considered. As an application, we bring attention to some features of of stock returns of SP500 which shows that our model is feasible. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a new bivariate random coefficient integer-valued autoregressive process based on modified negative binomial operator with dependent innovations is proposed. Basic probabilistic and statistical properties of this model are derived. To estimate unknown parameters, Yule-Walker, conditional least squares and conditional maximum likelihood methods are considered and evaluated by Monte Carlo simulations. Asymptotic properties of the estimators are derived. Moreover, coherent forecasting and possible extension of the proposed model is provided. Finally, the proposed model is applied to the monthly crime datasets and compared with other models. [ABSTRACT FROM AUTHOR]

While overdispersion is a common phenomenon in univariate count time series data, its exploration within bivariate contexts remains limited. To fill this gap, we propose a bivariate integer-valued autoregressive model. The model leverages a modified binomial thinning operator with a dispersion parameter ρ and integrates random coefficients. This approach combines characteristics from both binomial and negative binomial thinning operators, thereby offering a flexible framework capable of generating counting series exhibiting equidispersion, overdispersion, or underdispersion. Notably, our model includes two distinct classes of first-order bivariate geometric integer-valued autoregressive models: one class employs binomial thinning (BVGINAR(1)), and the other adopts negative binomial thinning (BVNGINAR(1)). We establish the stationarity and ergodicity of the model and estimate its parameters using a combination of the Yule–Walker (YW) and conditional maximum likelihood (CML) methods. Furthermore, Monte Carlo simulation experiments are conducted to evaluate the finite sample performances of the proposed estimators across various parameter configurations, and the Anderson-Darling (AD) test is employed to assess the asymptotic normality of the estimators under large sample sizes. Ultimately, we highlight the practical applicability of the examined model by analyzing two real-world datasets on crime counts in New South Wales (NSW) and comparing its performance with other popular overdispersed BINAR(1) models. [ABSTRACT FROM AUTHOR]

Summary: Data related to the counting of elements of variable character are frequently encountered in time series studies. This paper brings forward a new class of k$$ k $$th‐order dependence‐driven random coefficient mixed thinning integer‐valued autoregressive time series model (DDRCMTINAR(k$$ k $$)) to deal with such data. Stationarity and ergodicity properties of the proposed model are derived in detail. The unknown parameters are estimated by conditional least squares, and modified quasi‐likelihood and asymptotic normality of the obtained parameter estimators is established. The performances of the adopted estimate methods are checked via simulations, which present that modified quasi‐likelihood estimators perform better than the conditional least squares considering the proportion of within‐Ω$$ \Omega $$ estimates in certain regions of the parameter space. The validity and practical utility of the model are investigated by epileptic seizure data and COVID‐19 data of suspected cases in China. [ABSTRACT FROM AUTHOR]

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]

In this paper, a time-varying first-order mixture integer-valued threshold autoregressive process driven by explanatory variables is introduced. The basic probabilistic and statistical properties of this model are studied in depth. We proceed to derive estimators using the conditional least squares (CLS) and conditional maximum likelihood (CML) methods, while also establishing the asymptotic properties of the CLS estimator. Furthermore, we employed the CLS and CML score functions to infer the threshold parameter. Additionally, three test statistics to detect the existence of the piecewise structure and explanatory variables were utilized. To support our findings, we conducted simulation studies and applied our model to two applications concerning the daily stock trading volumes of VOW. [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]

We investigate the complete convergence for weighted sums of sequences of negative dependence (ND) random variables and p-th moment convergence for weighted sums of sequences of ND random variables under sublinear expectation space. Using moment inequality and truncation methods, we prove the equivalent conditions of complete convergence for weighted sums of sequences of ND random variables and p-th moment convergence for weighted sums of sequences of ND random variables under sublinear expectation space. [ABSTRACT FROM AUTHOR]

We are interested in the linear processes generated by dependent sequences under sub-linear expectation. Using the Beveridge–Nelson decomposition of linear processes and the inequalities, the moderate deviation principle for linear processes produced by an m-dependent sequence is established. We also prove the upper bound of the moderate deviation principle for linear processes produced by negatively dependent sequences via different methods from m-dependent sequences. These conclusions promote and improve the corresponding results from the traditional probability space to the sub-linear expectation space. [ABSTRACT FROM AUTHOR]

In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005. [ABSTRACT FROM AUTHOR]

This paper studies the empirical likelihood method for a multivariate first-order random coefficient autoregressive (RCAR(1)) model. Based on the modified least square score equation, empirical likelihood method is used to test the randomness of coefficients and estimate the unknown parameters. First, the limiting distribution of the log empirical likelihood ratio statistic is established. Second, the maximum empirical likelihood estimator is derived and its asymptotic properties are established. Finally, the performances of the estimator are compared with the modified least square estimator via simulation. Furthermore, simulation result shows that the test has the correct size and very good power for all cases considered. [ABSTRACT FROM AUTHOR]

The shortages of river sand and freshwater in marine engineering can be addressed by utilizing seawater and sea sand concrete (SWSSC). However, the sulfate resistance of SWSSC at low temperature is not clear. In this study, the seawater and sea sand cement mortar (SSCM) and ordinary cement mortar (OCM) with different limestone powder (LP) content were corroded by different sulfate solutions at 5 °C. The change of compressive strength and compositions of corrosion products in mortars after different times of corrosion were studied. The results show that SSCM presents 9.4∼19 % lower strength loss and better sulfate resistance than OCM. The presence of Friedel's salt in SSCM contributes to a reduction in corrosion products like ettringite, gypsum, and thaumasite, thereby minimizing microstructural damage and mitigating the thaumasite sulfate attack (TSA). All mortars containing LP suffer from TSA and significantly damage with 17.4∼48.7 % strength loss after 360 days of MgSO 4 or Na 2 SO 4 corrosion. Increasing the LP content exacerbates the TSA damage in SSCM. This is primarily due to the additional soluble carbonate from LP promoting the formation of gypsum and thaumasite, thereby worsening the microstructure deterioration of SSCM. Meanwhile, the thaumasite is not found in SSCM without LP after corrosion. In contrast to Na 2 SO 4 corrosion, the thaumasite formation under MgSO4 corrosion mainly results from indirect reaction. The combined corrosion effects of Mg2+ and SO 4 2- lead to a more significant increase in expansive products (thaumasite and gypsum) and TSA damage. The findings of this study help to advance the sulfate resistance of SWSSC in marine environments. • The TSA characteristics of seawater sea sand cement concrete under sulfate attack at 5 °C were summarized. • The sulfate resistance of seawater sea sand cement mortar and ordinary cement mortar were compared. • The effect of LP content on the TSA characteristics of seawater sea sand cement mortar was evaluated. • The differences of sodium sulfate and magnesium sulfate during the TSA damage were discussed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new first-order mixture integer-valued threshold autoregressive process, based on the binomial and negative binomial thinning operators. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are derived and the asymptotic properties of the estimators are established. The inference for the threshold parameter is obtained based on the CLS and CML score functions. Moreover, the Wald test is applied to detect the existence of the piecewise structure. Simulation studies are considered, along with an application: the number of criminal mischief incidents in the Pittsburgh dataset • First-Order mixture integer-valued threshold autoregressive process based on binomial thinning and negative binomial thinning. • Parameter estimation for BiNB-MTINAR(1) process. • Detect the existence of the piecewise structure. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented. [ABSTRACT FROM AUTHOR]

Integer-valued time series data are greatly useful in many applications, such as reporting the daily number of patients impacted by an epidemic. When modelling high-order integer-valued time series data, order selection is a difficult task. In this paper, we propose a penalized empirical likelihood (PEL) method for order selection and parameter estimation in generalized pth-order integer-valued autoregressive (GINAR(p)) model. We show that the PEL method in the GINAR(p) model has oracle properties, which means that the PEL estimators identify the model as efficiently as if the true structure of the model was known ahead of time. Furthermore, we present the PEL ratio statistic to test a linear hypothesis of the parameter and demonstrate that it has an asymptotically χ 2 distribution under the null hypothesis. Numerical simulation and real data analysis are carried out to assess the performance our proposed method. [ABSTRACT FROM AUTHOR]

With the rapid improvement of social economy and the enhancement of people's health awareness, it is necessary to make an in-depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K-clustering and Levenberg-Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy. [ABSTRACT FROM AUTHOR]

As an industrial solid waste from limestone quarrying, limestone powder (LP) is often used in cement-based materials to reduce cost and carbon dioxide emissions. It is well known that the synergistic effect between LP and alumina-rich supplementary cementitious materials (SCMs) influences the properties of cement-based materials. To study the synergistic effect quantitatively, the hydration heat, hydration products and porosity of cement pastes with different binders were investigated. Test results showed that the synergistic effect between LP and fly ash (FA) or slag (S) improved the hydration degree and increased the total hydration heat of the pastes. When FA or S were incorporated along with LP, the calcium carbonate consumption at 3 days increased from 2.0 wt% to 4 wt%; at 180 days it increased from 2.4 wt% to 9.3 wt% (with FA) and 11.8 wt% (with S). The incorporation of FA or S also increased the diffraction peak of carboaluminate, indicating more carboaluminate was formed. The synergistic effect between LP and SCMs refined the pore structure and reduced the porosity of the cement pastes. The synergistic effect between LP and FA or S increased the density of the cement pastes by 1.04% or 1.41%, respectively, and correspondingly improved the compressive strength of the pastes. [ABSTRACT FROM AUTHOR]

In order to accurately and flexibly capture the correlation structure between two random coefficients in the binomial AR(1) process, we propose a new class of models with copula. We derive some basic properties of the process. Then we discuss the conditional least squares (CLS) and conditional maximum likelihood (CML) estimators, as well as their asymptotic properties. We also investigate the finite-sample performance of the proposed method in simulation studies. Finally, a real data example is provided to illustrate the model. • A flexible binomial AR(1) (FBAR(1)) model based on copulas. • Basic probabilistic and statistical properties. • The CLS and CML estimators and the related asymptotic properties. • Real data applications in the fields of meteorology and finance. [ABSTRACT FROM AUTHOR]

A class of integer-valued autoregressive models is considered, which are based on a latten process e.g., a Markov chain. Some statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. A Monte Carlo simulation study is conducted to examine the finite sample performance of the given estimation procedure. Finally, the performance of these models is illustrated by empirical applications to two sets of real counts. [ABSTRACT FROM AUTHOR]

The first-order Poisson autoregressive model may be suitable in situations where the time series data are non-negative integer valued. In this article, we propose a new parameter estimator based on empirical likelihood. Our results show that it can lead to efficient estimators by making effective use of auxiliary information. As a by-product, a test statistic is given, testing the randomness of the parameter. The simulation values show that the proposed test statistic works well. We have applied the suggested method to a real count series. [ABSTRACT FROM AUTHOR]

To clarify the role of limestone powder (LP) in cement mortars under a magnesium sulfate solution at room temperature, the hydration products, microstructure, and mechanical properties of cement-based materials with different amounts and particle sizes of LP were investigated by Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometer (EDS), appearance change, and compressive strength. The specimens were immersed in 5% MgSO4 solution at 20°C for up to 9 months. LP content had little effect on the content of ettringite, but it significantly influenced the content of gypsum and Ca(OH)2. When the LP content increased from 0% to 30% (average particle size: 32.42 μm), the ettringite content in mortars ranged from 4.1% to 5.3%, the gypsum content increased from 7.7% to 57.0%, and the Ca(OH)2 content decreased from 9.4% to 0%. The particle size of LP content had little effect on the content of ettringite and Ca(OH)2 when its content was 30%, but it influenced the gypsum content. When the average particle size of LP decreased from 32.42 to 8.07 μm, the ettringite content ranged from 4.6% to 5.1%, the Ca(OH)2 content was always 0%, and the gypsum content decreased from 57.0% to 51.8%. The compressive strength variation coefficients of mortars with different LP content ranged from 7.7% to 57.0%, and it ranged from 82.3% to 86.7% for mortars with different LP particle sizes. [ABSTRACT FROM AUTHOR]

[Display omitted] • The ultrathin Ni-phyllosilicate was successfully synthesized by ball milling method. • H 2 O 2 played a crucial role of pore-forming reagent to create defects in Ni-phyllosilicate. • Nickel acetylacetonate was the optimal nickel salt precursor to form Ni-phyllosilicate. • Metal utilization rate was significantly improved owing to the ultrathin defect-rich structure. • The optimum catalyst exhibited enhanced low-temperature activity for CO 2 methanation. To address the problems of low metal utilization rate and poor low-temperature activity of the nickel phyllosilicate catalysts for CO 2 methanation, this work prepared ultrathin defect-rich Ni-phyllosilicate through ball milling method in the presence of H 2 O 2 modifier, which simultaneously created active sites with more quantity and higher quality. The instantaneous temperature and high pressure derived from the collision and friction of grinding balls provided driving force for the Ni-phyllosilicate synthesis, and the shear force destroyed the interlayer forces to construct the ultrathin layers. H 2 O 2 molecule could insert the layers and dissociate to O 2 bubbles, whose cavitation effect created holes and defects in the nickel phyllosilicate nanosheets and SiO 2 spheres. H 2 O 2 amount was crucial for the defect construction and the optimum Ni-AA-1H catalyst possessed the ultrathin nanosheet of 0.68 nm and high Ni dispersion of 15.6 %, whose TOF CO2 reached 3.76 × 10−2 s−1 at 160 °C. The in-situ DRIFTS results confirmed the enhanced low-temperature activity and the HCOO− reaction pathway over Ni-AA-1H for CO 2 methanation. Nickel precursor effect was also investigated, and nickel acetylacetonate was determined as the optimal one compared with nickel acetate and nickel chloride, owing to the high steric effect of CH 3 COCHCOCH 3 − group and strong basicity environment during ball milling process. [ABSTRACT FROM AUTHOR]

In this paper, the novel estimating methods and their properties for pth-order dependence-driven random coefficient integer-valued autoregressive time series model (DDRCINAR(p)) are studied as the innovation sequence has a Poisson distribution and the thinning is binomial. Strict stationarity and ergodicity for DDRCINAR(p) model are proved. Conditional maximum likelihood and conditional least squares are used to estimate the model parameters. Asymptotic normality of the proposed estimators are derived. Finite sample properties of the conditional maximum likelihood estimator are examined in relation to the widely used conditional least squares estimator. It is concluded that, if the Poisson assumption can be justified, conditional maximum likelihood method performs better in terms of bias and MSE. Finally, three real data sets are analyzed to demonstrate the practical relevance of the model. [ABSTRACT FROM AUTHOR]

We consider the nonparametric inference for the double‐threshold generalized autoregressive conditional heteroscedastic models. The quasi‐maximum exponential likelihood estimators (QMELEs) of the model parameters are obtained, and their asymptotic properties are established. Simulation studies imply that the estimators are asymptotically normally distributed. An empirical investigation of stock returns illustrates our findings. Both the simulations and the example indicate that the QMELE is feasible, reliable and appropriate to fit the financial time series data of the Hang Seng Index. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP)–reinforced seawater–sea sand concrete (SWSSC) is a new structure form for coastal infrastructure, especially for island construction. The degradation mechanism of FRP bars in SWSSC involves water molecules, hydroxide ions, chloride ions, high temperature, and stress. In general, water molecules and hydroxide and chloride ions react with some ingredients in FRP bars, destroy the interface between fiber and resin, and reduce the properties of FRP bars. In the environment of water molecules, hydroxide ions, and chloride ions, the strength retentions of FRP bars were 71%–77%, 26%–98%, and 49%–77%, respectively, depending on composition and manufacturing techniques of fiber. High temperature and stress accelerate the degradation of FRP bars. Under different temperatures and stress levels, the strength retentions of FRP bars were 0.6%–98% and 43%–93%, respectively, depending on the fiber type, temperature, and stress levels. The presence of chloride ions accelerates the hydration of cement and improves the properties of SWSSC at early age. However, the conclusions on the properties of SWSSC at later age are still controversial. Based on previous studies, some future needs on FRP-reinforced SWSSC are also recommended. [ABSTRACT FROM AUTHOR]

High‐resolution fluid dispensing techniques play a critical role in modern digital microfluidics, micro‐biosensing, and advanced fabrication. Though most of existing dispensers can achieve precise and high‐throughput fluid dispensing, they suffer from some inherent problems, such as specially fabricated dispensing micronozzles/microtips, large operating systems, low volume tunability, and poor performance for low surface tension liquids and liquids containing solid/liquid additives. Herein, the authors propose a facile, low‐frequency micro dispensing technique based on the Rayleigh–Plateau instability of singular liquid jets, which are stimulated by the air cavity collapse arising in the impact of microliter drops on non‐wetting surfaces. This novel dispensing strategy is capable to produce single microdrops of low‐viscosity liquids with a tunable volume from picoliters to nanoliters, and the operational surface tension range covers most laboratory solvents. The dispensing function is implemented without using small‐dimension nozzles/tips and enables handling diverse complex liquids. Moreover, the rather simple operating platform allows the integration of the whole dispensing function into a handy portable device with a low cost. Employing this microdispensing technique, the authors have controlled microchemical reactions, handled liquid samples in biological analysis, and fabricated smart materials and devices. The authors envision that this rational microdrop generator would find applications in various research areas. [ABSTRACT FROM AUTHOR]

High‐resolution fluid dispensing techniques play a critical role in modern digital microfluidics, micro‐biosensing, and advanced fabrication. Though most of existing dispensers can achieve precise and high‐throughput fluid dispensing, they suffer from some inherent problems, such as specially fabricated dispensing micronozzles/microtips, large operating systems, low volume tunability, and poor performance for low surface tension liquids and liquids containing solid/liquid additives. Herein, the authors propose a facile, low‐frequency micro dispensing technique based on the Rayleigh–Plateau instability of singular liquid jets, which are stimulated by the air cavity collapse arising in the impact of microliter drops on non‐wetting surfaces. This novel dispensing strategy is capable to produce single microdrops of low‐viscosity liquids with a tunable volume from picoliters to nanoliters, and the operational surface tension range covers most laboratory solvents. The dispensing function is implemented without using small‐dimension nozzles/tips and enables handling diverse complex liquids. Moreover, the rather simple operating platform allows the integration of the whole dispensing function into a handy portable device with a low cost. Employing this microdispensing technique, the authors have controlled microchemical reactions, handled liquid samples in biological analysis, and fabricated smart materials and devices. The authors envision that this rational microdrop generator would find applications in various research areas. [ABSTRACT FROM AUTHOR]

This paper proposes a penalized maximum quasi-likelihood (PMQL) estimation that can solve the problem of order selection and parameter estimation regarding the pth-order integer-valued time series models. The PMQL estimation can effectively delete the insignificant orders in model. By contrast, the significant orders can be retained and their corresponding parameters are estimated, simultaneously. Moreover, the PMQL estimation possesses certain robustness hence its order shrinkage effectiveness is superior to the traditional penalized estimation method even if the data is contaminated. The theoretical properties of the PMQL estimator, including the consistency and oracle properties, are also investigated. Numerical simulation results show that our method is effective in a variety of situations. The Westgren's data set is also analyzed to illustrate the practicability of the PMQL method. [ABSTRACT FROM AUTHOR]

The integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model is often utilized to describe data in biostatistics, such as the number of people infected with dengue fever, daily epileptic seizure counts of an epileptic patient and the number of cases of campylobacterosis infections, etc. Since the structure of such data is generally high-order and sparse, studies about order shrinkage and selection for the model attract many attentions. In this paper, we propose a penalized conditional maximum likelihood (PCML) method to solve this problem. The PCML method can effectively select significant orders and estimate the parameters, simultaneously. Some simulations and a real data analysis are carried out to illustrate the usefulness of our method. [ABSTRACT FROM AUTHOR]

Zero inflation, zero deflation, overdispersion, and underdispersion are commonly encountered in count time series. To better describe these characteristics of counts, this article introduces a zero‐modified geometric first‐order integer‐valued autoregressive (INAR(1)) model based on the generalized negative binomial thinning operator, which contains dependent zero‐inflated geometric counting series. The new model contains the NGINAR(1) model, ZMGINAR(1) model, and GNBINAR(1) model with geometric marginals as special cases. Some statistical properties are studied, and estimates of the model parameters are derived by the Yule–Walker, conditional least squares, and maximum likelihood methods. Asymptotic properties and numerical results of the estimators are also studied. In addition, some test and forecasting problems are addressed. Three real‐data examples are given to show the flexibility and practicability of the new model. [ABSTRACT FROM AUTHOR]

To better describe the characteristics of time series of counts such as overdispersion or structural change, in this paper, we redefines the integer-valued threshold autoregressive models based on negative binomial thinning (NBTINAR(1)) under a weaker condition that the expectation of the innovations is finite. Parameters' point estimation and interval estimation problems are considered. A method to test the nonlinearity of the data is provided. As an illustration, we conduct a simulation study and empirical analysis of Pittsburgh crime data sets. [ABSTRACT FROM AUTHOR]

In this article, we generalize the individual risk model by introducing a dependence relationship between premiums and claim sizes. The dependence structure is described by bivariate distribution and copulas. Some statistical properties of the models are obtained. The loss probability, value at risk (VaR) and tail value at risk (TVaR) are applied to quantify the risk of the models. To this end, we use net loss (claim sizes minus premiums) instead of claim sizes to calculate the risk measures. Some numerical examples are given to illustrate the results. The normal approximation method is used to simplify the risk measures for the models. [ABSTRACT FROM AUTHOR]

As the development of neuromorphic computing, it is meaningful to explore the synaptic memristors based on organic materials owing to their advantages in low cost, good mechanical flexibility and strong biocompatibility. In this work, organic pentacene thin films have been deposited on different substrates by the thermal evaporation method. The prepared thin films on SiO 2 /Si substrates demonstrate the co-existence of digital and analog resistive switching (RS) behaviors as well as a series of synaptic plasticity. To characterize the flexibility of the organic thin films, lateral memristors have been fabricated on polyethylene terephthalate (PET) substrates and demonstrate comparable RS behaviors when the bending radius is decreased to 1.26 cm or after 1000 bending. Furthermore, vertical memristors with Pt/pentacene/indium tin oxide (ITO) structure have been constructed when the organic thin films are deposited on ITO/PET substrates, exhibiting bipolar RS behaviors with low SET voltage of 0.65 V. The vertical synaptic memristors can be used to simulate the "learning-forgetting" experience of human brain. More importantly, an artificial neural network (ANN) can be simulated based on the vertical synaptic memristors, demonstrating 93.8 % recognition accuracy for hand-written digit image. This study suggests potential applications of the organic pentacene thin films for neuromorphic computing. [Display omitted] • The pentacene thin films show the co-existence of digital and analog memristive behaviors. • The thin films on flexible substrates demonstrate good mechanical flexibility. • The vertical synaptic memristors can successfully simulate the "learning-forgetting" experience. • An artificial neural network based on the vertical memristors can be emulated with high image recognition accuracy. [ABSTRACT FROM AUTHOR]

Superhydrophobic surfaces hold great prospects for extremely diverse applications owing to their water repellence property. The essential feature of superhydrophobicity is micro-/nano-scopic roughness to reserve a large portion of air under a liquid drop. However, the vulnerability of the delicate surface textures significantly impedes the practical applications of superhydrophobic surfaces. Robust superhydrophobicity is a must to meet the rigorous industrial requirements and standards for commercial products. In recent years, major advancements have been made in elucidating the mechanisms of wetting transitions, design strategies and fabrication techniques of superhydrophobicity. This review will first introduce the mechanisms of wetting transitions, including the thermodynamic stability of the Cassie state and its breakdown conditions. Then we highlight the development, current status and future prospects of robust superhydrophobicity, including characterization, design strategies and fabrication techniques. In particular, design strategies, which are classified into passive resistance and active regeneration for the first time, are proposed and discussed extensively. [ABSTRACT FROM AUTHOR]

In this paper, we study the use of the mean empirical likelihood (MEL) method in a first-order random coefficient integer-valued autoregressive model. The MEL ratio statistic is established, its limiting properties are discussed, and the confidence regions for the parameter of interest are derived. Furthermore, a simulation study is presented to demonstrate the performance of the proposed method. Finally, a real data analysis of dengue fever is performed. [ABSTRACT FROM AUTHOR]

This article considers a bivariate INAR(1) process based on an extension of the negative binomial thinning operator by prespecifying the distribution of the innovations. The dependence is introduced through the innovation components. The existence, uniqueness, strict stationarity, ergodicity, and some probabilistic properties of the process are derived. The estimation methods of conditional least squares and conditional maximum likelihood are considered. Some numerical results of the estimates are presented by simulation study. An application to crime data set is provided. [ABSTRACT FROM AUTHOR]

In this article, we introduce an extended binomial AR(1) model based on the generalized binomial thinning operator. This operator relaxes the independence assumption of the binomial thinning operator and contains dependent Bernoulli counting series. The new model contains the binomial AR(1) model as a particular case. Some probabilistic and statistical properties are explored. Estimators of the model parameters are derived by conditional maximum likelihood (CML), conditional least squares (CLS) and weighted conditional least squares (WCLS) methods. Some asymptotic properties and numerical results of the estimators are studied. The good performance of the new model is illustrated, among other competitive models in the literature, by an application to the monthly drunken driving counts. [ABSTRACT FROM AUTHOR]

In this paper, a multinomial autoregressive model for finite-range time series of counts is introduced to analyze the finite-range integer-valued data with more than two states. Basic probabilistic and statistical properties of the model are discussed. The related estimations of the parameters in proposed model are considered using conditional least squares (CLS), weighted conditional least squares (WCLS) and conditional maximum likelihood (CML) methods. The asymptotic properties of the estimators are established. Some simulation studies are conducted to verify the proposed procedure. A real example is analyzed to illustrate the advantages of our model. • A multinomial autoregressive model for finite-range time series of counts. • Basic probabilistic and statistical properties. • The CLS, WCLS and CML estimations and the related asymptotic properties. • A real data example is investigated to assess the performance. [ABSTRACT FROM AUTHOR]

To better describe the data whose variance is greater than mean in time series analysis, this paper introduces the RCINAR(1) process with generalized negative binomial marginals. The related estimations of this process are considered using Yule-Walker, modified conditional least squares, conditional maximum likelihood and Bayesian methods. The asymptotic properties of the estimators are established. Some simulations are conducted to verify the proposed estimation methods and a real example is proposed to illustrate the advantages of our model. [ABSTRACT FROM AUTHOR]

We study a covariate-adjusted regression(CAR) model that is proposed for such situations where both predictors and response in a regression model are not directly observable but are distorted by a multiplicative factor that is determined by an unknown function of some observable covariate. By establishing a connection to varying-coefficient models, we present the local linear L 1 -estimation method when the underlying error distribution deviates from a normal distribution. The robust estimators of parameters are proposed in the underlying regression model. The consistency and asymptotic normality of the robust estimators are investigated. Since the limit distribution depends on the unknown components of the errors, an empirical likelihood ratio method based on L 1 estimator is proposed. The confidence intervals for the regression coefficients are constructed. Simulation results demonstrate the superiority of the proposed estimators over other classical estimators when the underlying errors have heavy tails. Pima Indian diabetes data set is conducted to illustrate the performance of the proposed method, where the response and predictors are potentially contaminated by body mass index. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new bivariate first-order random coefficient integer-valued autoregressive (BRCINAR(1)) process with dependent innovations. Some basic probabilistic and statistical properties of this model are obtained. Estimators of unknown parameters are derived by using Yule–Walker, conditional least squares and conditional maximum likelihood methods. The asymptotic properties of the estimators are established. The performance of these estimators is compared through a simulation experiment. Moreover, the coherent forecasting for BRCINAR(1) model is addressed. Finally, an application to a real data example is investigated to assess the performance of the model. • A new bivariate random coefficient INAR(1) (BRCINAR(1)) process is proposed. • Some basic probabilistic and statistical properties of this process are obtained. • Three different estimation methods are provided to estimate parameters. • The coherent forecasting for BRCINAR(1) process is addressed. • Coefficients follow BETA distribution and Gaussian mixture distribution are studied. • An application to a real data example is investigated. [ABSTRACT FROM AUTHOR]

The process of serially dependent counts with deflation or inflation of zeros is commonly observed in many applications. This paper investigates the monitoring of such a process, the first-order zero-modified geometric integer-valued autoregressive process (ZMGINAR(1)). In particular, two control charts, the upper-sided and lower-sided CUSUM charts, are developed to detect the shifts in the mean process of the ZMGINAR(1). Both the average run length performance and the standard deviation of the run length performance of these two charts are investigated by using Markov chain approaches. Also, an extensive simulation is conducted to assess the effectiveness or performance of the charts, and the presented methods are applied to two sets of real data arising from a study on the drug use. [ABSTRACT FROM AUTHOR]

In order to investigate the effects of different spindle speeds on the surface integrity of TC4 specimens and the effect of ultrasonic-assisted rolling strengthening on the enhancement of microstructure and mechanical properties of TC4 specimens, this paper uses TC4 titanium alloy specimens as the test object for ultrasonic-assisted rolling strengthening test. The results show that increasing the spindle speed within a certain range can improve the surface morphology of the specimen, reduce the surface roughness and average profile height, and improve the surface hardness and residual stress. The research also finds that USRP can refine the surface grain of TC4 and introduce the grain refinement layer and work hardening layer with the same thickness on the surface of the specimen, thus improving the hardness, elastic modulus, and corrosion resistance of the specimen surface. [ABSTRACT FROM AUTHOR]

Highlights • The physical and chemical effects of limestone powder (LP) were calculated. • The chemical effect of LP was influenced by its particle size and amount of LP at 3d. • The particle size and amount of LP had little effect on its chemical effect at 180d. Abstract The study aims to quantitatively calculate the physical and chemical effects of limestone powder (LP) with four average particle sizes and two amounts in cement pastes by using thermal gravimetric analysis (TGA), X-ray diffraction (XRD), mercury intrusion porosimetry (MIP) techniques. At 3d, as the average particle size of LP decreased from 32 to 6 μm, the consumed CaCO 3 content in LP increased from 0.67% to 1.71%, and the chemically reactive portion of LP increased from 1.15% to 2.95%. However, at 180d, the consumed CaCO 3 content and chemically reactive portion of LP was almost 2.4% and 4.2%, respectively. At 3d, as the LP content increased from 25% to 50%, the consumed CaCO 3 content in LP increased from 1.71% to 2.02%, and the chemically reactive portion of LP increased from 2.95% to 3.5%. However, at 180d, the differences of consumed CaCO 3 content and chemically reactive portion of LP between cement pastes with different LP content were limited. The physical and chemical effects of limestone powder on the compressive strength of cement pastes were calculated. At 180d, as the average particle size of LP decreased from 32 to 6 μm, the contribution of chemical effect of LP on the compressive strength of cement pastes increased from 1.01% to 1.08% while the contribution of physical effect decreased from 21.19% to 20.03%. [ABSTRACT FROM AUTHOR]

Drop manipulation on hydrophobic surfaces is of importance in lab-on-a-chip applications. Recently, superhydrophobic surface-assisted lab-on-a-chips have attracted significant attention from researchers due to their advantages of contamination resistance and low adhesion between the drop and the surface during manipulation. However, control over both static and dynamic interactions between a drop and a superhydrophobic surface has been rarely achieved. In this study, we designed an electric-field-dependent liquid-dielectrophoresis force to manipulate a drop on a superhydrophobic surface. This type of control has been found to be fast in response, bio-friendly, convenient, repeatable, and energy efficient. Moreover, the adhesion force and rebounding for both the static and the dynamic interactions between the drop and the surface under an electric field have been explored. It was found that the adhesion force could be reversibly tuned three-fold without breaking the Cassie–Baxter state. Rebounding experiments showed a close to linear relation between energy dissipation and the applied voltage. This relation was used to tune the on-demand behaviors of a drop on a surface in a proof-of-concept experiment for drop sorting. This electric-field-dependent drop manipulation may have potential applications in digital microfluidics, micro-reactors and advanced lab-on-a-drop platforms. [ABSTRACT FROM AUTHOR]

Highlights • The effects of limestone powder (LS) depends on its particle size and content. • Using LS to replace fine aggregate improves the properties of concrete. • Using LS to replace cement reduces the properties of the concrete. Abstract Limestone powder (LS) is one of the mineral materials in concrete due to its wide availability and low cost. This paper discusses effects of LS on the workability, setting time, shrinkage, mechanical properties and durability of concrete. Such effects are mainly influenced by many factors, including the particle size and content of LS, the polymorph of calcium carbonate and synergetic effect between LS and alumina-rich supplementary cementitious materials (SCMs). In general, incorporating LS, as cement paste replacement or in the form of fine aggregate, improves the properties of concrete. Whereas, when LS is incorporated into concrete to replace cement, effects of LS on the properties of concrete depend on its particle size and content. In the end, some further research needs are also suggested. [ABSTRACT FROM AUTHOR]