For-adverbials, Frequentative Aspect, and Pluractionality

With the continuous development of social economy and urbanization in our country, noise pollution has become the fourth largest environment pollution in our country but water pollution, air pollution, solid waste pollution, which is the widest influence of traffic noise. In recent years, with the development of automobile industry and urban transportation, road traffic noise pollution in Harbin phenomenon is increasingly prominent, which seriously affected the People's daily life. Through detailed survey we found that the research on road traffic noise pollution situation in Harbin is very less, and part of the evaluation is not accurate. In this paper, firstly the characteristics of road traffic noise and noise evaluation index selection is discussed in detail. The xuefu road sections in Harbin city traffic noise and traffic noise pollution monitoring research in detail strict accordance with national standards. According to monitoring data of xuefu road traffic noise pollution source in time and space distribution analysis, the roughly the time distribution and spatial distribution of xuefu road traffic flow. By the overall analyzed monitoring data characteristic, the xuefu road traffic noise pollution is serious. According the previous analysis and research, the researchers find out the main factors influencing the xuefu road traffic condition, and we puts forward some countermeasures for the noise reduction. Through the study of this paper, that is to Harbin city xuefu road for the future of the road traffic noise monitoring and management work provide favorable basis, and it has good practical application value. It aimed at Harbin city road traffic noise control, urban planning and construction, provide theoretical basis for government decisions.

On distributions of barrier crossing times as observed in single-molecule studies of biomolecules

Seasonal ecology in ice-covered Arctic seas - Considerations for spill response decision making

The objective of this study is to examine the differences of visual attention patterns between young and elder drivers affected by engagement of audio non-driving related task (NDRT) and traffic conditions. Previous research focus on influence of the interaction of NDRT in Highly Automated Vehicle (HAV) during take over tasks both in driving performance and visual attention, yet the research on aging effect on visual attention pattern with NDRT during take over tasks is still limited. Twenty-seven young and twenty-seven elder participants drove in a highly automated driving simulator in different traffic conditions and NDRT levels, and after the take-over request (TOR) they were required to change lanes to pass a stationary truck. The traffic conditions are set by numbers of cars on the right lane and NDRT is classic n-back task with audio stimuli. Results showed that elder drivers have higher collision rate and less stable lateral control than young drivers. For fixation transition number between different regions, both age group have fewer transitions when engaging NDRT, elder group have significantly fewer transitions than younger group and are more influenced by high traffic density. The analysis for fixation durations show similar results. For the change of distribution of fixation time, elder group focus more on the road ahead than young group which is more obvious in high-traffic condition. Around 4 s after the TOR, a decrease of attention on mirrors is observed in elder group. These results show a probable difference in visual attention patterns for young and elder drivers when facing TOR with engagement of NDRT and in high traffic density. Possible relevance between less attention on mirrors and worse driving behavior in elder group is also discussed. Further improvements of this study is illustrated according to the previous results and analysis.

Recurrent groups of pollen types in time

On the scale of tens to hundreds of meters the release of heat from sea ice leads is an important component of the Arctic heat budget. On regional scales, however, their effects are difficult to assess without information on their distribution in time and space. Medium-resolution visible and thermal sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or Operational Line Scanner (OLS) provide perhaps the best combination of spatial coverage and spectral information for studies of leads. However, both the atmosphere and the geometrical aspects of the sensors play a significant role in the retrieval of lead characteristics. The present authors apply results from previous analyses of the effects of sensor spatial resolution and atmospheric attenuation on the retrieval of lead characteristics from satellite visible and thermal data (Key et al., 1994; Stone and Key, 1993). The attenuation of upwelling thermal radiation by ice crystal precipitation, aerosols, and clouds has been modeled and is discussed in the context of lead detectability. Statistical and empirical models of the effects of sensor resolution (i.e., pixel size) are presented in the context of lead width estimation. Data collected during IBADEX (Beaufort Sea, March-April 1992) are used for validation. >

In case-control studies, determination of alcohol consumption by cases immediately prior to the injury event is often conceptually straightforward. However, determination of consumption status by controls is difficult because they lack a reference point, especially when cases and controls are not individually matched. We describe a method of assigning alcohol consumption status to controls using a 24-hour drinking history, the distribution in time of case events, and the random assignment of a specific time period to each control subject. This methodology offers a practical approach for determining alcohol consumption status among control subjects immediately prior to a case event, when controls lack a reference point and have not been individually matched to cases. Published in 2001 by John Wiley & Sons, Ltd.

In various applications one encounters samples of objects, where each object consists of a small number of repeated event times observed over a fixed time interval. For such rare event data there are no flexible methods available that can be applied when the shapes of the intensity functions that generate the observed event times are not known, or vary substantially between objects. We model the underlying intensity functions as nonparametric object-specific random functions. Applying a novel functional method to obtain the covariance structure of the associated random densities, we reconstruct object-specific density functions that reflect the distribution of event times. We demonstrate in simulations that the proposed functional approach is superior to conventional nonparametric methods, as it borrows strength from the entire sample of objects rather than aiming at the estimation of each object's density separately. Our method is based on a key relationship that allows one to reduce the covariance estimation problem for random densities to the simpler problem of estimating a non-random joint density from pooled event times. We describe an application to model bid arrivals for a sample of online auctions, and also include asymptotic justifications of the methodology.

Semiparametric methods were proposed by Wei et al. (1989) to analyse recurring event-time data. They modelled the marginal distribution of each event time with a Cox proportional hazards model without imposing any constraint on the joint distribution of different event times. Therefore, it is unclear whether or not event times can simultaneously satisfy their respective marginal proportional hazards assumptions, while having continuous joint distribution. Often this leads to a difficulty of conducting simulation studies. In this note we construct parametric marginal proportional hazards models for recurring event times with proper joint density functions.

Multivariate current status data, consist of indicators of whether each of several events occur by the time of a single examination. Our interest focuses on inferences about the joint distribution of the event times. Conventional methods for analysis of multiple event-time data cannot be used because all of the event times are censored and censoring may be informative. Within a given subject, we account for correlated event times through a subject-specific latent variable, conditional upon which the various events are assumed to occur independently. We also assume that each event contributes independently to the hazard of censoring. Nonparametric step functions are used to characterize the baseline distributions of the different event times and of the examination times. Covariate and subject-specific effects are incorporated through generalized linear models. A Markov chain Monte Carlo algorithm is described for estimation of the posterior distributions of the unknowns. The methods are illustrated through application to multiple tumor site data from an animal carcinogenicity study.

It is often the case that some information is available on the parameter of failure time distributions from previous experiments or analyses of failure time data. The Bayesian approach provides the methodology for incorporation of previous information with the current data. In this paper, given a progressively type II censored sample from a Rayleigh distribution, Bayesian estimators and credible intervals are obtained for the parameter and reliability function. We also derive the Bayes predictive estimator and highest posterior density prediction interval for future observations. Two numerical examples are presented for illustration and some simulation study and comparisons are performed. Copyright © 2006 John Wiley & Sons, Ltd.

Mega-analysis, or the meta-analysis of individual data, enables pooling and comparing multiple studies to enhance estimation and power. A challenge in mega-analysis is estimating the distribution for clustered, potentially censored event times where the dependency structure can introduce bias if ignored. We propose a new proportional odds model with unknown, time-varying coefficients, and random effects. The model directly captures event dependencies, handles censoring using pseudo-values, and permits a simple estimation by transforming the model into an easily estimable additive logistic mixed effect model. Our method consistently estimates the distribution for clustered event times even under covariate-dependent censoring. Applied to three observational studies of Huntington's disease, our method provides, for the first time in the literature, evidence of similar conclusions about motor and cognitive impairments in all studies despite different recruitment criteria.

Rain event characteristics are assessed in a 10-year (1991–2000) record for 122 stations in the Czech Republic. Individual rain events are identified using the minimum interevent time (mit) concept. For each station, the optimal mit value is estimated by examining the distribution of interevent times. In addition, various mit values are considered to account for the effect of mit on rain event characteristics and their interrelationships. The interdependence between rain event characteristics and altitude, average rainfall depth, and geographic location are explored using simple linear models. Most rain event characteristics can be to some extent explained by average total rainfall or altitude, although models including the former significantly outperformed models using the latter. Significant correlation was found among several pairs of monthly mean characteristics often including event rain rate (with event duration, depth, maximum intensity, and fraction of intraevent rainless periods). Moreover, strong correlation was revealed between number of events, interevent time, event depth, and duration. In general, correlation decreases in absolute value with mit. Strong spatial correlation was found for the mean monthly interevent time and number of events. Spatial correlation was considerably smaller for other characteristics. In general, spatial dependence was smaller for larger mit values. Copyright © 2013 John Wiley & Sons, Ltd.

We consider a fractional counting process with jumps of amplitude 1,2,...,k, withk∈N, whoseprobabilitiessatisfy a suitablesystemoffractionaldifference-differential equations. We obtain the moment generating function and the probability law of the result- ing process in terms of generalized Mittag-Leffler functions. We also discuss two equiv- alent representations both in terms of a compound fractional Poisson process and of a subordinator governed by a suitable fractional Cauchy problem. The first occurrence time of a jump of fixed amplitude is proved to have the same distribution as the waiting time of the first event of a classical fractional Poisson process, this extending a well-known property of the Poisson process. When k = 2 we also express the distribution of the first passage time of the fractional counting process in an integral form. Finally, we show that the ratios given by the powers of the fractional Poisson process and of the countingprocess over their means tend to 1 in probability.

Evidence of [formula omitted]-exponential statistics in Greek seismicity