Abstract:
A multiple-pollutant version of CMAQ v4.6 (i.e., CMAQ-MP) has been applied by the US EPA over continental US in 2002 to demonstrate the model’s capability in reproducing the long-term trends of ambient criteria and hazardous air pollutants (CAPs and HAPs, respectively) in support of regulatory analysis for air quality management. In this study, a comprehensive model performance evaluation for the full year of 2002 is performed for the first time for CMAQ-MP using the surface networks and satellite measurements. CMAQ-MP shows a comparable and improved performance for most CAPs species as compared to an older version of CMAQ that did not treat HAPs and used older versions of national emission inventories. CMAQ-MP generally gives better performance for CAPs than for HAPs. Max 8-h ozone (O_{3}) mixing ratios are well reproduced in the O_{3}season. The seasonal-mean performance is fairly good for fine particulate matter (PM_{2.5}), sulfate (SO_{4}^{2-}), and mercury (Hg) wet deposition and worse for other CAPs and HAPs species. The reasons for the model biases may be attributed to uncertainties in emissions for some species (e.g., ammonia (NH_{3}), elemental carbon (EC), primary organic aerosol (POA), HAPs), gas/aerosol chemistry treatments (e.g., secondary or- ganic aerosol formation, meteorology (e.g., overestimate in summer precipitation), measurements (e.g., NO_{3}^{-}), and the use of a coarse grid resolution. CMAQ cannot well reproduce spatial and seasonal variations of column variables except for nitrogen dioxide (NO_{2}) and the ratio of column mass of HCHO/NO_{2}. Possible reasons include inaccurate seasonal allocation or underestimation of emissions, inaccurate BCONs at higher altitudes, lack of model treatments such as mineral dust or plume-in-grid process, and limitations and errors in satellite data retrievals. The process analysis results show that in addition to transport, gas chemistry or aerosol/emissions play the most important roles for O_{3} or PM_{2.5}, respectively. For most HAPs, emissions are important sources and cloud processes are a major sink. Simulated P_{ H2} H_{ O2}/P_{ HNO3 } and HCHO/NO_{2} indicate VOC-limited chemistry in major urban areas throughout the year and in other non-urban areas in winter, but NO_{x}-limited chemistry in most areas in summer.

Abstract:
By using the China Health and Nutrition Survey data,
this paper targets the generation who enter the labor market at the age of 18
to 35 and tries to study the long-term impact of their family health on income
mobility as well as keep track of the tendency. Three conclusions can be drown
from the research. Firstly, the impact is more significant in the second poor
group and the middle-income group than that in the high-income group, and that
of the low-income group is the worst. Secondly, in the long run, the impact
shows a weakening trend, but the changing process is not the same. Thirdly, the
significance of impact varies in different age groups. Compared with the groups
aged 20 to 35 and 25 to 30, the impact is relatively remarkable for those who
just enter the labor market aged 18 to 25.

Abstract:
The behaviors of one-dimensional quantum random walks are strikingly different from those of classical ones. However, when decoherence is involved, the limiting distributions take on many classical features over time. In this paper, we study the decoherence on both position and ``coin'' spaces of the particle. We propose a new analytical approach to investigate these phenomena and obtain the generating functions which encode all the features of these walks. Specifically, from these generating functions, we find exact analytic expressions of several moments for the time and noise dependence of position. Moreover, the limiting position distributions of decoherent quantum random walks are shown to be Gaussian in an analytical manner. These results explicitly describe the relationship between the system and the level of decoherence.

Abstract:
It is important to detect a low-dimensional linear dependency in high-dimensional data. We provide a perspective on this problem, called the rank-extreme (ReX) association, through studies of the maximum norm of a vector of $p$ standard Gaussian variables that has a covariance matrix of rank $d \le p$. We find a simple asymptotic upper bound of such extreme values as $\sqrt{d(1-p^{-2/(d-1)})}$. This upper bound is shown to be sharp when the entries of the correlation matrix are generated by inner products of i.i.d. uniformly distributed unit vectors. This upper bound also takes on an interesting trichotomy phenomenon depending on the limit of $d/\log{p}$. Based on this ReX approach, we propose several methods for high-dimensional inference. These applications include a test of the overall significance in regressions, a refinement of valid post-selection inference when the size of selected models is restricted, a classification of deficient ranks based on the magnitude of the extreme, and an inference method for low-ranks. One advantage of this approach is that the asymptotics are in the dimensions $d$ and $p$ but not in the sample size $n$. Thus, the inference can be made even when $n < d \le p$, which allows fast detection of low-dimensional structure. Furthermore, the higher the dimension is, the more accurate the inference is. Therefore, these results can be regarded as a "blessing of dimensionality."

Abstract:
We study the spherical cap packing problem with a probabilistic approach. Such probabilistic considerations result in an asymptotic sharp universal uniform bound on the maximal inner product between any set of unit vectors and a stochastically independent uniformly distributed unit vector. When the set of unit vectors are themselves independently uniformly distributed, we further develop the extreme value distribution limit of the maximal inner product, which characterizes its uncertainty around the bound. As applications of the above asymptotic results, we derive (1) an asymptotic sharp universal uniform bound on the maximal spurious correlation, as well as its uniform convergence in distribution when the explanatory variables are independently Gaussian distributed; and (2) an asymptotic sharp universal bound on the maximum norm of a low-rank elliptically distributed vector, as well as related limiting distributions. With these results, we develop a fast detection method for a low-rank structure in high-dimensional Gaussian data without using the spectrum information.

Abstract:
The intrinsic kinetics of hydrorefining catalyst in ex-situ presulfurization was investigated using a fixed-bed penetrating method. A mathematical model was built to express the intrinsic kinetics of presulfurization using an unreacted shrinking core model for catalyst grains and one-dimension unhomogeneous model for beds, and then the significance of the new model was tested. Results show that the presulfurization with hydrorefining catalyst was a nonstationary process, as the reaction rate changed with time, and this first-order reaction displayed high activation energy. In this dynamic mathematical model, a correction coefficient f0 was introduced into the common power-function-formed rate equation, which indicated the effects of solid diffusion on reaction. The model with high significance was able to improve the presulfurization rate and the raw material utilization ratio, thus providing theoretical guidance for achieving high presulfurization effects.

This article did a research about exhaust
gas constituent inside the catalytic combustion furnace with Pd-based honeycomb
monoliths of lean natural gas-air mixtures and discussed the feature of the
exhaust gas. In addition, the near-zero pollutant emissions of catalytic
combustion burner was proved by a test report provided by NIM. From a
low-carbon prospective, the application prospect of catalytic combustion
furnace was discussed

Abstract:
Due to the unobserved nature of the true return
variation process, one of the most challenging problems in evaluation of
volatility forecasts is to find an accurate benchmark proxy for ex-post volatility. This paper uses the Australian equity market ultra-high-frequency
data to construct an unbiased ex-post volatility estimator and then use
it as a benchmark to evaluate various practical volatility forecasting
strategies (GARCH class model based). These forecasting strategies allow for
the skewed distribution of innovations and use various estimation windows in
addition to the standard GARCH volatility models. In out-of-sample tests, we
find that forecasting errors across all model specifications are systematically
reduced if using the unbiased ex-post volatility estimator compared with
those using the realized volatility based on sparsely sampled intra-day data.
In particular, we show that the three benchmark forecasting models outperform
most of the modified strategies with different distribution of returns and
estimation windows. Comparing the three standard GARCH class models, we find
that the asymmetric power ARCH (APARCH) model exhibits the best forecasting
power in both normal and financial turmoil periods, which indicates the ability
of APARCH model to capture the leptokurtic returns and stylized features of
volatility in the Australian stock market.

Abstract:
By applying the least action principle and minimax methods in critical point theory, we prove the existence of periodic solutions for a class of difference systems with p-Laplacian and obtain some existence theorems.

Abstract:
We discuss stochastic functional differential equation under regime switching . We obtain unique global solution of this system without the linear growth condition; furthermore, we prove its asymptotic ultimate boundedness. Using the ergodic property of the Markov chain, we give the sufficient condition of almost surely exponentially stable of this system. 1. Introduction Recently, many papers devoted their attention to the hybrid system, they concerned that how to change if the system undergoes the environmental noise and the regime switching. For the detailed understanding of this subject, [1] is good reference. In this paper we will consider the following stochastic functional equation: The switching between these regimes is governed by a Markovian chain on the state space . is defined by ; . denote the family of continuous functions from to , which is a Banach space with the norm . satisfies local Lipschitz condition as follows. Assumption A. For each integer , there is a positive number such that for all and those with . Throughout this paper, unless otherwise specified, we let be a complete probability space with a filtration satisfying the usual conditions (i.e., it is right continuous and contains all P-null sets). Let ( ), , be the standard Brownian motion defined on this probability space. We also denote by . Let be a right-continuous Markov chain on the probability space taking values in a finite state space with the generator given by where . Here is the transition rate from to and if while We assume that the Markov chain is independent on the Brownian motion ; furthermore, and are independent. In addition, throughout this paper, let denote the family of all positive real-valued functions on which are continuously twice differentiable in and once in . If for the following equation there exists , define an operator from to by where Here we should emphasize that [1, Page 305] the operator (thought as a single notation rather than acting on ) is defined on although is defined on . 2. Global Solution Firstly, in this paper, we are concerned about that the existence of global solution of stochastic functional differential equation (1.1). In order to have a global solution for any given initial data for a stochastic functional equation, it is usually required to satisfy the local Lipschitz condition and the linear growth condition [1, 2]. In addition, as a generation of linear condition, it is also mentioned in [3, 4] with one-sided linear growth condition. The authors improve the results using polynomial growth condition in [5, 6]. After that,