Joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples

In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also obtained.

Pre-Training Physics-Informed Neural Network with Mixed Sampling and Its Application in High-Dimensional Systems

Recently,the physics-informed neural network shows remarkable ability in the context of solving the low-dimensional nonlinear partial differential equations.However,for some cases of high-dimensional systems,such technique may be time-consuming and inaccurate.In this paper,the authors put forward a pre-training physics-informed neural network with mixed sampling(pPINN)to address these issues.Just based on the initial and boundary conditions,the authors design the pre-training stage to filter out the set of the misfitting points,which is regarded as part of the training points in the next stage.The authors further take the parameters of the neural network in Stage 1 as the initialization in Stage 2.The advantage of the proposed approach is that it takes less time to transfer the valuable information from the first stage to the second one to improve the calculation accuracy,especially for the high-dimensional systems.To verify the performance of the pPINN algorithm,the authors first focus on the growing-and-decaying mode of line rogue wave in the Davey-Stewartson I equation.Another case is the accelerated motion of lump in the inhomogeneous Kadomtsev-Petviashvili equation,which admits a more complex evolution than the uniform equation.The exact solution provides a perfect sample for data experiments,and can also be used as a reference frame to identify the performance of the algorithm.The experiments confirm that the pPINN algorithm can improve the prediction accuracy and training efficiency well,and reduce the training time to a large extent for simulating nonlinear waves of high-dimensional equations.

Total pollution effect of urban surface runoff

For pollution research with regard to urban surface runoff, most sampling strategies to date have focused on differences in land usage. With single land-use sampling, total surface runoff pollution effect cannot be evaluated unless every land usage spot is monitored. Through a new sampling strategy known as mixed stormwater sampling for a street community at discharge outlet adjacent to river, this study assessed the total urban surface runoff pollution effect caused by a variety of land uses and the pollutants washed off from the rain pipe system in the Futian River watershed in Shenzhen City of China. The water quality monitoring indices were COD （chemical oxygen demand）, TSS （total suspend solid）, TP （total phosphorus）, TN （total nitrogen） and BOD （biochemical oxygen demand）. The sums of total pollution loads discharged into the river for the four indices of COD, TSS, TN, and TP over all seven rainfall events were very different. The mathematical model for simulating total pollution loads was established from discharge outlet mixed stormwater sampling of total pollution loads on the basis of four parameters： rainfall intensity, total land area, impervious land area, and pervious land area. In order to treat surface runoff pollution, the values of MFF30 （mass first flush ratio） and FF30 （first 30% of runoff volume） can be considered as split-flow control criteria to obtain more effective and economical design of structural BMPs （best management practices） facilities.

The identification of individuals with hatchery and natural origin in a mixed sample of Amur River chum salmon by Otolith microchemistry

We estimated the proportion of hatchery and natural fall spawning chum salmon returning to the Amur River using chemical markers specific to hatchery-origin fry.We used otolith microchemistry technique to identify fish with artificial origin among returning spawners.First,we found that juveniles of artificial origin had higher values of the Sr:Ca molar ratio of the otoliths’edge zone compared with juveniles of natural origin,what can be related to the use of rearing feed produced from raw materials of marine origin rich in strontium.Then we observed that most of the spawners from Anyuisky Hatchery and from the Amur River mouth at the start of the spawning migration has also the higher value of Sr:Ca molar ratio of the juvenile zone of otoliths.Also,adults with higher values of the Sr:Ca molar ratio are characterized by a skewed right in the peak of the age distribution.Both,the age structure and phenological shift in the time of spawning migration of individuals with higher value of the used chemical marker corresponds to results of studies on hatchery-produced chum salmon completed at different parts on Northern Pacific.The results of this study will be used in the management of Amur fall chum salmon fisheries,and also demonstrates the necessity of the development of specific measures for increasing the survival of juvenile anadromous salmonids released at large rivers and exposed to prolonged freshwater migration to the ocean.As a further application of the methodology,we plan to identify the markers specific to each of the hatcheries and main spawning tributaries belonging to Amur River catchments.This will be an important step in the evaluation of the contribution of different stocks in mixed fisheries and also in the estimation of the effect of hatchery releases on naturally spawning stocks of Amur fall chum.Following to,our results may indicate the applicability of this approach for the determination of artificial-origin fish in a mixed sample of the Amur fall chum salmon.

Clumped Isotope Analysis of Calcite and Dolomite Mixtures Using Selective Acid Extraction

Acid extraction methods have been used in the last half century to selectively extract the CO_(2)produced from different carbonate minerals in mixed samples.However,these methods are often time-consuming and labor intensive.Their application to clumped isotope(Δ47)analysis has not been demonstrated.We propose here an acid extraction method with phosphoric acid for bulk stable and clumped isotope analysis that treats mixtures of calcite and dolomite the same regardless of the proportional composition.CO_(2)evolved from calcite is extracted by allowing a reaction with phosphoric acid to proceed for 10 min at 50℃.We then extract CO_(2)evolved from dolomite by rapid ramping the acid temperature from 50 to 90℃and allowing the reaction to complete.The experimental results show that our method yields accurate calcite and dolomiteΔ_(47)values from mixed samples under different proportional compositions.Our method also displays equal or higher accuracy for calciteδ^(13)C and dolomiteδ^(13)C andδ^(18)O values from mixtures when compared to previous studies.Our approach exhibits higher sample throughput than previous methods,is adequate for clumped isotopic analysis and simplifies the reaction progression from over 24 h to less than 2 h,while maintaining relatively high isotopic obtaining accuracy.It yet poorly resolves calciteδ18O values,as found with previous methods.

基于在线大数据的通货膨胀“现时”预测

新冠肺炎疫情冲击导致经济出现结构性变化,对通胀预测提出了新的挑战;而大数据时代的到来,则为提高通胀预测的时效性提供了新的机遇.本文据此围绕基于大数据的通胀“现时”预测(nowcasting)进行探索,提出一个基本的现时预测框架,其核心是引入新的大数据宏观实时变量或大数据预测方法.本文通过引入宏观实时变量--基于互联网在线大数据的居民消费价格指数(internet-based consumer price index,iCPI),包括总类和大类的iCPI日环比指数、周环比指数、旬同比指数和月同比指数,采用LASSO(the least absolute shrinkage and selection operator)降维法和混频数据抽样模型(mixed data sampling,MIDAS),有效地提高了通胀预测的时效性和准确性.研究发现:不同频率的iCPI均有利于提高通胀预测准确性,其表现优于基准模型和大部分的同频传统指标,当其与传统指标相结合时,可进一步降低预测误差,目前尚不能完全舍弃传统变量和方法;在不同频率下(日度除外),iCPI八大类的预测效果优于iCPI总类;不同频率的大数据指标在通胀预测的准确性和时效性上各有优势,这与其背后反映的信息结构有关,其中高频旬同比iCPI表现尤为突出、其能较好地兼顾预测时效性和准确性.本研究为数字经济时代利用大数据提高通胀预测的准确性和时效性、创新宏观经济监测与预测体系提供了有益参考.

Mixing MC events in a reactor neutrino experiment

In reactor neutrino experiments, the analysis of time correlations between different physical events is an important task. Such analysis can help to understand the physical mechanisms of the signal and background events as well as the details of event selection and background estimation. This study investigates a ＂sampling and mixing＂ method used for producing large MC data samples for the Daya Bay reactor neutrino experiment. We designed a simple, generic mixing algorithm and generated large MC data samples for physics analysis from several samples according to their respective event rates. Basic plots based on the mixed data are shown.

On business cycle forecasting

We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature.The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time(widely used in the literature).The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles,a dynamic factor model encompassing many economic and financial variables,and a mixed data sampling regression incorporating common factors with mixed sampling frequencies.The model gene rates significantly more accurate forecasts for U.S.recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those gene rated by existing models.