构建江西城乡劳动力市场体系若干问题的研究

劳动就业是我国经济社会发展过程中面临的战略性问题。构建江西城乡劳动力市场体系,必须立足江西实际,加强宏观指导,转变政府职能,注重制度创新,增加市场弹性,培育劳动力市场价格形成机制,健全和完善各项经济社会政策,以促进江西经济社会的和谐发展。     

新乡贤助推乡村振兴战略实现的路径研究

乡村振兴重在“人才振兴”,只有解决好了人的问题,乡村才能真正振兴,因而推动各类人才尤其是吸引新乡贤参与家乡各项事业发展,成为实现乡村振兴战略的客观要求。新乡贤作为实现乡村振兴战略的重要力量,在全面推进乡村振兴过程中对产业振兴、人才振兴、文化振兴、生态振兴和组织振兴等都发挥着积极作用,但在实践中暴露出一些突出问题,诸如对新乡贤认识定位不清晰、缺乏情感沟通、实践渠道受阻以及权益保障不到位等。为此,需要通过多种舆论宣传确保新乡贤“时刻挂念家乡”、加强日常情感联系推动新乡贤“经常回来走走”和注重多措并举鼓励支持新乡贤“共谋家乡发展”等实施路径,来不断提升新乡贤在乡村振兴中的工作实效性。     

适应新形势，实现新要求——合肥工业大学非英语专业博士生英语教改与实践

针对合肥工业大学非英语专业博士生教学时数大幅度减少的现状,提出博士生英语教学应以国际学术交流英语为中心,并展开博士生英语教学改革尝试及实效性研究,以期促进博士生使用英语进行国际学术交流,从而实现其科研创新能力不断提升的教学基本目标。     

电子信息工程专业教学内容与课程体系的改革

为提高我校电子信息工程专业本科教学水平,提出了本专业人才培养方案的原则,并对人才创新能力培养的计划和具体措施进行较为详细的阐述,明确了专业建设内容和目标,促进电子专业持续、快速、协调、健康发展。     

边远农村教育政策落实情况的调查与思考——基于海南省五指山市王村民族教育的田野调查

对海南省黎族边远农村的少数民族教育政策落实进行调查,解读了当地少数民族教育政策存在的现实问题及其原因,并提出了相应的对策以希促进边远农村少数民族教育的改善。     

Adaptive Estimation of the Integral of Squared Regression Derivatives

A problem of estimating the integral of a squared regression function and of its squared derivatives has been addressed in a number of papers. For the case of a heteroscedastic model where smoothness of the underlying regression function, the design density, and the variance of errors are known, the asymptotically sharp minimax lower bound and a sharp estimator were found in Pastuchova & Khasminski (1989). However, there are apparently no results on the either rate optimal or sharp optimal adaptive, or data-driven, estimation when neither the degree of regression function smoothness nor design density, scale function and distribution of errors are known. After a brief review of main developments in non-parametric estimation of non-linear functionals, we suggest a simple adaptive estimator for the integral of a squared regression function and its derivatives and prove that it is sharp-optimal whenever the estimated derivative is sufficiently smooth.     

Robust location estimation with missing data

In a missing data setting, we have a sample in which a vector of explanatory variables ${\bf x}_i$ is observed for every subject i, while scalar responses $y_i$ are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at random (MAR) data, under a semiparametric regression model. Our approach allows the consistent estimation of any weakly continuous functional of the response's distribution. In particular, strongly consistent estimators of any continuous location functional, such as the median, L‐functionals and M‐functionals, are proposed. A robust fit for the regression model combined with the robust properties of the location functional gives rise to a robust recipe for estimating the location parameter. Robustness is quantified through the breakdown point of the proposed procedure. The asymptotic distribution of the location estimators is also derived. The proofs of the theorems are presented in Supplementary Material available online. The Canadian Journal of Statistics 41: 111–132; 2013 © 2012 Statistical Society of Canada     

Predicting infectious disease outbreak risk via migratory waterfowl vectors

The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.     

Functional principal component analysis for the explorative analysis of multisite–multivariate air pollution time series with long gaps

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.