Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis,United States

Direct applications of remote sensing thermal infrared (TIR) data in landscape ecological research are rare due to limitations in the sensors, calibration, and difficulty in interpretation. Currently there is a general lack of methodology for examining the relationship between land surface temperatures (LST) derived from TIR data and landscape patterns extracted from optical sensors. A separation of landscapes into values directly related to their scale and signature is a key step. In this study, a Landsat ETM+ image of Indianapolis, Unites States, acquired on June 22, 2000, was spectrally unmixed (using spectral mixture analysis, SMA) into fraction endmembers of green vegetation, soil, high albedo, and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land use and land cover (LULC) classes. Using the fractional images, the landscape composition and pattern were examined. Next, pixel-based LST measurements were correlated with the landscape fractional components to investigate LULC based relationships between LST and impervious surface and green vegetation fractions. An examination of the relationship between the LULC and LST maps with landscape metrics was finally conducted to deepen understanding of their interactions. Results indicate that SMA-derived fraction images were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables. LST was found to be positively correlated with impervious surface fraction but negatively correlated with green vegetation fraction. Each temperature zone was associated with a dominant LULC category. Further research should be directed to the theoretical and applied implications of describing such relationships between LULC patterns and urban thermal conditions.

Two preprocessing algorithms for climate time series

We propose two preprocessing algorithms suitable for climate time series. The first algorithm detects outliers based on an autoregressive cost update mechanism. The second one is based on the wavelet transform, a method from pattern recognition. In order to benchmark the algorithms'' performance we compare them to existing methods based on a synthetic data set. Eventually, for exemplary purposes, the proposed methods are applied to a data set of high-frequent temperature measurements from Novi Sad, Serbia. The results show that both methods together form a powerful tool for signal preprocessing: In case of solitary outliers the autoregressive cost update mechanism prevails, whereas the wavelet-based mechanism is the method of choice in the presence of multiple consecutive outliers.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

Regression from another angle

This paper presents a simply viewed framework that brings together various concepts of regression, prediction, and principal components. Several new concepts related to prediction are introduced, and then the interrelationships of these concepts are established. The generalizations are examined in detail and are illustrated in the context of a well known data set.     

公允价值应用研究

我国在2006年新的企业会计准则体系中全面引入了公允价值,这将对会计信息的生成和呈报产生重要影响。论文阐述了公允价值的内涵,分析了公允价值的特征,从公允价值计量的可操作性、公允价值计量的信息质量的可靠性、我国的宏观经济环境、会计人员素质等方面分析了公允价值在应用中存在的问题,最后提出了在我国推行公允价值的建议。     

Measurement Error Correction by Exploiting Gene–Environment Independence in Family‐Based Case–Control Studies

Abstract. Family‐based case–control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to explore the situation of measurement error affecting the assessment of the environmental exposure. We correct for measurement error through a likelihood‐based method. We exploit a conditional likelihood approach to relate the probability of disease to the genetic and the environmental risk factors. We show that this approach provides less biased and more efficient results than that based on logistic regression. Regression calibration, instead, provides severely biased estimators of the parameters. The comparison of the correction methods is performed through simulation, under common measurement error structures.     

基于鲁棒性的关键链二次资源冲突消除策略

以关键链方法中的二次资源冲突困境为切入点,从鲁棒调度优化角度提出一种解决策略.首先,采用定量化建模对问题进行数学描述和表达,剖析插入输入缓冲引起二次资源冲突的原理,进而采用场景分析法从复杂的冲突表象中分解出四种基本的冲突场景构成要素.其次,基于鲁棒调度优化理论,探究各种冲突子问题的有效对策并归类,据此开发出一种消除二次资源冲突的局部重调度启发式协调策略;根据策略设计基于两次调度进程和两类缓冲动态消耗的鲁棒性指标,采用鲁棒性关键链项目调度问题输出鲁棒性最大的调度方案.再次,设计仿真程序和三个测试指标:项目实际按期完工率、活动开始时间偏差绝对值之和及偏差绝对值的方差;基于ProGen随机地生成测试算例集合进行数值实验.结果发现,以鲁棒性调度方案为依据安排项目的实施过程,三个统计指标值都优于以传统关键链调度方案为依据时相对应的指标值.结论表明:基于鲁棒调度优化的二次资源冲突消除策略及设计的关键链鲁棒性指标在项目实施中具有较好的稳定性效果.     

论新闻计算句子长短的句子标准

新闻语体的句子应该比其他语体的句子短，这是由新闻的易读性决定的。但是目前的一些新闻语言研究中，统计出的句子比其他语体的句子还要长几倍。一些新闻语言研究中同时以单句和复句为单位。单句和复句在语法结构上不是同一层级的语言单位，同时作为计算单位，是违反逻辑的。语法系统中，小句是基本的句子，是传递信息的基本单位。新闻语言研究中也应以小句为计算单位。     

网上银行顾客感知服务质量影响因素研究

通过文献研究和深度访谈，提炼出可能影响网上银行顾客感知服务质量的19个因素，通过探索性因子分析，将这19个因素归纳为经济性、易用性、履行性、安全性和响应性。通过相关分析和多元回归分析发现，有4类因素与网上银行顾客感知服务质量呈显著正相关，依次是经济性、易用性、履行性和安全性。通过对我国主要网上银行服务质量测评发现，当前我国网上银行服务质量不高，且各家网上银行服务质量存在明显差异。