Contact Binaries: A Study of the Proximity Effects and Gravity Darkening Based on Kopal’s Fourier Method

A new method for the determination of the proximity effects and gravity darkening exponents in contact binaries of W UMa type is presented. The method is based on Kopal’s method of Fourier analysis of the light changes of eclipsing variables in the Frequency Domain. The method was applied to 36 W UMa systems for which geometric and photometric elements have been derived by the most powerful techniques. The derived values are very close to those predicted by the existing theory of radiative transfer or convective equilibrium.     

UV Leo: The Binary with the Two Suns

BV light curves of the eclipsing binary UV Leo obtained at the Kryonerion Astronomical Station of the National Observatory of Athens, Greece, are analyzed. The analysis is based on a Roche configuration with two spots on the secondary surface. The elements of the two components of the system are calculated and the spot characteristics are given.     

On the Period Variation of the W UMa-type Contact Binary V502 Ophiuchi

The variation in the orbital period of the W UMa type contact binary V502 Oph is analyzed. The orbital period exhibits a wavelike variation with a periodicity of 23.0 years and an amplitude of △P = 1.24×10~(-6) days superimposed on secular decrease of dP/dt = 1.68×10-7 day per year. The long-term decrease may be accompanied by the contraction of the secondary at a rate of 83 m per year and a mass transfer rate from the primary to the secondary of 4.28×10~8 M per year. The short-term oscillation may be explained by the presence of a third component. Orbital elements of the third body and its possible mass are presented.     

Deciphering potential groundwater zone in hard rock through the application of GIS

Remote Sensing and Geographic Information System has become one of the leading tools in the field of hydrogeological science, which helps in assessing, monitoring and conserving groundwater resources. It allows manipulation and analysis of individual layer of spatial data. It is used for analysing and modelling the interrelationship between the layers. This paper mainly deals with the integrated approach of Remote Sensing and geographical information system (GIS) to delineate groundwater potential zones in hard rock terrain. The remotely sensed data at the scale of 1:50,000 and topographical information from available maps, have been used for the preparation of ground water prospective map by integrating geology, geomorphology, slope, drainage-density and lineaments map of the study area. Further, the data on yield of aquifer, as observed from existing bore wells in the area, has been used to validate the groundwater potential map. The final result depicts the favourable prospective zones in the study area and can be helpful in better planning and management of groundwater resources especially in hard rock terrains.     

JD直连式深孔绳索取心钻杆的研制与应用

根据深部找矿的需要,立足于我国目前钻探设备现状和材料冶炼及机械加工水平,通过对钻杆选材、结构、制造工艺的创新,研制了一种两端镦粗后整体热处理再车螺纹的新型深孔绳索取心钻杆及配套钻具,经使用,效果达到了设计要求。     

利用UML模型构建Geodatabase的方法与实践

利用UML模型生成Geodatabase数据库便于数据库的更新及移植,同时能很好地反映数据库结构及对象之间的关系。论文分析了利用VISIO建模工具构建Geodatabase的UML模型以及在此基础上利用ArcGIS Desktop相关模块生成Geodatabase数据库的方法,并以上海市绿化林业信息数据库为例,演示了Geodatabase的创建及更新过程。     

螺杆钻定向钻探技术研究与应用

论述了螺杆钻定向钻探配套器具的研究、施工工艺及应用典型实例，提出了今后液项技术的发展方向。     

多媒体创作工具Compel的应用

本文介绍了一种多媒体应用的创作工具。利用这种工具可以编辑和组织文字、图象、图表、声音以及视频等多种媒体元素。并通过媒体链接以及超级链接，完成多媒体应用的制作。同时，也介绍了多媒体应用的播放。     

立方氮化硼的应用与机械加工的技术进步

立方氮化硼内其具有“三高”特性，而成为一种理想的硬磨料得以发展，着重介绍了立方氮化硼磨具和刀具及其推广应用，并阐述其技术的发展对机械加工技术的进步具有重要的意义。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.