Cosmological model with a conformally coupled scalar field

Modern concepts of the universe support the assumed existence of a nongravitational source, known as dark energy, for which ε + 3 P < 0 (where ε is the energy density and P is the pressure). This ensures accelerated expansion of the universe. This paper examines a tensor-scalar variant of the theory of gravitation with a conformally coupled scalar field. Various cosmological models are examined and the possible evolutionary development of the universe with accelerated expansion is discussed. Translated from Astrofizika, Vol. 51, No. 4, pp. 653–661 (November 2008).     

天地图·河南影像数据关键技术研究

天地图·河南是"天地图"的有机组成部分保质保量实现天地图.河南影像数据一年更新两次目标,需要研究如何快速处理影像数据,提高工作效率,有必要从软件与硬件应用两个方面探讨影像数据处理关键技术。     

基于SWH模型的青藏高原高寒草甸蒸散发及其组分变化分析

基于Shuttleworth-Wallace Hu（SWH）双源蒸散模型对青藏高原那曲、纳木错、藏东南站蒸散发进行估算,在结果验证良好基础上,对青藏高原蒸散发变化特征及各站主要影响因素进行了分析。结果表明：SWH模型在青藏高原3个草甸站适用性良好;年蒸散发介于388~732 mm之间,年内分布呈先增大后减小特征;3站蒸散发组分差异较大,那曲站和纳木错站土壤蒸发对蒸散总量的贡献分别为53%和56%,藏东南站蒸散发则几乎全部由植被蒸腾贡献,占比高达95%;植被叶面积指数为3站蒸散发最主要的影响因素,饱和水汽压差对藏东南站蒸散发影响也较大。研究结果可对青藏高原蒸散发及其组分时空格局与水循环过程研究提供科学依据。     

松辽盆地尚家—太平川地区石油地质条件及勘探方向分析

通过对尚家—太平川地区构造演化、烃源岩特征、沉积特征、储盖组合、油水关系及油藏类型和油气成藏特征的分析,研究了本区的石油地质条件,认为烃源岩具有较好的生、排烃能力,分流河道砂体为油气聚集提供较好的沉积环境,良好的储盖组合有利于油气的聚集与保存。研究区没有统一油水界面,尚家地区以岩性—构造油藏为主,太平川地区以构造—岩性油藏为主。区内扶余—杨大城子油层油气成藏受有效性圈闭、优势运移通道、过剩压力、储集条件和构造诱导作用控制。据上述认识确定了研究区下一步的勘探方向,并已在指导勘探实践中取得了较好的效果。     

长江上游(宜宾—宜昌)干流两岸山地城镇滑坡灾害及防治

本文概要介绍长江上游宜宾—宜昌干流两岸山地城镇座落的自然环境,重点论述山地城镇滑坡灾害,最后提出了区域防灾战略的新途迳。     

南极格罗夫山陨石岩石学特征Ⅰ:非平衡L3型普通球粒陨石

我国第16次南极科学考察队在格罗夫山区域的蓝冰表面回收到28块陨石,其中有6块属于非平衡型(即3型)普通球粒陨石。它们保存了原始的岩石矿物学特征,包括非常清晰的球粒结构、基质未重结晶和不透明、橄榄石和低钙辉石颗粒具有明显的成分环带和极不均一的化学组成等。Fe-Ni合金含量、含Fe总量和橄榄石成分综合分析表明这6块陨石全部属于低铁群(L)。因为非平衡普通球粒陨石橄榄石成分变化的PMD(Fa)值与岩石类型亚型有良好的相关性,因此,根据橄榄石Fa的PMD标准这6块陨石被划分为如下亚型:GRV 99019为3.6型,GRV99020、GRV 99021和GRV 99026为3.5型,GRV 99001为3.4型,GRV 99022为3.0—3.4型。     

User Contribution Patterns and Completeness Evaluation of Mapillary,a Crowdsourced Street Level Photo Service

Mapillary is a Web 2.0 application which allows users to contribute crowdsourced street level photographs from all over the world. In the first part of the analysis this article reviews Mapillary data growth for continents and countries as well as the contribution behavior of individual mappers, such as the number of days of active mapping. In the second part of the analysis the study assesses Mapillary data completeness relative to a reference road network dataset at the country level. In addition, a more detailed completeness analysis is conducted for selected urban and rural areas in the US and part of northern Europe for which the completeness of Mapillary data will also be compared with that of Google Street View. Results show that Street View provides generally a better coverage on almost all road categories with some exceptions for pedestrian and cycle paths in selected cities. However, Mapillary data can be conveniently collected from any mobile device that is equipped with a photo camera. This gives Mapillary the potential to reach better coverage along off‐road segments than Google Street View.