平庄盆地六家立井地区找铀目标层岩石学特征及其找矿意义

六家立井地区位于平庄盆地北缘,白垩系下统阜新组上段是该区砂岩型铀矿床目标层位,具有较好找铀前景。为深入了解该区砂体的岩石学特征,通过野外钻探工作、岩心编录、样品采集及岩矿测试方法,详细研究了目标层碎屑岩类型、碎屑岩成分、结构构造等岩石学方面特征。结果表明目标层碎屑岩成分成熟度和结构成熟度均较低,具有搬运距离短、沉积速度快的特点,其物源较单一,初步判断物源来自盆地东北部和西北部隆起带酸性花岗岩,同时也为铀成矿提供了良好的铀源。     

胜利东二号露天矿6煤层直接液化性能评价

基于褐煤直接液化特性与煤性质的关系,从煤岩组分和煤质基本性质出发,结合前人对与褐煤直接液化相关的煤岩组分和煤质指标研究成果,通过采样测试与收集数据,探讨了胜利煤田东二号露天矿6煤层煤岩组分、灰分、挥发分、H/C原子比对其直接液化的影响,提出了褐煤作为直接液化的指标要求范围。结果表明:该煤层活性组分(腐植组和稳定组)含量较高,挥发分含量大于35%、H/C原子大部分大于0. 70、浮煤灰分大部分小于10%,符合液化指标要求,可作为较理想的直接液化原料煤。     

Binary Star Fractions from the LAMOST DR4

Stellar systems composed of single, double, triple or higher-order systems are rightfully regarded as the fundamental building blocks of the Milky Way. Binary stars play an important role in formation and evolution of the Galaxy. Through comparing the radial velocity variations from multiepoch observations, we analyze the binary fraction of dwarf stars observed with LAMOST. Effects of different model assumptions, such as orbital period distributions on the estimate of binary fractions,are investigated. The results based on log-normal distribution of orbital periods reproduce the previous complete analyses better than the power-law distribution. We find that the binary fraction increases with Teff and decreases with [Fe/H]. We first investigate the relation between α-elements and binary fraction in such a large sample as provided by LAMOST. The old stars with high [α/Fe] dominate with a higher binary fraction than young stars with low [α/Fe]. At the same mass, earlier forming stars possess a higher binary fraction than newly forming ones, which may be related with evolution of the Galaxy.     

艾比湖原核微生物菌群结构演变与环境因子关系分析

用非培养法获得新疆维吾尔自治区艾比湖湖底沉积物原核微生物菌群组成,并与已有盐湖原核微生物菌群数据进行比对,分析湖泊由淡水湖向盐湖演替过程中原核微生物群落结构变化规律。实验获得艾比湖原核微生物16S rRNA基因序列,并从NCBI数据库下载赛里木湖、柴窝堡湖和顿巴斯他乌盐湖3个湖泊的非培养原核微生物16S rRNA基因序列数据。用不同盐湖细菌和古菌16S rRNA序列信息构建系统发育树并与其理化指标进行典型性相关分析。同源比对及聚类结果显示,艾比湖湖底沉积物中细菌包括4个门,拟杆菌门(Bacteroidetes)占克隆文库的64%,变形菌门(Proteobacteria)占9.4%,厚壁菌门(Firmicutes)占3.4%,放线菌门(Actinobacteria)占2.6%,此外含有未分类类群20.6%。古菌含有两个门,广古菌门(Euryarchaeota,98.3%)和盐纳古菌门(Nanohaloarchaeota,1.7%)。不同盐湖系统发育树结果显示,随盐度增加,盐湖细菌从变形菌门向拟杆菌门演替；古菌从奇古菌门和泉古菌门向广古菌门和盐纳古菌门演替。RDA结果显示,Na⁺、Cl-、SO₄2-和矿化度对盐湖原核微生物多样性结构起到决定性的作用,K⁺、Mg²⁺和Ca²⁺对艾比湖菌群结构影响作用最为显著。原核微生物群落会随着湖水盐浓度的增加和盐湖化学成分的不同而发生演替。     

Impacts of SIS and CICE as Sea Ice Components in BCC_CSM on the Simulation of the Arctic Climate

Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0(CICE5.0) was introduced to the Beijing Climate Center Climate System Model(BCC_CSM) as a new alternative to the Sea Ice Simulator(SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation(AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_ CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further.     

塔克拉玛干荒漠-绿洲过渡带尘卷风活动特征——以肖塘为例

利用1992-2011年塔克拉玛干沙漠北缘荒漠-绿洲过渡带肖塘气象站的观测资料，分析了该地区尘卷风的年、月变化规律及其与气象因子的关系。结果表明：（1）1992-2011年尘卷风发生日数总体呈波动递减趋势；尘卷风主要发生在3-9月，占全年总日数的90.9%，其中4-7月占全年总日数的70%左右。（2）尘卷风月发生日数随月平均地表与1.5 m高处温差的增大而线性增加（r=0.875，P<0.01）。（3）尘卷风月发生日数随着月平均风速的增大而幂函数增加（r=0.89，P<0.01）。（4）尘卷风月发生日数随月平均相对湿度的增大而线性减少（r=-0.869，P<0.01）。     

Machine-Learning-Aided Determination of Post-blast Ore Boundary for Controlling Ore Loss and Dilution

Natural Resources Research - In the process of open-pit bench blasting for many mines, rock fragments move in the direction of loose space after fragmentation under explosive energy, leading to ore...     

Projections of changes in marine environment in coastal China seas over the 21st century based on CMIP5 models

Journal of Oceanology and Limnology - The increases of atmospheric carbon dioxide and other greenhouse gases have caused fundamental changes to the physical and biogeochemical properties of the...     

A new prediction model for grain yield in Northeast China based on spring North Atlantic Oscillation and late-winter Bering Sea ice cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security. New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index. The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year. The multivariate linear prediction model of maize shows good predictive ability, with a low normalized root-mean-square error (NRMSE) of 13.9%, and the simulated yield accounts for 81% of the total variance of the observation. To improve the performance of the multivariate linear model, a combined forecasting model of rice is built by considering the weight of the predictors. The NRMSE of the model is 12.9% and the predicted rice yield explains 71% of the total variance. The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models. It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest. The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.