极地低压对费拉姆海峡大西洋入流水天气尺度变化的影响

The Atlantic inflow in the Fram Strait(78°50′N) has synoptic scale variability based on an array of moorings over the period of 1998–2010. The synoptic scale variability of Atlantic inflow, whose significant cycle is 3–16 d, occurs mainly in winter and spring(from January to April) and is related with polar lows in the Barents Sea. On the synoptic scale, the enhancement(weakening) of Atlantic inflow in the Fram Strait is accompanied by less(more)polar lows in the Barents Sea. Wind stress curl induced by polar lows in the Barents Sea causes Ekman-transport,leads to decrease of sea surface height in the Barents Sea, due to geostrophic adjustment, further induces a cyclonic circulation anomaly around the Barents Sea, and causes the weakening of the Atlantic inflow in the Fram Strait. Our results highlight the importance of polar lows in forcing the Atlantic inflow in the Fram Strait and can help us to further understand the effect of Atlantic warm water on the change of the Arctic Ocean.     

Enhanced a novel β-agarase production in recombinant Escherichia coli BL21(DE3) through induction mode optimization and glycerol feeding strategy

Agarases are hydrolytic enzymes that act on the hydrolysis of agar and have a broad range of applications in food,cosmetics and pharmaceutical industries. In this study, a glycerol feeding strategy based on induction mode optimization for high cell density and β-agarase production was established, which could effectively control acetate yield. First, exponential feeding strategy of glycerol with different overall specific growth rates(μ) was applied in the pre-induction phase. The results showed that the low μ(μ=0.2) was suggested to be the optimal for cell growth and β-agarase production. Second, the effects of induction temperature and the inducer concentration on cell growth and β-agarase production were investigated in the post-induction phase. When induced by isopropyl-β-d-thiogalactoside(IPTG), the strategy of 0.8 mmol/L IPTG induction at 20℃ was found to be optimal for β-agarase production. When cultivation was induced by continuous lactose feeding strategy of 1.0 g/(L·h), the β-agarase activity reached 112.5 U/mL, which represented the highest β-agarase production to date.Furthermore, the β-agarase was capable of degrading G. lemaneiformis powder directly to produce neoagarooligosaccharide, and the hydrolysates were neoagarotetraose(NA4) and neoagarohexaose(NA6). The overall research may be useful for the industrial production and application of β-agarase.     

鄂尔多斯盆地宜川地区二叠纪孢粉化石组合特征

通过对延长油矿延223井晚古生代孢粉组合研究以及古植被的恢复,探讨了该区晚古生代孢粉植物化石组合的地层学和古气候意义。根据孢粉化石主要属种的时代分布及其在各样品中百分含量的规律性变化,自下而上建立了3个孢粉组合:Gulisporites cochlearius-Laevigatosporites minimus组合、Sinulatisporites-Florinites组合和Lueckeisporites permianus-Platysaccus radialis组合。根据对划分孢粉组合的特征分析,通过与邻区及华北地区晚古生代孢粉组合的对比,探讨了孢粉组合的时代意义。通过孢粉组合,推测了相应时期的古植被类型,并与前人在相同地质时期所建立的大植物化石带进行了对比,分析了各地质时期植被的兴起、发展、繁盛以及衰退绝灭的历史过程,研究区二叠纪植物群属于典型的华夏植物群,反映了一种温暖潮湿的热带雨林气候,二叠纪晚期随着松柏类植物所占比例的增加,气候有变干旱的趋势。     

烟台市地质灾害发育现状及影响因素分析

2016年山东省开展各市地质灾害排查工作,烟台市作为地质灾害较发育区,是该次排查重点工作区之一。该文以现状调查为基础,查明了烟台市共发育471处地质灾害隐患点,地质灾害规模以小型为主,主要类型为崩塌、滑坡、泥石流、采空塌陷和岩溶塌陷,其中崩塌隐患点占地质灾害总数的68%,数量最多,其次为泥石流和采空塌陷,各占总数的15%和14%,滑坡数量最少,仅占总数的1%,并介绍了各类地质灾害分布特征。在此基础上,对地质灾害的主要影响因素——地形地貌、岩土体类型、地质构造、降水和人类工程活动等进行了分析。最后提出了在进行工程建设活动的同时重视其引发的地质灾害问题,以减少和预防其带来的次生地质灾害。     

载波/多普勒平滑伪距在Android手机中的定位实现

针对Android手机GNSS伪距定位精度较低的问题，利用手机端观测信息，通过载波相位/多普勒平滑伪距改善手机端伪距观测值的质量，从而达到提高定位精度的效果。首先给出了Android手机GNSS原始观测量的获取方法，然后推导了载波相位平滑伪距和多普勒平滑伪距算法模型，并设计合理有效的试验对算法的精度进行评定。试验结果表明：在手机端静态定位中载波相位和多普勒平滑算法均可提高原始伪距的定位精度，且多普勒平滑算法表现更优；在手机端动态定位中多普勒平滑算法可获得比原始伪距更优的定位精度，但是载波相位平滑算法较原始伪距更差；由于硬件的制约手机端周跳和信号失锁严重，占比超过50%，载波相位在手机端的可用性较低；多普勒平滑算法的最优平滑时间常数小于等于10 s，具有实时动态定位的巨大潜力。     

青海湖流域植被盖度时空变化研究

高寒区植被变化一直是气候和生态学领域关注的热点问题。本研究基于MODIS NDVI数据计算的植被覆盖度数据和高分辨率气象数据,分析了青海湖流域2001-2017年植被覆盖度分布格局及动态变化,探讨了其对气候变化、人类活动和冻土退化的响应。结果表明：① 近十几年青海湖流域植被覆盖度整体表现为增加趋势,不同植被类型增幅存在差异性,草地增幅最大,达到6.1%/10a,其它植被类型增幅在2%~3%/10a之间;② 流域局部地区仍存在植被退化现象,研究期植被退化面积表现为先增加后减小的变化趋势。2006-2011年重度退化区集中在青海湖东岸,2011-2017年重度退化区集中在流域的西北部,这些区域是青海湖流域荒漠分布区,植被覆盖度较低,是今后生态恢复需重点关注的区域;③ 气候变化是流域植被覆盖度变化的主导因素,气候变化对青海湖流域主要植被类型覆盖度变化的贡献率为84.21%,对草原、草甸和灌丛植被覆盖度变化的贡献率分别为81.84%、87.47%和75.96%;④ 人类活动对流域主要植被类型覆盖度变化的贡献率为15.79%,对草原、草甸和灌丛植被覆盖度变化的贡献率分别为18.16%、12.53%和24.04%,环青海湖地区人类活动对植被恢复有促进效应,在青海湖流域北部部分地区人类活动的破坏力度仍大于建设力度;⑤ 冻土退化对青海湖流域草甸和灌丛植被覆盖度变化影响很小,主要影响草原植被覆盖度变化,冻土退化造成草原植被覆盖度增长速率减小了1.2%/10a。     

基于深度域地震成像的中沙海槽盆地东北部结构构造研究

文章基于叠前深度偏移地震成像分析, 结合前人重磁反演等地球物理资料成果, 对中沙海槽盆地东北部结构构造进行探索研究。研究表明: 中沙海槽盆地东北部发育新生代地层, 厚度在1500~2500m之间, 地层层速度为1500~ 3500m·s^-1, 不存在中生界沉积层, 盆地基底为海山或前寒武系基岩及岩浆岩隆起。中沙海槽盆地新生代陆源海相烃源岩丰富, 盆地新生界地层厚度大, 盆地凹陷、断裂发育, 具有一定的油气勘探潜力。     

秦皇岛近岸海域绿藻微观繁殖体的分布以及在绿潮形成中的作用

2015年以来，秦皇岛近岸海域暴发了绿潮，对北戴河旅游区的环境和生态系统造成了严重影响。绿藻微观繁殖体在绿潮的形成过程中起到重要作用，主要包括孢子、配子、幼苗和营养片段。绿藻微观繁殖体作为绿潮的“种源”，其分布规律可以反映绿潮的“藻源”位置。本研究于2016年4-9月和2017年1月对秦皇岛近岸海域绿藻微观繁殖体的调查，探究了其分布规律以及生物量变化。结果显示，绿藻微观繁殖体主要分布在近岸海域，由近岸向远岸海域逐渐降低。绿藻微观繁殖体的数量在7、8月份最高，在冬季最低。受绿潮影响严重的海域微观繁殖体数量高于其它海域。秦皇岛近岸海域的绿藻微观繁殖体为该海域绿潮的种源，其分布规律表明秦皇岛近岸海域绿潮起源于本地。     

新疆乡村聚落特征及其区域融合发展探析

把握兵地聚落差异，以期为新疆兵地融合发展提供理论依据。基于土地利用数据，运用探 索式空间统计模型定量分析天山北坡兵地乡村聚落空间格局与经济发展差异，并基于其差异对其 融合发展提出相应对策。结果表明：（1）天山北坡兵团与地方乡村聚落在空间、规模、区位以及形 态分布特征上均呈现出明显的空间差异，乡村聚落空间整体上呈集聚分布模式，地方聚落集聚态 势较明显；聚落规模呈低值集聚的分布特征，兵团聚落大多为大规模聚落斑块，地方聚落则相反； 乡村聚落的分布具有明显的高程、坡度、道路、河流、城镇指向性，而兵团聚落分布于区位条件较差 地区；在乡村聚落的外部形态复杂度上，地方聚落大于兵团聚落；（2）经济发展方面，天山北坡整体 经济发展较为良好，兵团人均地区生产总值较高，以农业作为主要产业，地方经济结构较为多样， 除农业外，牧业也是其主要产业；（3）基于兵地聚落间差异，提出相应的融合对策，在空间格局上， 根据实际情况适当进行兵地聚落的迁移合并；在经济发展方面，有效利用优势互补原则，推进兵地 间产业融和。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.