南海沉积物细菌胞外蛋白酶在家族水平上的多样性

海洋产蛋白酶细菌及其分泌的胞外蛋白酶在降解有机氮以推动海洋氮循环进行方面发挥着重要作用,但目前对这二者多样性的认识非常有限。本研究自南海沉积物中筛选得到90株产蛋白酶细菌,并通过N-端氨基酸序列,分析了其胞外蛋白酶在家族水平上的多样性。16S rRNA基因序列分析的结果表明,筛选的产蛋白酶细菌均属于Gammaproteobacteria纲,且大多数属于Alteromonadales目和Vibrionales目的不同属。对其中14株菌株的14个胞外蛋白酶的N-端氨基酸序列分析表明,所有这些蛋白酶属于金属蛋白酶的M4家族或丝氨酸蛋白酶的S8家族。本研究提供了海洋沉积物产蛋白酶细菌在类群及其胞外蛋白酶在类型上的新细节,这将有助于全面了解微生物酶促降解海洋沉积物有机氮的过程和机理。     

Experimental,Numerical and Simplified Theoretical Model Study for Internal Solitary Wave Load on FPSO with Emphasis on Scale Effect

Scale effect of ISWs loads on Floating Production Storage and Offloading(FPSO) is studied in this paper. The application conditions of KdV, eKdV and MCC ISWs theories are used in the numerical method. The depthaveraged velocities induced by ISWs are used for the velocity-inlet boundary. Three scale ratio numerical models λ=1, 20 and 300 were selected, which the scale ratio is the size ratio of numerical models to the experimental model.The comparisons between the numerical and former experimental results are performed to verify the feasibility of numerical method. The comparisons between the numerical and simplified theoretical results are performed to discuss the applicability of the simplified theoretical model summarized from the load experiments. Firstly, the numerical results of λ=1 numerical model showed a good agreement with former experimental and simplified theoretical results. It is feasible to simulate the ISWs loads on FPSO by the numerical method. Secondly, the comparisons between the results of three scale ratio numerical models and experimental results indicated that the scale ratios have more significant influence on the experimental horizontal forces than the vertical forces. The scale effect of horizontal forces mainly results from the different viscosity effects associated with the model’s dimension.Finally, through the comparisons between the numerical and simplified theoretical results for three scale ratio models, the simplified theoretical model of the pressure difference and friction forces exerted by ISWs on FPSO is applied for large-scale or full-scale FPSO.     

基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析

云南沿边地区包括8个地州,共56个县,其中有25个县市与老挝、缅甸和越南直接毗邻,具有重要的地缘位置。本研究利用土地利用数据和夜间灯光数据在实现云南沿边地区GDP空间化的基础上,对GDP的空间分布格局进行深入探讨,这对缩小区域经济差异及促进地区共同发展具有一定的指导意义。采用土地利用数据对国内生产总值（Gross Domestic Product, GDP）数据的第一产业进行空间化拟合,采用DMSP/OLS夜间灯光数据对GDP的第二、三产业进行拟合,将第一产业和第二、三产业空间化拟合的结果相加,实现云南沿边地区1992-2013年的GDP的空间化拟合。在此基础上对云南沿边地区GDP空间分布差异进行分析。结果表明：① 土地利用数据对第一产业建模的效果较好,拟合的多期数据的相对误差均低于1.12%,采用夜间灯光数据,基于“分类回归”方法对第二、三产业拟合相对误差最大仅为6.404%,最终二者之和拟合的GDP拟合精度都较好,相对误差最大仅为4.241%;② 22期GDP数据在空间分布上均呈现正的相关性,且均为显著集聚;③ GDP空间分布局部集聚的高值-高值区域集中在开远、蒙自等县域,低值-低值地区集中在绿春、西蒙等地区;④ 云南沿边地区县域之间的经济差异在1992-1996年逐渐增强,1996年之后,经济差异波动缩小,空间关联效应呈现波动式的增强和减弱;⑤ 云南沿边地区的三维插值结果均呈现出西北至东南一线的“洼地-丘陵-平地-高峰”地势变化格局,沿边地区的东南角地区即红河州的建水、个旧和开远等县市的GDP最高,“丘陵”地势主要集中在腾冲、保山市以及最南部的景洪地区,“洼地-平地”地势主要分布在沿边地区西北角的贡山和福贡等县域、西南角的西蒙和孟连等县及中部区域的绿春和江城县等地区。     

Preparation of pure gum raw materials-low brown algae application

Octenylsuccinate starch ester, also called pure gum, is non-toxic and odourless modified starch which is widely used in many food fields. This study synthesized pure gum in a reaction kettle using the low molecular weight trehalose and octenyl succinic acid. An orthogonal test was carried out to find how the reaction factors affect the synthetization of octenylsuccinic acid polysaccharide ester and to optimize the reaction at single factor level. The optimal products were obtained using 1:2 of octenylsuccinic acid:alginic acid, catalysed by 0.1% p-toluenesulfonic acid catalyst for 1.5 h at 200℃ under vacuum conditions.The gained product contains up to 46% of seaweed gel monoesters. The degree of esterification of the polysaccharide is controlled by the use of the small-molecule trehalose. Compared with the traditional methods, our process can reduce raw material cost and improve emulsification stability of pure gum. These all can significantly improve the market competitiveness of pure gum products.     

长白山景区旅游安全风险动态评价研究

以长白山景区旅游安全为研究对象,以鱼骨图、动态贝叶斯、GIS技术等为基本研究方法,从研究区自然环境、社会环境及责任人为3个方面出发,筛选景区致险因子,构建景区旅游安全风险危险性评价指标体系,利用动态贝叶斯方法综合构建景区旅游安全风险动态评价模型;并以实测数据及景区统计数据为依据,划分景区旅游安全风险评价的4个动态时段,综合实现景区旅游安全风险动态风险评价。研究结果表明：① 中等以上风险区域呈条带状分布;② 高风险区域与主要景点重合;③ 长白山景区安全风险发生高概率的时段发生在第三个时段（12:00~14:00）;较高概率发生分别在第二个时段（10:00~12:00）与第四个时段（14:00~16:00）;中等概率发生较高的时段在第四个时段（14:00~16:00）;较低概率发生在第一个时段（8:00~10:00）。     

城市建成环境对居民体力活动的影响 ——以福州市的经验研究为例

利用福州中心城区的社会调查、POI、道路网、用地性质等数据,在社区尺度探究城市建成环境对居民休闲型中高强度体力活动的影响。研究发现：① 低密度土地利用与体力活动时长呈正向关联,功能单一和邻近主干道则对体力活动起到抑制作用。② 体力活动目的地对不同人群具有差异化影响,体育用地和付费运动场所仅能促进中高阶层和中高收入者的体力活动,而周边公共运动设施的丰富程度更具普适性意义。③ 通过纳入运动偏好和单独分析保障房、单位大院及房改房住户达到规避居住自选择的目的,证明上述建成环境特征对体力活动的影响是独立且稳健的。     

南海海洋地磁固定观测站建设初步研究

为填补我国海洋地磁固定观测的空白,支撑科学研究、防灾减灾、海洋资源开发,维护国家的海洋权益和履行我国在南海海域负有的国际责任,急需在南海建设岛礁和海底地磁固定观测站。通过初步分析国外海洋地磁观测站案例、南海岛礁基础设施现状以及海底观测网计划实施情况,认为南海海洋地磁固定观测站建设的条件已经成熟。文中给出了合理空间布局、分步实施、因地制宜及智能化观测的建议。     

基于无人机多源遥感数据海岛植被叶面积指数协同反演研究

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。     

基于GNSS方位辅助惯性导航系统的水下地形精密测量技术

多波束测深技术是目前水下地形测量的主要技术手段,测量平台的瞬时姿态及方位是影响多波束测深系统最终成果准确度的重要因素。GNSS方位辅助惯性导航系统,作为目前应用较为广泛的方位、姿态、及位置综合测量系统,不仅能够提供高精度位置信息,同时也能提供测量平台的瞬时姿态及方位数据,而且因为具有GNSS方位辅助测量,使得最终方位测量结果比传统方位测量精度大大提高,这对于多波束最终测量成果精度提高具有重要意义。文中从GNSS方位辅助惯性导航系统原理及技术优势出发,结合Trimble RTX后处理技术,从姿态测量、方位测量及辅助高程测量方面分析了在多波束水下地形测量中的应用,并以实际测量成果来展现其在水下地形精密测量技术方面的优势,结果显示,定位精度可以达到优于2 cm级别,方位精度可以优于0.01°(依赖于双GNSS天线之间的基线长度),该技术对水下地形测量准确度提升作用显著。     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.